From 7b264856da96c9eedc90de877f7553c8590b2ed3 Mon Sep 17 00:00:00 2001 From: jack Date: Wed, 28 Jan 2026 18:20:30 +0800 Subject: [PATCH] ++ --- alpha_template.py | 2 +- expression_validator.py | 8 +- generated_alpha/2026/01/26/1.txt | 147 +++ ...pseek-ai_DeepSeek-V3.1-Terminus_170924.txt | 129 +++ ...wen_Qwen3-VL-235B-A22B-Instruct_171034.txt | 109 ++ generated_alpha/2026/01/27/1.txt | 306 +++++ ...pseek-ai_DeepSeek-V3.1-Terminus_103743.txt | 97 ++ ...pseek-ai_DeepSeek-V3.1-Terminus_110404.txt | 167 +++ ...Pro_moonshotai_Kimi-K2-Thinking_104536.txt | 99 ++ ...Pro_moonshotai_Kimi-K2-Thinking_111154.txt | 95 ++ ...wen_Qwen3-VL-235B-A22B-Instruct_103926.txt | 99 ++ ...wen_Qwen3-VL-235B-A22B-Instruct_110727.txt | 133 +++ generated_alpha/2026/01/28/1.txt | 384 +++++++ ...pseek-ai_DeepSeek-V3.1-Terminus_171838.txt | 117 ++ ...pseek-ai_DeepSeek-V3.1-Terminus_174630.txt | 99 ++ ...Pro_moonshotai_Kimi-K2-Thinking_172450.txt | 99 ++ ...Pro_moonshotai_Kimi-K2-Thinking_175352.txt | 99 ++ ...wen_Qwen3-VL-235B-A22B-Instruct_171924.txt | 61 + ...wen_Qwen3-VL-235B-A22B-Instruct_174755.txt | 49 + main.py | 4 +- .../01/26/manual_prompt_20260126170731.txt | 438 ++++++++ .../01/27/manual_prompt_20260127103636.txt | 734 ++++++++++++ .../01/27/manual_prompt_20260127110240.txt | 817 ++++++++++++++ .../01/28/manual_prompt_20260128171705.txt | 814 ++++++++++++++ .../01/28/manual_prompt_20260128174445.txt | 817 ++++++++++++++ planning_post_alpha.txt | 1001 +++++++++-------- prepare_prompt/alpha_prompt.txt | 12 +- prepare_prompt/keys_text.txt | 5 +- template_output.txt | 995 ++++++++-------- 手动创建新金融逻辑1.txt | 19 +- 30 files changed, 6938 insertions(+), 1017 deletions(-) create mode 100644 generated_alpha/2026/01/26/1.txt create mode 100644 generated_alpha/2026/01/26/Pro_deepseek-ai_DeepSeek-V3.1-Terminus_170924.txt create mode 100644 generated_alpha/2026/01/26/Qwen_Qwen3-VL-235B-A22B-Instruct_171034.txt create mode 100644 generated_alpha/2026/01/27/1.txt create mode 100644 generated_alpha/2026/01/27/Pro_deepseek-ai_DeepSeek-V3.1-Terminus_103743.txt create mode 100644 generated_alpha/2026/01/27/Pro_deepseek-ai_DeepSeek-V3.1-Terminus_110404.txt create mode 100644 generated_alpha/2026/01/27/Pro_moonshotai_Kimi-K2-Thinking_104536.txt create mode 100644 generated_alpha/2026/01/27/Pro_moonshotai_Kimi-K2-Thinking_111154.txt create mode 100644 generated_alpha/2026/01/27/Qwen_Qwen3-VL-235B-A22B-Instruct_103926.txt create mode 100644 generated_alpha/2026/01/27/Qwen_Qwen3-VL-235B-A22B-Instruct_110727.txt create mode 100644 generated_alpha/2026/01/28/1.txt create mode 100644 generated_alpha/2026/01/28/Pro_deepseek-ai_DeepSeek-V3.1-Terminus_171838.txt create mode 100644 generated_alpha/2026/01/28/Pro_deepseek-ai_DeepSeek-V3.1-Terminus_174630.txt create mode 100644 generated_alpha/2026/01/28/Pro_moonshotai_Kimi-K2-Thinking_172450.txt create mode 100644 generated_alpha/2026/01/28/Pro_moonshotai_Kimi-K2-Thinking_175352.txt create mode 100644 generated_alpha/2026/01/28/Qwen_Qwen3-VL-235B-A22B-Instruct_171924.txt create mode 100644 generated_alpha/2026/01/28/Qwen_Qwen3-VL-235B-A22B-Instruct_174755.txt create mode 100644 manual_prompt/2026/01/26/manual_prompt_20260126170731.txt create mode 100644 manual_prompt/2026/01/27/manual_prompt_20260127103636.txt create mode 100644 manual_prompt/2026/01/27/manual_prompt_20260127110240.txt create mode 100644 manual_prompt/2026/01/28/manual_prompt_20260128171705.txt create mode 100644 manual_prompt/2026/01/28/manual_prompt_20260128174445.txt diff --git a/alpha_template.py b/alpha_template.py index b2d46fe..a97a3f9 100644 --- a/alpha_template.py +++ b/alpha_template.py @@ -14,7 +14,7 @@ PREPARE_PROMPT = os.path.join(str(PROJECT_PATH), 'prepare_prompt') SQLITE_PATH = os.path.join(str(PREPARE_PROMPT), 'data_sets.db') # Alpha模板 - 支持 语法 -ALPHA_TEMPLATE = "ts_covariance(, , )" +ALPHA_TEMPLATE = "ts_mean(, )" # 窗口列表 WINDOW_LIST = [5, 20, 60, 250] diff --git a/expression_validator.py b/expression_validator.py index 05db5f4..48365bb 100644 --- a/expression_validator.py +++ b/expression_validator.py @@ -11,6 +11,7 @@ import re import sys import json import os +import random from typing import List, Dict, Any, Optional, Tuple # 尝试导入PLY库，如果不存在则提供安装提示 @@ -934,16 +935,19 @@ def main(): for error in result["errors"]: print(f" - {error}") + output_path = '' + # 保存有效表达式到 .手动处理每天alpha.txt if valid_expressions: valid_expressions = list(set(valid_expressions)) + valid_expressions = random.sample(valid_expressions, len(valid_expressions)) + valid_expressions = sorted(valid_expressions, key=lambda x: random.random()) output_path = os.path.join(base_dir, "planning_post_alpha.txt") try: with open(output_path, 'w', encoding='utf-8') as f: # 每个表达式一行 for expr in valid_expressions: f.write(expr + "\n") - print(f"有效表达式已保存到: {output_path}") # 如果需要，也可以保留源文件名信息 # source_info_path = os.path.join(base_dir, "手动处理每天alpha_source_info.txt") @@ -995,6 +999,8 @@ def main(): print(f"有效表达式: {len(valid_expressions)}") print(f"无效表达式: {len(invalid_expressions)}") + if output_path: + print(f"有效表达式已保存到: {output_path}") if __name__ == "__main__": main() diff --git a/generated_alpha/2026/01/26/1.txt b/generated_alpha/2026/01/26/1.txt new file mode 100644 index 0000000..1efd84e --- /dev/null +++ b/generated_alpha/2026/01/26/1.txt @@ -0,0 +1,147 @@ +inverse(customer_revenue_share_percent) +subtract(fnd72_pit_or_bs_a_bs_acct_payable, accounts_receivable_gross) +divide(quarterly_gross_margin_percent, customer_revenue_share_percent) +multiply(inverse(customer_revenue_share_percent), quarterly_gross_margin_percent) +add(fnd72_pit_or_bs_a_bs_acct_payable, quarterly_gross_margin_percent) +sign(subtract(fnd72_pit_or_bs_a_bs_acct_payable, accounts_receivable_gross)) +abs(divide(accounts_receivable_gross, fnd72_pit_or_bs_a_bs_acct_payable)) +power(inverse(customer_revenue_share_percent), 2) +sqrt(quarterly_gross_margin_percent) +reverse(customer_revenue_share_percent) +max(fnd72_pit_or_bs_a_bs_acct_payable, accounts_receivable_gross) +min(quarterly_gross_margin_percent, quarterly_operating_margin_percent) +divide(sales_revenue_total, accounts_receivable_gross) +multiply(fnd72_pit_or_bs_a_bs_acct_payable, inverse(accounts_receivable_gross)) +subtract(quarterly_gross_margin_percent, quarterly_net_profit_margin_percent) +add(inverse(customer_revenue_share_percent), divide(fnd72_pit_or_bs_a_bs_acct_payable, sales_revenue_total)) +log(quarterly_gross_margin_percent) +signed_power(subtract(fnd72_pit_or_bs_a_bs_acct_payable, accounts_receivable_gross), 3) +divide(quarterly_operating_margin_percent, customer_revenue_share_percent) +multiply(quarterly_gross_margin_percent, inverse(accounts_receivable_turnover_quarterly)) +abs(subtract(fnd72_pit_or_bs_a_bs_acct_payable, accounts_receivable_total_5)) +power(quarterly_gross_margin_percent, 0.5) +inverse(divide(accounts_receivable_gross, sales_revenue_total)) +add(quarterly_gross_margin_percent, inverse(customer_revenue_share_percent)) +reverse(subtract(accounts_receivable_gross, fnd72_pit_or_bs_a_bs_acct_payable)) +max(inverse(customer_revenue_share_percent), quarterly_gross_margin_percent) +min(divide(fnd72_pit_or_bs_a_bs_acct_payable, sales_revenue_total), inverse(accounts_receivable_gross)) +divide(fnd72_pit_or_bs_a_bs_acct_payable, customer_revenue_share_percent) +multiply(subtract(fnd72_pit_or_bs_a_bs_acct_payable, accounts_receivable_gross), quarterly_gross_margin_percent) +add(quarterly_operating_margin_percent, divide(fnd72_pit_or_bs_a_bs_acct_payable, accounts_receivable_gross)) +sign(divide(fnd72_pit_or_bs_a_bs_acct_payable, accounts_receivable_gross)) +abs(power(customer_revenue_share_percent, 2)) +inverse(quarterly_receivables_turnover) +subtract(inverse(customer_revenue_share_percent), divide(accounts_receivable_gross, sales_revenue_total)) +multiply(quarterly_net_profit_margin_percent, inverse(customer_revenue_share_percent)) +divide(subtract(fnd72_pit_or_bs_a_bs_acct_payable, accounts_receivable_gross), quarterly_gross_margin_percent) +add(quarterly_net_profit_margin_percent, divide(fnd72_pit_or_bs_a_bs_acct_payable, sales_revenue_total)) +log(inverse(customer_revenue_share_percent)) +signed_power(quarterly_gross_margin_percent, 2) +sqrt(inverse(customer_revenue_share_percent)) +reverse(divide(accounts_receivable_gross, fnd72_pit_or_bs_a_bs_acct_payable)) +max(quarterly_gross_margin_percent, divide(fnd72_pit_or_bs_a_bs_acct_payable, sales_revenue_total)) +min(inverse(customer_revenue_share_percent), quarterly_operating_margin_percent) +multiply(divide(fnd72_pit_or_bs_a_bs_acct_payable, sales_revenue_total), quarterly_gross_margin_percent) +subtract(quarterly_operating_margin_percent, divide(accounts_receivable_gross, sales_revenue_total)) +add(inverse(quarterly_receivables_turnover), inverse(customer_revenue_share_percent)) +abs(multiply(customer_revenue_share_percent, divide(accounts_receivable_gross, fnd72_pit_or_bs_a_bs_acct_payable))) +power(divide(fnd72_pit_or_bs_a_bs_acct_payable, accounts_receivable_gross), 2) +divide(inverse(customer_revenue_share_percent), quarterly_net_profit_margin_percent) +multiply(reverse(customer_revenue_share_percent), quarterly_gross_margin_percent) +multiply(kth_element(quarterly_gross_margin_percent 250 1) kth_element(quarterly_gross_margin_percent_2 250 1)) + +divide(kth_element(accounts_receivable_total_5 250 1) kth_element(fnd72_pit_or_is_q_sales_rev_turn 250 1)) + +subtract(kth_element(accounts_payable_trade_creditors 250 1) kth_element(accounts_receivable_total_5 250 1)) + +rank(kth_element(customer_revenue_share_percent 250 1)) + +multiply(rank(kth_element(accounts_payable_trade_creditors 250 1)) rank(kth_element(fnd72_pit_or_is_q_sales_rev_turn 250 1))) + +subtract(kth_element(accounts_payable_trade_creditors 250 1) ts_delay(kth_element(accounts_payable_trade_creditors 250 1) 90)) + +divide(kth_element(accounts_receivable_total_5 250 1) kth_element(accounts_payable_trade_creditors 250 1)) + +ts_sum(kth_element(quarterly_gross_margin_percent 250 1) 250) + +rank(subtract(kth_element(accounts_payable_trade_creditors 250 1) kth_element(accounts_receivable_gross 250 1))) + +multiply(kth_element(quarterly_cost_of_goods_sold 250 1) kth_element(quarterly_sales_rev_turn 250 1)) + +divide(ts_sum(kth_element(accounts_receivable_total_5 250 1) 90) ts_sum(kth_element(fnd72_pit_or_is_q_sales_rev_turn 250 1) 90)) + +subtract(ts_delay(kth_element(accounts_payable_trade_creditors 250 1) 30) kth_element(accounts_payable_trade_creditors 250 1)) + +rank(kth_element(quarterly_gross_margin_percent 250 1)) + +ts_delta(kth_element(quarterly_cost_of_goods_sold 250 1) 90) + +multiply(ts_sum(kth_element(quarterly_gross_margin_percent 250 1) 250) ts_sum(kth_element(quarterly_gross_margin_percent_2 250 1) 250)) + +divide(kth_element(accounts_receivable_gross 250 1) kth_element(accounts_payable_trade_creditors 250 1)) + +subtract(kth_element(fnd72_pit_or_is_q_sales_rev_turn 250 1) kth_element(quarterly_cost_of_goods_sold 250 1)) + +rank(divide(kth_element(accounts_receivable_total_5 250 1) kth_element(fnd72_pit_or_is_q_sales_rev_turn 250 1))) + +ts_sum(kth_element(quarterly_cost_of_goods_sold 250 1) 250) + +multiply(rank(kth_element(accounts_receivable_total_5 250 1)) rank(kth_element(customer_revenue_share_percent 250 1))) + +divide(ts_delay(kth_element(fnd72_pit_or_is_q_sales_rev_turn 250 1) 90) kth_element(fnd72_pit_or_is_q_sales_rev_turn 250 1)) + +subtract(kth_element(accounts_payable_trade_creditors 250 1) kth_element(accounts_receivable_gross_2 250 1)) + +rank(multiply(kth_element(quarterly_gross_margin_percent 250 1) kth_element(quarterly_gross_margin_percent_2 250 1))) + +ts_delta(kth_element(fnd72_pit_or_is_q_sales_rev_turn 250 1) 90) + +multiply(kth_element(quarterly_net_profit_margin_percent 250 1) kth_element(quarterly_gross_margin_percent 250 1)) + +divide(kth_element(accounts_payable_trade_creditors 250 1) ts_sum(kth_element(quarterly_cost_of_goods_sold 250 1) 90)) + +subtract(ts_delay(kth_element(accounts_receivable_total_5 250 1) 30) kth_element(accounts_receivable_total_5 250 1)) + +rank(subtract(kth_element(fnd72_pit_or_is_q_sales_rev_turn 250 1) kth_element(quarterly_cost_of_goods_sold 250 1))) + +ts_sum(kth_element(fnd72_pit_or_is_q_sales_rev_turn 250 1) 250) + +multiply(kth_element(quarterly_operating_margin_percent 250 1) kth_element(quarterly_pretax_margin_percent 250 1)) + +divide(kth_element(accounts_receivable_total_5 250 1) ts_sum(kth_element(fnd72_pit_or_is_q_sales_rev_turn 250 1) 90)) + +subtract(kth_element(accounts_payable_trade_creditors 250 1) ts_delay(kth_element(accounts_receivable_total_5 250 1) 90)) + +rank(divide(kth_element(accounts_payable_trade_creditors 250 1) kth_element(quarterly_cost_of_goods_sold 250 1))) + +ts_delta(kth_element(quarterly_gross_margin_percent 250 1) 90) + +multiply(rank(kth_element(accounts_payable_trade_creditors 250 1)) rank(subtract(kth_element(fnd72_pit_or_is_q_sales_rev_turn 250 1) kth_element(quarterly_cost_of_goods_sold 250 1)))) + +divide(ts_delay(kth_element(accounts_payable_trade_creditors 250 1) 30) kth_element(accounts_payable_trade_creditors 250 1)) + +subtract(kth_element(accounts_receivable_gross 250 1) kth_element(accounts_payable_trade_creditors 250 1)) + +rank(ts_sum(kth_element(quarterly_gross_margin_percent 250 1) 250)) + +ts_sum(kth_element(quarterly_net_profit_margin_percent 250 1) 250) + +multiply(kth_element(quarterly_pretax_margin_percent 250 1) kth_element(quarterly_net_profit_margin_percent 250 1)) + +divide(kth_element(accounts_payable_trade_creditors 250 1) kth_element(quarterly_cost_of_goods_sold_2 250 1)) + +subtract(ts_delay(kth_element(accounts_receivable_gross 250 1) 30) kth_element(accounts_receivable_gross 250 1)) + +rank(multiply(kth_element(quarterly_operating_margin_percent 250 1) kth_element(quarterly_pretax_margin_percent 250 1))) + +ts_delta(kth_element(quarterly_operating_margin_percent 250 1) 90) + +multiply(rank(kth_element(accounts_receivable_total_5 250 1)) rank(divide(kth_element(accounts_payable_trade_creditors 250 1) kth_element(quarterly_cost_of_goods_sold 250 1)))) + +divide(kth_element(accounts_receivable_gross_2 250 1) kth_element(fnd72_pit_or_is_q_sales_rev_turn 250 1)) + +subtract(kth_element(fnd72_pit_or_is_q_sales_rev_turn 250 1) ts_delay(kth_element(quarterly_cost_of_goods_sold 250 1) 90)) + +rank(ts_delta(kth_element(quarterly_gross_margin_percent 250 1) 90)) + +ts_sum(kth_element(quarterly_pretax_margin_percent 250 1) 250) \ No newline at end of file diff --git a/generated_alpha/2026/01/26/Pro_deepseek-ai_DeepSeek-V3.1-Terminus_170924.txt b/generated_alpha/2026/01/26/Pro_deepseek-ai_DeepSeek-V3.1-Terminus_170924.txt new file mode 100644 index 0000000..31e0ecc --- /dev/null +++ b/generated_alpha/2026/01/26/Pro_deepseek-ai_DeepSeek-V3.1-Terminus_170924.txt @@ -0,0 +1,129 @@ +group_neutralize(subtract(divide(ts_backfill(fnd72_pit_or_bs_q_bs_acct_payable,lookback=252),ts_backfill(quarterly_cost_of_goods_sold,lookback=252)),divide(ts_backfill(accounts_receivable_gross,lookback=252),ts_backfill(sales_revenue_total,lookback=252))),"industry") + +group_neutralize(ts_backfill(customer_revenue_share_percent,lookback=252),"industry") + +group_neutralize(divide(ts_backfill(fnd72_pit_or_bs_q_bs_acct_payable,lookback=252),ts_backfill(sales_revenue_total,lookback=252)),"industry") + +group_neutralize(divide(ts_backfill(accounts_receivable_gross,lookback=252),ts_backfill(sales_revenue_total,lookback=252)),"industry") + +group_neutralize(ts_std_dev(ts_backfill(quarterly_gross_margin_percent,lookback=252),252),"industry") + +group_neutralize(ts_std_dev(ts_backfill(quarterly_cost_of_goods_sold,lookback=252),252),"industry") + +ts_rank(group_neutralize(subtract(divide(ts_backfill(fnd72_pit_or_bs_q_bs_acct_payable,lookback=252),ts_backfill(quarterly_cost_of_goods_sold,lookback=252)),divide(ts_backfill(accounts_receivable_gross,lookback=252),ts_backfill(sales_revenue_total,lookback=252))),"industry"),252) + +rank(group_neutralize(subtract(divide(ts_backfill(fnd72_pit_or_bs_q_bs_acct_payable,lookback=252),ts_backfill(quarterly_cost_of_goods_sold,lookback=252)),divide(ts_backfill(accounts_receivable_gross,lookback=252),ts_backfill(sales_revenue_total,lookback=252))),"industry")) + +signed_power(group_neutralize(subtract(divide(ts_backfill(fnd72_pit_or_bs_q_bs_acct_payable,lookback=252),ts_backfill(quarterly_cost_of_goods_sold,lookback=252)),divide(ts_backfill(accounts_receivable_gross,lookback=252),ts_backfill(sales_revenue_total,lookback=252))),"industry"),0.5) + +group_neutralize(subtract(divide(ts_backfill(accounts_payable_trade_creditors,lookback=252),ts_backfill(quarterly_cost_of_goods_sold,lookback=252)),divide(ts_backfill(accounts_receivable_gross,lookback=252),ts_backfill(fnd72_pit_or_is_q_sales_rev_turn,lookback=252))),"industry") + +group_neutralize(divide(ts_backfill(accounts_payable_trade_creditors,lookback=252),ts_backfill(sales_revenue_total,lookback=252)),"industry") + +group_neutralize(ts_backfill(quarterly_net_profit_margin_percent,lookback=252),"industry") + +ts_std_dev(ts_backfill(quarterly_net_profit_margin_percent,lookback=252),252) + +group_neutralize(ts_delta(ts_backfill(quarterly_cost_of_goods_sold,lookback=252),252),"industry") + +group_rank(group_neutralize(subtract(divide(ts_backfill(fnd72_pit_or_bs_q_bs_acct_payable,lookback=252),ts_backfill(quarterly_cost_of_goods_sold,lookback=252)),divide(ts_backfill(accounts_receivable_gross,lookback=252),ts_backfill(sales_revenue_total,lookback=252))),"industry"),"industry") + +group_neutralize(ts_mean(ts_backfill(quarterly_gross_margin_percent,lookback=252),252),"industry") + +group_neutralize(ts_delta(ts_backfill(quarterly_gross_margin_percent,lookback=252),252),"industry") + +rank(ts_backfill(customer_revenue_share_percent,lookback=252)) + +ts_rank(ts_backfill(customer_revenue_share_percent,lookback=252),252) + +group_neutralize(log(divide(ts_backfill(fnd72_pit_or_bs_q_bs_acct_payable,lookback=252),ts_backfill(sales_revenue_total,lookback=252))),"industry") + +group_neutralize(abs(subtract(divide(ts_backfill(fnd72_pit_or_bs_q_bs_acct_payable,lookback=252),ts_backfill(quarterly_cost_of_goods_sold,lookback=252)),divide(ts_backfill(accounts_receivable_gross,lookback=252),ts_backfill(sales_revenue_total,lookback=252)))),"industry") + +group_neutralize(divide(ts_backfill(accounts_receivable_gross,lookback=252),ts_backfill(quarterly_cost_of_goods_sold,lookback=252)),"industry") + +group_neutralize(ts_backfill(quarterly_operating_margin_percent,lookback=252),"industry") + +ts_std_dev(ts_backfill(quarterly_operating_margin_percent,lookback=252),252) + +group_neutralize(ts_sum(ts_backfill(quarterly_cost_of_goods_sold,lookback=252),252),"industry") + +group_neutralize(ts_zscore(ts_backfill(quarterly_gross_margin_percent,lookback=252),252),"industry") + +rank(group_neutralize(ts_backfill(quarterly_net_profit_margin_percent,lookback=252),"industry")) + +ts_rank(group_neutralize(ts_backfill(quarterly_net_profit_margin_percent,lookback=252),"industry"),252) + +group_neutralize(divide(ts_backfill(fnd72_pit_or_bs_q_bs_acct_payable,lookback=252),ts_backfill(accounts_receivable_gross,lookback=252)),"industry") + +group_neutralize(signed_power(divide(ts_backfill(fnd72_pit_or_bs_q_bs_acct_payable,lookback=252),ts_backfill(sales_revenue_total,lookback=252)),2),"industry") + +group_neutralize(ts_backfill(quarterly_pretax_margin_percent,lookback=252),"industry") + +ts_delta(ts_backfill(quarterly_pretax_margin_percent,lookback=252),252) + +group_neutralize(ts_corr(ts_backfill(quarterly_gross_margin_percent,lookback=252),ts_backfill(quarterly_net_profit_margin_percent,lookback=252),252),"industry") + +group_neutralize(ts_backfill(quarterly_receivables_turnover,lookback=252),"industry") + +group_neutralize(ts_backfill(quarterly_inventory_turnover,lookback=252),"industry") + +ts_std_dev(ts_backfill(quarterly_receivables_turnover,lookback=252),252) + +group_neutralize(divide(ts_backfill(accounts_receivable_gross,lookback=252),ts_backfill(quarterly_receivables,lookback=252)),"industry") + +group_neutralize(ts_decay_linear(ts_backfill(quarterly_gross_margin_percent,lookback=252),252),"industry") + +rank(group_neutralize(divide(ts_backfill(fnd72_pit_or_bs_q_bs_acct_payable,lookback=252),ts_backfill(quarterly_cost_of_goods_sold,lookback=252)),"industry")) + +ts_rank(group_neutralize(divide(ts_backfill(accounts_receivable_gross,lookback=252),ts_backfill(sales_revenue_total,lookback=252)),"industry"),252) + +group_neutralize(subtract(ts_backfill(quarterly_gross_margin_percent,lookback=252),ts_backfill(quarterly_net_profit_margin_percent,lookback=252)),"industry") + +group_neutralize(ts_backfill(anl69_best_ebit_to_sales,lookback=252),"industry") + +ts_delta(ts_backfill(anl69_best_ebit_to_sales,lookback=252),252) + +group_neutralize(ts_backfill(fnd31_irttmsalesev,lookback=252),"industry") + +group_neutralize(ts_mean(ts_backfill(quarterly_cost_of_goods_sold,lookback=252),252),"industry") + +group_neutralize(ts_product(ts_backfill(quarterly_gross_margin_percent,lookback=252),252),"industry") + +rank(ts_backfill(quarterly_operating_margin_percent,lookback=252)) + +ts_rank(ts_backfill(quarterly_pretax_margin_percent,lookback=252),252) + +group_neutralize(divide(ts_backfill(accounts_receivable_gross,lookback=252),ts_backfill(accounts_payable_trade_creditors,lookback=252)),"industry") + +group_neutralize(signed_power(ts_backfill(customer_revenue_share_percent,lookback=252),0.3),"industry") + +group_neutralize(ts_backfill(quarterly_sga_to_sales_ratio,lookback=252),"industry") + +ts_std_dev(ts_backfill(quarterly_sga_to_sales_ratio,lookback=252),252) + +group_neutralize(ts_backfill(net_profit_margin_quarterly,lookback=252),"industry") + +group_neutralize(ts_backfill(pretax_margin_quarterly,lookback=252),"industry") + +ts_delta(ts_backfill(net_profit_margin_quarterly,lookback=252),252) + +group_neutralize(ts_covariance(ts_backfill(quarterly_gross_margin_percent,lookback=252),ts_backfill(quarterly_net_profit_margin_percent,lookback=252),252),"industry") + +group_neutralize(ts_backfill(quarterly_asset_turnover,lookback=252),"industry") + +ts_rank(ts_backfill(quarterly_asset_turnover,lookback=252),252) + +group_neutralize(divide(ts_backfill(sales_revenue_total,lookback=252),ts_backfill(accounts_receivable_gross,lookback=252)),"industry") + +group_neutralize(ts_scale(ts_backfill(quarterly_gross_margin_percent,lookback=252),252),"industry") + +rank(group_neutralize(ts_backfill(quarterly_pretax_margin_percent,lookback=252),"industry")) + +ts_rank(group_neutralize(ts_backfill(quarterly_sga_to_sales_ratio,lookback=252),"industry"),252) + +group_neutralize(subtract(ts_backfill(quarterly_operating_margin_percent,lookback=252),ts_backfill(quarterly_pretax_margin_percent,lookback=252)),"industry") + +group_neutralize(ts_backfill(fnd17_margin5yr,lookback=252),"industry") + +ts_delta(ts_backfill(fnd17_margin5yr,lookback=252),252) \ No newline at end of file diff --git a/generated_alpha/2026/01/26/Qwen_Qwen3-VL-235B-A22B-Instruct_171034.txt b/generated_alpha/2026/01/26/Qwen_Qwen3-VL-235B-A22B-Instruct_171034.txt new file mode 100644 index 0000000..6c9321e --- /dev/null +++ b/generated_alpha/2026/01/26/Qwen_Qwen3-VL-235B-A22B-Instruct_171034.txt @@ -0,0 +1,109 @@ +group_neutralize(divide(add(quarterly_cost_of_goods_sold, quarterly_receivables), quarterly_sales_revenue_total), industry) + +ts_mean(subtract(quarterly_gross_margin_percent, quarterly_net_profit_margin_percent), 120) + +group_zscore(ts_delta(quarterly_inventory_turnover, 20), sector) + +normalize(multiply(quarterly_operating_margin_percent, quarterly_pretax_margin_percent), useStd=true) + +rank(ts_scale(quarterly_receivables_turnover, 90, constant=0.5)) + +group_rank(divide(quarterly_receivables, quarterly_sales_revenue_total), industry) + +ts_zscore(add(quarterly_sga_to_sales_ratio, quarterly_asset_turnover), 60) + +winsorize(ts_arg_max(quarterly_gross_margin_percent, 180), std=3) + +group_scale(ts_mean(quarterly_net_profit_margin_percent, 120), sector) + +ts_regression(quarterly_sales_revenue_total, quarterly_cost_of_goods_sold, 90, lag=0, rettype=0) + +quantile(ts_delta(quarterly_pretax_margin_percent, 30), driver="uniform", sigma=1.5) + +group_backfill(ts_std_dev(quarterly_operating_margin_percent, 60), industry, 90, std=4.0) + +ts_quantile(quarterly_receivables_turnover, 120, driver="gaussian") + +group_mean(ts_av_diff(quarterly_gross_margin_percent, 90), weight=1, group=sector) + +ts_count_nans(quarterly_inventory_turnover, 30) + +ts_decay_linear(ts_delta(quarterly_sales_revenue_total, 20), 60, dense=false) + +ts_corr(quarterly_cost_of_goods_sold, quarterly_receivables, 90) + +ts_product(quarterly_net_profit_margin_percent, 30) + +ts_max(quarterly_operating_margin_percent, 120) + +ts_min(quarterly_pretax_margin_percent, 90) + +ts_sum(quarterly_sga_to_sales_ratio, 60) + +ts_target_tvr_decay(quarterly_gross_margin_percent, lambda_min=0, lambda_max=1, target_tvr=0.1) + +ts_target_tvr_delta_limit(quarterly_receivables_turnover, quarterly_inventory_turnover, lambda_min=0, lambda_max=1, target_tvr=0.1) + +ts_step(1) + +ts_backfill(quarterly_sales_revenue_total, lookback=90, k=1, ignore="NAN") + +ts_covariance(quarterly_cost_of_goods_sold, quarterly_receivables, 90) + +ts_arg_min(quarterly_net_profit_margin_percent, 180) + +ts_arg_max(quarterly_operating_margin_percent, 120) + +ts_av_diff(quarterly_pretax_margin_percent, 90) + +ts_scale(quarterly_gross_margin_percent, 90, constant=0) + +ts_rank(quarterly_receivables_turnover, 120, constant=0) + +ts_zscore(quarterly_inventory_turnover, 60) + +group_neutralize(ts_mean(quarterly_sga_to_sales_ratio, 90), industry) + +group_zscore(ts_delta(quarterly_sales_revenue_total, 30), sector) + +group_rank(ts_std_dev(quarterly_net_profit_margin_percent, 60), industry) + +group_scale(ts_sum(quarterly_operating_margin_percent, 90), sector) + +normalize(ts_regression(quarterly_receivables, quarterly_sales_revenue_total, 90, lag=0, rettype=0), useStd=true) + +rank(ts_quantile(quarterly_pretax_margin_percent, 120, driver="gaussian")) + +winsorize(ts_corr(quarterly_cost_of_goods_sold, quarterly_receivables, 90), std=4) + +ts_decay_linear(ts_delta(quarterly_gross_margin_percent, 20), 60, dense=false) + +ts_product(quarterly_operating_margin_percent, 30) + +ts_max(quarterly_receivables_turnover, 120) + +ts_min(quarterly_inventory_turnover, 90) + +ts_sum(quarterly_pretax_margin_percent, 60) + +ts_target_tvr_decay(quarterly_sga_to_sales_ratio, lambda_min=0, lambda_max=1, target_tvr=0.1) + +ts_target_tvr_delta_limit(quarterly_net_profit_margin_percent, quarterly_gross_margin_percent, lambda_min=0, lambda_max=1, target_tvr=0.1) + +ts_step(1) + +ts_backfill(quarterly_receivables, lookback=90, k=1, ignore="NAN") + +ts_covariance(quarterly_sales_revenue_total, quarterly_cost_of_goods_sold, 90) + +ts_arg_min(quarterly_operating_margin_percent, 180) + +ts_arg_max(quarterly_pretax_margin_percent, 120) + +ts_av_diff(quarterly_receivables_turnover, 90) + +ts_scale(quarterly_inventory_turnover, 90, constant=0) + +ts_rank(quarterly_sga_to_sales_ratio, 120, constant=0) + +ts_zscore(quarterly_net_profit_margin_percent, 60) \ No newline at end of file diff --git a/generated_alpha/2026/01/27/1.txt b/generated_alpha/2026/01/27/1.txt new file mode 100644 index 0000000..0648924 --- /dev/null +++ b/generated_alpha/2026/01/27/1.txt @@ -0,0 +1,306 @@ +rank(divide(fnd28_growthratesa_value_08631a, max(fnd28_growthratesa_value_08635a, 0.001))) +zscore(group_neutralize(abs(subtract(anl14_median_revenue_fy1, anl14_median_revenue_fy2)), "sw1")) +normalize(multiply(quarterly_return_on_average_assets, fnd28_ratesq_value_08301q)) +rank(group_scale(sqrt(abs(fnd28_growthratesa_value_08601a)), "sw1")) +zscore(divide(anl14_median_eps_fy1, max(anl14_median_eps_fy2, 0.001))) +normalize(add(quarterly_debt_to_equity_ratio_2, fnd28_ratesq_value_08231q)) +rank(subtract(ts_mean(fnd28_growthratesa_value_08680a, 720), fnd28_growthratesa_value_08676a)) +zscore(multiply(sqrt(anl14_median_roa_fy1), quarterly_quick_ratio)) +group_neutralize(divide(fnd28_ratesq_value_08366q, max(fnd28_ratesq_value_08316q, 0.001)), "sw1") +rank(ts_zscore(add(anl14_median_ebit_fy1, anl14_median_ebitda_fy1), 720)) +normalize(abs(subtract(quarterly_total_debt_to_equity_ratio, quarterly_long_term_debt_to_equity_ratio))) +zscore(multiply(ts_rank(fnd28_growthratesa_value_08606a, 720), fnd17_qcurratio)) +rank(group_zscore(sqrt(abs(fnd28_growthratesa_value_08611a)), "sw1")) +normalize(divide(anl14_median_cfps_fy1, max(anl14_median_cfps_fy2, 0.001))) +zscore(add(ts_mean(quarterly_pe_ratio_low, 720), quarterly_price_to_sales_ratio)) +rank(subtract(anl14_median_ntp_fy1, anl14_median_ntprep_fy1)) +group_neutralize(multiply(sqrt(quarterly_current_ratio), fnd28_ratesq_value_08306q), "sw1") +normalize(ts_zscore(divide(fnd28_growthratesa_value_08646a, max(fnd28_growthratesa_value_08650a, 0.001)), 720)) +zscore(abs(multiply(anl14_median_roe_fy1, return_on_average_assets_quarterly))) +rank(add(group_rank(fnd28_ratesq_value_08401q, "sw1"), ts_rank(quarterly_reinvestment_rate, 720))) +normalize(divide(ts_sum(fnd28_growthratesa_value_08621a, 720), max(ts_sum(fnd28_growthratesa_value_08625a, 720), 0.001))) +zscore(subtract(anl14_median_bvps_fy1, anl14_median_bvps_fy2)) +group_neutralize(sqrt(abs(multiply(fnd28_ratesq_value_08326q, quarterly_interest_coverage))), "sw1") +rank(ts_mean(divide(anl14_median_div_fy1, max(anl14_median_div_fy2, 0.001)), 720)) +normalize(multiply(ts_std_dev(fnd28_growthratesa_value_08616a, 720), fnd17_apayratio)) +zscore(add(quarterly_sga_to_revenue_ratio, fnd28_ratesq_value_08311q)) +rank(subtract(group_mean(fnd28_ratesq_value_08381q, 1, "sw1"), fnd28_ratesq_value_08381q)) +group_zscore(abs(divide(anl14_median_capex_fy1, max(anl14_median_capex_fy2, 0.001))), "sw1") +normalize(ts_rank(multiply(quarterly_tax_rate_percent, fnd17_ttmtaxrate), 720)) +zscore(sqrt(add(anl14_median_ebitda_fy1, anl14_median_ebit_fy2))) +rank(divide(ts_delta(fnd28_growthratesa_value_08636a, 30), max(ts_std_dev(fnd28_growthratesa_value_08636a, 720), 0.001))) +group_neutralize(add(quarterly_pe_ratio_high, fnd28_ratesq_value_08321q), "sw1") +normalize(abs(multiply(ts_corr(fnd28_growthratesa_value_08601a, fnd28_growthratesa_value_08606a, 720), quarterly_dividend_payout_ratio))) +zscore(subtract(anl14_median_epsrep_fy1, anl14_median_epsrep_fy2)) +rank(sqrt(group_scale(divide(fnd28_ratesq_value_08287q, max(fnd28_ratesq_value_08241q, 0.001)), "sw1"))) +normalize(multiply(ts_decay_linear(fnd28_growthratesa_value_08640a, 720), quarterly_quick_ratio_2)) +zscore(add(ts_mean(quarterly_debt_to_assets_ratio, 720), fnd17_siratio1)) +rank(group_rank(subtract(anl14_median_nav_fy1, anl14_median_ndebt_fy1), "sw1")) +group_zscore(abs(ts_av_diff(fnd28_ratesq_value_08301q, 720)), "sw1") +normalize(divide(anl14_median_ptp_fy1, max(anl14_median_ptprep_fy1, 0.001))) +zscore(multiply(ts_quantile(fnd28_growthratesa_value_08615a, 720), quarterly_effective_tax_rate)) +rank(add(ts_backfill(fnd28_ratesq_value_08251q, 720), fnd28_ratesq_value_08251q)) +group_neutralize(sqrt(ts_sum(anl14_median_opp_fy1, 720)), "sw1") +normalize(abs(subtract(ts_max(fnd28_growthratesa_value_08626a, 720), ts_min(fnd28_growthratesa_value_08626a, 720)))) +zscore(divide(quarterly_return_on_average_equity, max(ttm_return_on_average_equity, 0.001))) +rank(multiply(group_mean(quarterly_inventory_turnover_ratio, 1, "sw1"), fnd28_ratesq_value_08416q)) +group_zscore(ts_regression(fnd28_growthratesa_value_08676a, fnd28_growthratesa_value_08680a, 720), "sw1") +normalize(add(sqrt(anl14_median_revenue_fp1), anl14_median_revenue_fp2)) +zscore(abs(ts_count_nans(fnd28_ratesq_value_08346q, 720))) +rank(divide(group_max(fnd28_growthratesa_value_08604a, "sw1"), max(group_min(fnd28_growthratesa_value_08604a, "sw1"), 0.001))) +ts_zscore(group_zscore(divide(ts_sum(fnd28_ratesq_value_08601q,504),ts_sum(fnd28_ratesq_value_08601q,756)),fnd28_ratesq_value_08601q),504) + +ts_rank(multiply(ts_mean(quarterly_total_debt_to_equity_ratio,252),subtract(1,ts_mean(anl14_median_eps_fp1,126))),504) + +ts_zscore(group_zscore(subtract(ts_mean(fnd28_ratesq_value_08326q,504),ts_mean(fnd28_ratesq_value_08301q,504)),fnd28_ratesq_value_08326q),252) + +ts_rank(divide(ts_sum(anl14_median_revenue_fp1,504),ts_sum(anl14_median_revenue_fp2,504)),504) + +ts_corr(subtract(fnd28_ratesq_value_08636q,fnd28_ratesq_value_08631q),subtract(fnd28_ratesq_value_08616q,fnd28_ratesq_value_08621q),252) + +ts_mean(group_zscore(divide(ts_sum(anl14_median_ebit_fp1,504),ts_sum(anl14_median_ebit_fp2,504)),anl14_median_ebit_fp1),252) + +ts_rank(subtract(ts_zscore(fnd28_ratesq_value_08311q,504),ts_zscore(fnd28_ratesq_value_08316q,504)),504) + +ts_zscore(group_zscore(divide(ts_mean(quarterly_pe_ratio_low,252),ts_mean(quarterly_pe_ratio_high,252)),quarterly_pe_ratio_low),252) + +ts_corr(fnd28_ratesq_value_08601q,subtract(fnd28_ratesq_value_08606q,fnd28_ratesq_value_08611q),252) + +ts_rank(multiply(ts_zscore(quarterly_current_ratio,504),subtract(1,ts_zscore(quarterly_quick_ratio,504))),504) + +ts_mean(group_zscore(subtract(ts_sum(anl14_median_ebitda_fp1,504),ts_sum(anl14_median_ebitda_fp2,504)),anl14_median_ebitda_fp1),252) + +ts_zscore(divide(ts_sum(anl14_median_capex_fp1,504),ts_sum(anl14_median_capex_fp2,504)),504) + +ts_rank(subtract(ts_mean(fnd28_ratesq_value_08371q,504),ts_mean(fnd28_ratesq_value_08381q,504)),504) + +ts_corr(anl14_median_eps_fp1,subtract(anl14_median_eps_fp2,anl14_median_eps_fp3),252) + +ts_mean(group_zscore(divide(ts_mean(fnd28_ratesq_value_08416q,252),ts_mean(fnd28_ratesq_value_08421q,252)),fnd28_ratesq_value_08416q),252) + +ts_zscore(subtract(ts_sum(fnd28_ratesq_value_08231q,504),ts_sum(fnd28_ratesq_value_08226q,504)),504) + +ts_rank(multiply(ts_zscore(quarterly_total_debt_to_assets_ratio,504),ts_zscore(quarterly_long_term_debt_to_assets_ratio,504)),504) + +ts_corr(fnd28_ratesq_value_08346q,subtract(fnd28_ratesq_value_08366q,fnd28_ratesq_value_08371q),252) + +ts_mean(group_zscore(divide(ts_sum(anl14_median_roe_fp1,504),ts_sum(anl14_median_roe_fp2,504)),anl14_median_roe_fp1),252) + +ts_zscore(subtract(ts_mean(fnd28_ratesq_value_08111q,504),ts_mean(fnd28_ratesq_value_08106q,504)),504) + +ts_rank(divide(ts_sum(anl14_median_ntp_fp1,504),ts_sum(anl14_median_ntp_fp2,504)),504) + +ts_corr(fnd28_ratesq_value_08676q,subtract(fnd28_ratesq_value_08601q,fnd28_ratesq_value_08606q),252) + +ts_mean(group_zscore(subtract(ts_sum(fnd28_ratesq_value_08301q,504),ts_sum(fnd28_ratesq_value_08306q,504)),fnd28_ratesq_value_08301q),252) + +ts_zscore(divide(ts_mean(anl14_median_div_fp1,252),ts_mean(anl14_median_div_fp2,252)),504) + +ts_rank(multiply(ts_zscore(quarterly_payout_ratio,504),subtract(1,ts_zscore(quarterly_reinvestment_rate,504))),504) + +ts_corr(anl14_median_bvps_fp1,subtract(anl14_median_bvps_fp2,anl14_median_bvps_fp3),252) + +ts_mean(group_zscore(divide(ts_sum(anl14_median_roa_fy1,504),ts_sum(anl14_median_roa_fy2,504)),anl14_median_roa_fy1),252) + +ts_zscore(subtract(ts_sum(fnd28_ratesq_value_08266q,504),ts_sum(fnd28_ratesq_value_08287q,504)),504) + +ts_rank(divide(ts_mean(fnd28_ratesq_value_08401q,252),ts_mean(fnd28_ratesq_value_08396q,252)),504) + +ts_corr(fnd28_ratesq_value_08321q,subtract(fnd28_ratesq_value_08326q,fnd28_ratesq_value_08331q),252) + +ts_mean(group_zscore(subtract(ts_sum(anl14_median_cfps_fp1,504),ts_sum(anl14_median_cfps_fp2,504)),anl14_median_cfps_fp1),252) + +ts_zscore(divide(ts_sum(anl14_median_ptp_fp1,504),ts_sum(anl14_median_ptp_fp2,504)),504) + +ts_rank(multiply(ts_zscore(quarterly_sga_to_revenue_ratio,504),ts_zscore(quarterly_sga_to_sales_ratio,504)),504) + +ts_corr(fnd28_ratesq_value_08611q,subtract(fnd28_ratesq_value_08616q,fnd28_ratesq_value_08621q),252) + +ts_mean(group_zscore(divide(ts_mean(quarterly_debt_to_equity_ratio,252),ts_mean(quarterly_debt_to_equity_ratio_prior_year,252)),quarterly_debt_to_equity_ratio),252) + +ts_zscore(subtract(ts_mean(fnd28_ratesq_value_08381q,504),ts_mean(fnd28_ratesq_value_08386q,504)),504) + +ts_rank(divide(ts_sum(anl14_median_opp_fp1,504),ts_sum(anl14_median_opp_fy1,504)),504) + +ts_corr(fnd28_ratesq_value_08631q,subtract(fnd28_ratesq_value_08636q,fnd28_ratesq_value_08641q),252) + +ts_mean(group_zscore(subtract(ts_sum(fnd28_ratesq_value_08311q,504),ts_sum(fnd28_ratesq_value_08316q,504)),fnd28_ratesq_value_08311q),252) + +ts_zscore(divide(ts_mean(anl14_median_ndebt_fp1,252),ts_mean(anl14_median_ndebt_fp2,252)),504) + +ts_rank(multiply(ts_zscore(quarterly_interest_coverage,504),subtract(1,ts_zscore(quarterly_interest_coverage_db,504))),504) + +ts_corr(fnd28_ratesq_value_08606q,subtract(fnd28_ratesq_value_08611q,fnd28_ratesq_value_08616q),252) + +ts_mean(group_zscore(divide(ts_sum(anl14_median_epsrep_fp1,504),ts_sum(anl14_median_epsrep_fp2,504)),anl14_median_epsrep_fp1),252) + +ts_zscore(subtract(ts_sum(fnd28_ratesq_value_08251q,504),ts_sum(fnd28_ratesq_value_08261q,504)),504) + +ts_rank(divide(ts_mean(fnd28_ratesq_value_08396q,252),ts_mean(fnd28_ratesq_value_08401q,252)),504) + +ts_corr(fnd28_ratesq_value_08616q,subtract(fnd28_ratesq_value_08621q,fnd28_ratesq_value_08626q),252) + +ts_mean(group_zscore(subtract(ts_sum(anl14_median_ntprep_fp1,504),ts_sum(anl14_median_ntprep_fp2,504)),anl14_median_ntprep_fp1),252) + +ts_zscore(divide(ts_sum(anl14_median_ptprep_fy1,504),ts_sum(anl14_median_ptprep_fy2,504)),504) + +reverse(zscore(group_neutralize(ts_delta(fnd28_ishtspmnt_value_01084a, 12), industry))) +rank(group_scale(subtract(fnd28_fsa1_value_03054a, ts_mean(fnd28_fsa1_value_03054a, 24)), industry)) +normalize(multiply(sqrt(abs(ts_delta(bonus_proportion_total_comp, 6))), sign(ts_corr(bonus_proportion_total_comp, quarterly_employee_turnover_rate, 18)))) +group_zscore(divide(ts_sum(fnd28_ishtspmnt_value_01084a, 12), ts_sum(annual_base_salary, 12)), industry_grouping_level2_top3000_2) +winsorize(ts_zscore(subtract(ts_mean(fnd28_ishtspmnt_value_01084a, 6), ts_mean(bonus_proportion_total_comp, 6)), 12), 3) +sign(ts_av_diff(divide(fnd28_ishtspmnt_value_01084a, annual_base_salary), 18)) +ts_quantile(group_rank(abs(ts_delta(estimated_value_exercisable_options, 9)), industry), 12, "gaussian") +if_else(ts_arg_max(fnd28_ishtspmnt_value_01084a, 12) < 3, reverse(zscore(ts_mean(bonus_proportion_total_comp, 6))), zscore(ts_mean(bonus_proportion_total_comp, 6))) +ts_scale(add(ts_std_dev(fnd28_ishtspmnt_value_01084a, 12), ts_std_dev(annual_base_salary, 12)), 24) +group_neutralize(ts_corr(fnd28_ishtspmnt_value_01084a, quarterly_reinvestment_rate_2, 18), pca_industry_grouping_method4_20) +ts_decay_linear(multiply(ts_delta(fnd28_ishtspmnt_value_01084a, 6), sign(ts_delta(bonus_proportion_total_comp, 6))), 12) +subtract(ts_sum(fnd28_ishtspmnt_value_01084a, 6), ts_sum(bonus_proportion_total_comp, 12)) +divide(ts_mean(fnd28_ishtspmnt_value_01084a, 12), ts_mean(annual_base_salary, 12)) +ts_backfill(abs(ts_delta(divide(fnd28_ishtspmnt_value_01084a, annual_base_salary), 6)), 12) +ts_count_nans(fnd28_ishtspmnt_value_01084a, 12) +ts_covariance(fnd28_ishtspmnt_value_01084a, quarterly_employee_turnover_rate, 18) +ts_rank(abs(ts_delta(fnd28_ishtspmnt_value_01084a, 6)), 12) +ts_arg_min(ts_mean(fnd28_ishtspmnt_value_01084a, 6), 12) +ts_arg_max(ts_mean(bonus_proportion_total_comp, 6), 12) +signed_power(ts_zscore(subtract(fnd28_ishtspmnt_value_01084a, bonus_proportion_total_comp), 12), 1.5) +ts_product(sign(ts_delta(fnd28_ishtspmnt_value_01084a, 6)), 6) +ts_delay(ts_mean(fnd28_ishtspmnt_value_01084a, 6), 3) +multiply(ts_std_dev(fnd28_ishtspmnt_value_01084a, 12), ts_std_dev(quarterly_employee_turnover_rate, 12)) +add(ts_mean(fnd28_ishtspmnt_value_01084a, 6), ts_mean(quarterly_reinvestment_rate_2, 6)) +abs(ts_av_diff(divide(fnd28_ishtspmnt_value_01084a, quarterly_employee_turnover_rate), 12)) +group_rank(ts_zscore(fnd28_ishtspmnt_value_01084a, 12), industry_grouping_level20_top3000_2) +winsorize(subtract(fnd28_ishtspmnt_value_01084a, ts_mean(fnd28_ishtspmnt_value_01084a, 12)), 2) +ts_regression(fnd28_ishtspmnt_value_01084a, quarterly_employee_turnover_rate, 18, 0, 1) +reverse(group_zscore(ts_mean(fnd28_ishtspmnt_value_01084a, 12), top1500_pca_factor2_grouping50)) +sign(ts_delta(divide(fnd28_ishtspmnt_value_01084a, annual_base_salary), 12)) +ts_sum(sign(ts_delta(fnd28_ishtspmnt_value_01084a, 3)), 12) +ts_corr(bonus_proportion_total_comp, quarterly_reinvestment_rate_2, 12) +normalize(ts_mean(subtract(fnd28_ishtspmnt_value_01084a, bonus_proportion_total_comp), 12)) +ts_quantile(divide(fnd28_ishtspmnt_value_01084a, annual_base_salary), 12, "uniform") +ts_av_diff(multiply(fnd28_ishtspmnt_value_01084a, quarterly_employee_turnover_rate), 18) +group_neutralize(ts_rank(abs(ts_delta(bonus_proportion_total_comp, 6)), 12), resampled_pca_factor_7) +ts_decay_linear(subtract(fnd28_ishtspmnt_value_01084a, ts_mean(fnd28_ishtspmnt_value_01084a, 12)), 18) +multiply(ts_delta(fnd28_ishtspmnt_value_01084a, 6), ts_delta(quarterly_employee_turnover_rate, 6)) +ts_scale(abs(subtract(fnd28_ishtspmnt_value_01084a, bonus_proportion_total_comp)), 12) +divide(ts_sum(fnd28_ishtspmnt_value_01084a, 12), ts_sum(quarterly_employee_turnover_rate, 12)) +ts_backfill(ts_zscore(bonus_proportion_total_comp, 12), 18) +ts_count_nans(bonus_proportion_total_comp, 18) +ts_covariance(bonus_proportion_total_comp, quarterly_reinvestment_rate_2, 12) +ts_rank(ts_mean(fnd28_ishtspmnt_value_01084a, 6), 18) +ts_arg_min(divide(fnd28_ishtspmnt_value_01084a, annual_base_salary), 12) +ts_arg_max(multiply(fnd28_ishtspmnt_value_01084a, quarterly_employee_turnover_rate), 18) +signed_power(subtract(ts_mean(fnd28_ishtspmnt_value_01084a, 6), ts_mean(bonus_proportion_total_comp, 6)), 2) +ts_product(ts_delta(fnd28_ishtspmnt_value_01084a, 3), 6) +ts_delay(divide(fnd28_ishtspmnt_value_01084a, annual_base_salary), 6) +add(ts_std_dev(bonus_proportion_total_comp, 12), ts_std_dev(quarterly_reinvestment_rate_2, 12)) +abs(ts_av_diff(fnd28_ishtspmnt_value_01084a, 18)) +group_rank(subtract(fnd28_ishtspmnt_value_01084a, bonus_proportion_total_comp), top2000_factor2_group50_score) +winsorize(ts_zscore(fnd28_ishtspmnt_value_01084a, 18), 4) +reverse(group_neutralize(ts_rank(subtract(divide(fnd28_fsa1_value_01706a, fnd28_statisticsa_value_07230a), divide(fnd28_fsa1_value_02001a, fnd28_bsasseta_value_02051a)), 252), industry)) + +reverse(group_neutralize(ts_rank(subtract(divide(ts_mean(fnd28_ishta_value_01101a, 63), ts_mean(fnd28_ishta_value_01250a, 63)), divide(ts_mean(fnd28_fsa1_value_03054a, 63), ts_mean(fnd28_fsa1_value_02051a, 63))), 252), industry)) + +reverse(group_neutralize(ts_zscore(subtract(ts_delta(divide(fnd28_fsa1_value_01706a, fnd28_bsasseta_value_02098a), 21), ts_delta(divide(fnd28_fsa1_value_02001a, fnd28_bsasseta_value_02051a), 21)), 63), industry)) + +reverse(group_neutralize(ts_rank(subtract(ts_delta(divide(fnd28_ishta_value_01101a, fnd28_ishta_value_01250a), 126), ts_delta(divide(fnd28_fsa1_value_03054a, fnd28_fsa1_value_02051a), 126)), 252), industry)) + +reverse(group_neutralize(ts_corr(ts_delta(divide(fnd28_fsa1_value_01706a, fnd28_bsasseta_value_02098a), 5), ts_delta(divide(fnd28_fsa1_value_02001a, fnd28_bsasseta_value_02051a), 5), 252), industry)) + +reverse(group_neutralize(ts_rank(ts_av_diff(subtract(divide(fnd28_ishta_value_01101a, fnd28_ishta_value_01250a), divide(fnd28_fsa1_value_03054a, fnd28_fsa1_value_02051a)), 252), 126), industry)) + +reverse(group_neutralize(ts_sum(subtract(divide(ts_delay(fnd28_fsa1_value_01706a, 5), ts_delay(fnd28_statisticsa_value_07230a, 5)), divide(ts_delay(fnd28_fsa1_value_02001a, 5), ts_delay(fnd28_bsasseta_value_02051a, 5))), 21), industry)) + +reverse(group_neutralize(ts_regression(subtract(divide(fnd28_ishta_value_01101a, fnd28_ishta_value_01250a), divide(fnd28_fsa1_value_03054a, fnd28_fsa1_value_02051a)), ts_step(1), 252, 0, 1), industry)) + +reverse(group_neutralize(ts_rank(ts_decay_linear(subtract(divide(fnd28_fsa1_value_01706a, fnd28_bsasseta_value_02098a), divide(fnd28_fsa1_value_02001a, fnd28_bsasseta_value_02051a)), 21), 126), industry)) + +reverse(group_neutralize(ts_zscore(ts_delta(subtract(divide(fnd28_ishta_value_01101a, fnd28_ishta_value_01250a), divide(fnd28_fsa1_value_03054a, fnd28_fsa1_value_02051a)), 42), 63), industry)) + +reverse(group_neutralize(ts_rank(subtract(ts_mean(divide(fnd28_fsa1_value_01706a, fnd28_statisticsa_value_07230a), 21), ts_mean(divide(fnd28_fsa1_value_02001a, fnd28_bsasseta_value_02051a), 21)), 252), industry)) + +reverse(group_neutralize(ts_av_diff(subtract(divide(ts_delay(fnd28_ishta_value_01101a, 10), ts_delay(fnd28_ishta_value_01250a, 10)), divide(ts_delay(fnd28_fsa1_value_03054a, 10), ts_delay(fnd28_fsa1_value_02051a, 10))), 252), industry)) + +reverse(group_neutralize(ts_covariance(subtract(divide(fnd28_fsa1_value_01706a, fnd28_bsasseta_value_02098a), divide(fnd28_fsa1_value_02001a, fnd28_bsasseta_value_02051a)), ts_step(1), 126), industry)) + +reverse(group_neutralize(ts_rank(ts_sum(subtract(divide(fnd28_ishta_value_01101a, fnd28_ishta_value_01250a), divide(fnd28_fsa1_value_03054a, fnd28_fsa1_value_02051a)), 42), 252), industry)) + +reverse(group_neutralize(ts_zscore(ts_av_diff(subtract(divide(ts_delay(fnd28_fsa1_value_01706a, 5), ts_delay(fnd28_statisticsa_value_07230a, 5)), divide(ts_delay(fnd28_fsa1_value_02001a, 5), ts_delay(fnd28_bsasseta_value_02051a, 5))), 126), 63), industry)) + +reverse(group_neutralize(ts_rank(ts_decay_linear(subtract(divide(fnd28_ishta_value_01101a, fnd28_ishta_value_01250a), divide(fnd28_fsa1_value_03054a, fnd28_fsa1_value_02051a)), 42), 126), industry)) + +reverse(group_neutralize(ts_corr(ts_delta(divide(fnd28_fsa1_value_01706a, fnd28_bsasseta_value_02098a), 10), ts_delta(divide(fnd28_fsa1_value_02001a, fnd28_bsasseta_value_02051a), 10), 252), industry)) + +reverse(group_neutralize(ts_rank(subtract(ts_delta(divide(fnd28_ishta_value_01101a, fnd28_ishta_value_01250a), 21), ts_delta(divide(fnd28_fsa1_value_03054a, fnd28_fsa1_value_02051a), 21)), 252), industry)) + +reverse(group_neutralize(ts_sum(ts_av_diff(subtract(divide(ts_delay(fnd28_fsa1_value_01706a, 5), ts_delay(fnd28_statisticsa_value_07230a, 5)), divide(ts_delay(fnd28_fsa1_value_02001a, 5), ts_delay(fnd28_bsasseta_value_02051a, 5))), 126), 21), industry)) + +reverse(group_neutralize(ts_regression(subtract(divide(fnd28_ishta_value_01101a, fnd28_ishta_value_01250a), divide(fnd28_fsa1_value_03054a, fnd28_fsa1_value_02051a)), ts_step(1), 126, 0, 1), industry)) + +reverse(group_neutralize(ts_rank(ts_zscore(subtract(divide(fnd28_fsa1_value_01706a, fnd28_bsasseta_value_02098a), divide(fnd28_fsa1_value_02001a, fnd28_bsasseta_value_02051a)), 63), 252), industry)) + +reverse(group_neutralize(ts_av_diff(subtract(ts_mean(divide(fnd28_ishta_value_01101a, fnd28_ishta_value_01250a), 42), ts_mean(divide(fnd28_fsa1_value_03054a, fnd28_fsa1_value_02051a), 42)), 252), industry)) + +reverse(group_neutralize(ts_covariance(subtract(divide(ts_delay(fnd28_fsa1_value_01706a, 10), ts_delay(fnd28_statisticsa_value_07230a, 10)), divide(ts_delay(fnd28_fsa1_value_02001a, 10), ts_delay(fnd28_bsasseta_value_02051a, 10))), ts_step(1), 252), industry)) + +reverse(group_neutralize(ts_rank(ts_sum(subtract(divide(fnd28_ishta_value_01101a, fnd28_ishta_value_01250a), divide(fnd28_fsa1_value_03054a, fnd28_fsa1_value_02051a)), 63), 252), industry)) + +reverse(group_neutralize(ts_zscore(ts_decay_linear(subtract(divide(fnd28_fsa1_value_01706a, fnd28_bsasseta_value_02098a), divide(fnd28_fsa1_value_02001a, fnd28_bsasseta_value_02051a)), 21), 63), industry)) + +reverse(group_neutralize(ts_rank(ts_av_diff(subtract(divide(fnd28_ishta_value_01101a, fnd28_ishta_value_01250a), divide(fnd28_fsa1_value_03054a, fnd28_fsa1_value_02051a)), 126), 252), industry)) + +reverse(group_neutralize(ts_corr(ts_delta(divide(fnd28_fsa1_value_01706a, fnd28_bsasseta_value_02098a), 21), ts_delta(divide(fnd28_fsa1_value_02001a, fnd28_bsasseta_value_02051a), 21), 252), industry)) + +reverse(group_neutralize(ts_rank(subtract(ts_delta(divide(fnd28_ishta_value_01101a, fnd28_ishta_value_01250a), 42), ts_delta(divide(fnd28_fsa1_value_03054a, fnd28_fsa1_value_02051a), 42)), 252), industry)) + +reverse(group_neutralize(ts_sum(ts_zscore(subtract(divide(ts_delay(fnd28_fsa1_value_01706a, 5), ts_delay(fnd28_statisticsa_value_07230a, 5)), divide(ts_delay(fnd28_fsa1_value_02001a, 5), ts_delay(fnd28_bsasseta_value_02051a, 5))), 63), 21), industry)) + +reverse(group_neutralize(ts_regression(subtract(divide(fnd28_ishta_value_01101a, fnd28_ishta_value_01250a), divide(fnd28_fsa1_value_03054a, fnd28_fsa1_value_02051a)), ts_step(1), 252, 0, 2), industry)) + +reverse(group_neutralize(ts_rank(ts_decay_linear(subtract(divide(fnd28_fsa1_value_01706a, fnd28_bsasseta_value_02098a), divide(fnd28_fsa1_value_02001a, fnd28_bsasseta_value_02051a)), 42), 126), industry)) + +reverse(group_neutralize(ts_av_diff(subtract(ts_mean(divide(fnd28_ishta_value_01101a, fnd28_ishta_value_01250a), 21), ts_mean(divide(fnd28_fsa1_value_03054a, fnd28_fsa1_value_02051a), 21)), 252), industry)) + +reverse(group_neutralize(ts_covariance(subtract(divide(ts_delay(fnd28_fsa1_value_01706a, 5), ts_delay(fnd28_statisticsa_value_07230a, 5)), divide(ts_delay(fnd28_fsa1_value_02001a, 5), ts_delay(fnd28_bsasseta_value_02051a, 5))), ts_step(1), 126), industry)) + +reverse(group_neutralize(ts_rank(ts_sum(subtract(divide(fnd28_ishta_value_01101a, fnd28_ishta_value_01250a), divide(fnd28_fsa1_value_03054a, fnd28_fsa1_value_02051a)), 84), 252), industry)) + +reverse(group_neutralize(ts_zscore(ts_av_diff(subtract(divide(fnd28_fsa1_value_01706a, fnd28_bsasseta_value_02098a), divide(fnd28_fsa1_value_02001a, fnd28_bsasseta_value_02051a)), 126), 63), industry)) + +reverse(group_neutralize(ts_rank(ts_delta(subtract(divide(fnd28_ishta_value_01101a, fnd28_ishta_value_01250a), divide(fnd28_fsa1_value_03054a, fnd28_fsa1_value_02051a)), 21), 252), industry)) + +reverse(group_neutralize(ts_corr(ts_decay_linear(divide(fnd28_fsa1_value_01706a, fnd28_bsasseta_value_02098a), 10), ts_decay_linear(divide(fnd28_fsa1_value_02001a, fnd28_bsasseta_value_02051a), 10), 252), industry)) + +reverse(group_neutralize(ts_rank(subtract(ts_zscore(divide(fnd28_ishta_value_01101a, fnd28_ishta_value_01250a), 63), ts_zscore(divide(fnd28_fsa1_value_03054a, fnd28_fsa1_value_02051a), 63)), 252), industry)) + +reverse(group_neutralize(ts_sum(ts_regression(subtract(divide(fnd28_fsa1_value_01706a, fnd28_statisticsa_value_07230a), divide(fnd28_fsa1_value_02001a, fnd28_bsasseta_value_02051a)), ts_step(1), 126, 0, 1), 21), industry)) + +reverse(group_neutralize(ts_av_diff(subtract(divide(ts_delay(fnd28_ishta_value_01101a, 5), ts_delay(fnd28_ishta_value_01250a, 5)), divide(ts_delay(fnd28_fsa1_value_03054a, 5), ts_delay(fnd28_fsa1_value_02051a, 5))), 252), industry)) + +reverse(group_neutralize(ts_rank(ts_covariance(subtract(divide(fnd28_fsa1_value_01706a, fnd28_bsasseta_value_02098a), divide(fnd28_fsa1_value_02001a, fnd28_bsasseta_value_02051a)), ts_step(1), 126), 252), industry)) + +reverse(group_neutralize(ts_zscore(ts_sum(subtract(divide(fnd28_ishta_value_01101a, fnd28_ishta_value_01250a), divide(fnd28_fsa1_value_03054a, fnd28_fsa1_value_02051a)), 42), 63), industry)) + +reverse(group_neutralize(ts_rank(ts_decay_linear(subtract(divide(fnd28_fsa1_value_01706a, fnd28_bsasseta_value_02098a), divide(fnd28_fsa1_value_02001a, fnd28_bsasseta_value_02051a)), 63), 126), industry)) + +reverse(group_neutralize(ts_av_diff(subtract(ts_mean(divide(fnd28_ishta_value_01101a, fnd28_ishta_value_01250a), 63), ts_mean(divide(fnd28_fsa1_value_03054a, fnd28_fsa1_value_02051a), 63)), 252), industry)) + +reverse(group_neutralize(ts_corr(ts_delta(divide(fnd28_fsa1_value_01706a, fnd28_bsasseta_value_02098a), 42), ts_delta(divide(fnd28_fsa1_value_02001a, fnd28_bsasseta_value_02051a), 42), 252), industry)) + +reverse(group_neutralize(ts_rank(subtract(ts_delta(divide(fnd28_ishta_value_01101a, fnd28_ishta_value_01250a), 63), ts_delta(divide(fnd28_fsa1_value_03054a, fnd28_fsa1_value_02051a), 63)), 252), industry)) + +reverse(group_neutralize(ts_sum(ts_zscore(subtract(divide(ts_delay(fnd28_fsa1_value_01706a, 10), ts_delay(fnd28_statisticsa_value_07230a, 10)), divide(ts_delay(fnd28_fsa1_value_02001a, 10), ts_delay(fnd28_bsasseta_value_02051a, 10))), 63), 21), industry)) + +reverse(group_neutralize(ts_regression(subtract(divide(fnd28_ishta_value_01101a, fnd28_ishta_value_01250a), divide(fnd28_fsa1_value_03054a, fnd28_fsa1_value_02051a)), ts_step(1), 126, 0, 2), industry)) + +reverse(group_neutralize(ts_rank(ts_av_diff(subtract(divide(fnd28_fsa1_value_01706a, fnd28_bsasseta_value_02098a), divide(fnd28_fsa1_value_02001a, fnd28_bsasseta_value_02051a)), 252), 252), industry)) + +reverse(group_neutralize(ts_zscore(ts_delta(subtract(divide(fnd28_ishta_value_01101a, fnd28_ishta_value_01250a), divide(fnd28_fsa1_value_03054a, fnd28_fsa1_value_02051a)), 42), 63), industry)) + +reverse(group_neutralize(ts_covariance(subtract(divide(ts_delay(fnd28_fsa1_value_01706a, 21), ts_delay(fnd28_statisticsa_value_07230a, 21)), divide(ts_delay(fnd28_fsa1_value_02001a, 21), ts_delay(fnd28_bsasseta_value_02051a, 21))), ts_step(1), 252), industry)) + +reverse(group_neutralize(ts_rank(ts_sum(subtract(divide(fnd28_ishta_value_01101a, fnd28_ishta_value_01250a), divide(fnd28_fsa1_value_03054a, fnd28_fsa1_value_02051a)), 105), 252), industry)) + +reverse(group_neutralize(ts_decay_linear(subtract(divide(fnd28_fsa1_value_01706a, fnd28_bsasseta_value_02098a), divide(fnd28_fsa1_value_02001a, fnd28_bsasseta_value_02051a)), 21), industry)) + +reverse(group_neutralize(ts_rank(ts_zscore(subtract(divide(fnd28_ishta_value_01101a, fnd28_ishta_value_01250a), divide(fnd28_fsa1_value_03054a, fnd28_fsa1_value_02051a)), 126), 252), industry)) diff --git a/generated_alpha/2026/01/27/Pro_deepseek-ai_DeepSeek-V3.1-Terminus_103743.txt b/generated_alpha/2026/01/27/Pro_deepseek-ai_DeepSeek-V3.1-Terminus_103743.txt new file mode 100644 index 0000000..6ce2bcc --- /dev/null +++ b/generated_alpha/2026/01/27/Pro_deepseek-ai_DeepSeek-V3.1-Terminus_103743.txt @@ -0,0 +1,97 @@ +ts_mean(anl14_median_eps_fy1, 20) + +rank(anl14_median_eps_fy1) + +divide(anl14_median_eps_fy1, anl14_median_bvps_fy1) + +ts_delta(anl14_median_revenue_fy1, 10) + +add(anl14_median_eps_fy1, anl14_median_roe_fy1) + +multiply(anl14_median_eps_fy1, quarterly_pe_ratio) + +ts_rank(anl14_median_roe_fy1, 30) + +zscore(quarterly_pe_ratio) + +normalize(anl14_median_revenue_fy1) + +ts_zscore(anl14_median_eps_fy1, 20) + +abs(ts_delta(anl14_median_eps_fy1, 5)) + +log(anl14_median_revenue_fy1) + +sqrt(anl14_median_eps_fy1) + +ts_sum(anl14_median_eps_fy1, 20) + +ts_std_dev(anl14_median_eps_fy1, 20) + +ts_min(anl14_median_eps_fy1, 20) + +ts_max(anl14_median_eps_fy1, 20) + +ts_decay_linear(anl14_median_eps_fy1, 20) + +ts_corr(anl14_median_eps_fy1, anl14_median_revenue_fy1, 20) + +if_else(anl14_median_eps_fy1 > 0, anl14_median_eps_fy1, 0) + +subtract(anl14_median_eps_fy1, anl14_median_eps_fy2) + +divide(ts_mean(anl14_median_eps_fy1, 20), ts_mean(anl14_median_eps_fy2, 20)) + +rank(ts_delta(anl14_median_roe_fy1, 10)) + +ts_backfill(anl14_median_eps_fy1, 10) + +ts_count_nans(anl14_median_eps_fy1, 20) + +ts_covariance(anl14_median_eps_fy1, anl14_median_revenue_fy1, 20) + +ts_product(anl14_median_eps_fy1, 5) + +ts_quantile(anl14_median_eps_fy1, 20, "gaussian") + +ts_regression(anl14_median_eps_fy1, anl14_median_revenue_fy1, 20, 0, 0) + +ts_scale(anl14_median_eps_fy1, 20, 0) + +ts_step(1) + +signed_power(anl14_median_eps_fy1, 0.5) + +inverse(anl14_median_eps_fy1) + +hump(anl14_median_eps_fy1, 0.01) + +days_from_last_change(anl14_median_eps_fy1) + +kth_element(anl14_median_eps_fy1, 20, 1) + +last_diff_value(anl14_median_eps_fy1, 20) + +ts_arg_max(anl14_median_eps_fy1, 20) + +ts_arg_min(anl14_median_eps_fy1, 20) + +ts_av_diff(anl14_median_eps_fy1, 20) + +normalize(anl14_median_eps_fy1, true) + +quantile(anl14_median_eps_fy1, "gaussian", 1.0) + +scale(anl14_median_eps_fy1, 1, 1, 1) + +winsorize(anl14_median_eps_fy1, 4) + +bucket(rank(anl14_median_eps_fy1), "0,1,0.1") + +trade_when(anl14_median_eps_fy1 > 0, anl14_median_eps_fy1, 0) + +ts_mean(quarterly_pe_ratio, 30) + +rank(fnd17_acurratio) + +divide(quarterly_revenue_fy1, quarterly_pe_ratio) \ No newline at end of file diff --git a/generated_alpha/2026/01/27/Pro_deepseek-ai_DeepSeek-V3.1-Terminus_110404.txt b/generated_alpha/2026/01/27/Pro_deepseek-ai_DeepSeek-V3.1-Terminus_110404.txt new file mode 100644 index 0000000..919ef5b --- /dev/null +++ b/generated_alpha/2026/01/27/Pro_deepseek-ai_DeepSeek-V3.1-Terminus_110404.txt @@ -0,0 +1,167 @@ +rank(anl14_high_eps_fy1) + +zscore(anl14_low_eps_fy1) + +group_neutralize(rank(anl14_median_eps_fy1), industry) + +ts_rank(ts_delta(quarterly_ebitda, 1), 4) + +divide(anl14_high_eps_fy1, anl14_low_eps_fy1) + +multiply(rank(anl14_high_eps_fy1), rank(anl14_low_eps_fy1)) + +subtract(anl14_high_eps_fy1, anl14_low_eps_fy1) + +if_else(anl14_high_eps_fy1 > anl14_low_eps_fy1, 1, -1) + +ts_mean(quarterly_ebitda, 4) + +ts_std_dev(quarterly_ebitda, 4) + +ts_zscore(quarterly_ebitda, 4) + +group_rank(anl14_high_eps_fy1, industry) + +scale(anl14_high_eps_fy1) + +winsorize(anl14_high_eps_fy1, std=4) + +normalize(anl14_high_eps_fy1) + +quantile(anl14_high_eps_fy1, driver="gaussian") + +ts_corr(quarterly_ebitda, quarterly_revenue, 4) + +ts_covariance(quarterly_ebitda, quarterly_revenue, 4) + +ts_decay_linear(quarterly_ebitda, 4) + +ts_delay(quarterly_ebitda, 1) + +ts_delta(quarterly_ebitda, 1) + +ts_product(quarterly_ebitda, 4) + +ts_sum(quarterly_ebitda, 4) + +ts_av_diff(quarterly_ebitda, 4) + +ts_count_nans(quarterly_ebitda, 4) + +ts_arg_max(quarterly_ebitda, 4) + +ts_arg_min(quarterly_ebitda, 4) + +ts_backfill(quarterly_ebitda, lookback=4) + +ts_regression(quarterly_ebitda, quarterly_revenue, 4, rettype=0) + +ts_scale(quarterly_ebitda, 4) + +ts_quantile(quarterly_ebitda, 4) + +days_from_last_change(anl14_high_eps_fy1) + +hump(anl14_high_eps_fy1, hump=0.01) + +kth_element(anl14_high_eps_fy1, 4, 1) + +last_diff_value(anl14_high_eps_fy1, 4) + +bucket(rank(anl14_high_eps_fy1), range="0,1,0.1") + +trade_when(anl14_high_eps_fy1 > 0, 1, 0) + +group_backfill(anl14_high_eps_fy1, industry, 4) + +group_mean(anl14_high_eps_fy1, fnd28_value_08003, industry) + +group_scale(anl14_high_eps_fy1, industry) + +group_zscore(anl14_high_eps_fy1, industry) + +self_corr(anl14_high_eps_fy1) + +universe_size + +rank(ts_delta(quarterly_revenue, 1)) + +group_neutralize(zscore(annual_book_value_per_share_2), industry) + +divide(quarterly_ebitda, quarterly_total_assets) + +ts_rank(divide(quarterly_ebitda, quarterly_total_assets), 4) + +if_else(quarterly_return_on_equity_percent_3 > 10, 1, -1) + +ts_rank(ts_delta(anl14_high_nav_fy1, 5), 20) + +divide(anl14_median_bvps_fy4, anl14_low_bvps_fp5) + +multiply(ts_mean(quarterly_cash_flow_per_share, 4), rank(anl14_high_roe_fp3)) + +subtract(anl14_high_ptp_fp2, anl14_low_ptp_fy1) + +if_else(anl14_median_ntp_fp1 > anl14_median_ntp_fy5, 1, -1) + +group_neutralize(ts_zscore(quarterly_long_term_debt_to_equity_ratio, 4), industry) + +ts_corr(quarterly_free_cash_flow, quarterly_total_assets, 4) + +ts_decay_linear(quarterly_operating_margin_percent, 4) + +ts_delay(quarterly_pretax_margin_percent_2, 1) + +ts_delta(quarterly_reinvestment_rate_2, 1) + +ts_sum(quarterly_sga_to_sales_ratio, 4) + +ts_av_diff(quarterly_price_to_sales_ratio, 4) + +ts_count_nans(quarterly_ebitda, 4) + +ts_arg_max(quarterly_total_debt, 4) + +ts_arg_min(quarterly_current_ratio_alt, 4) + +ts_backfill(quarterly_cash_and_equivalents, lookback=4) + +ts_regression(quarterly_eps_incl_extraordinary, quarterly_revenue_per_share, 4, rettype=0) + +ts_scale(quarterly_inventory_turnover, 4) + +ts_quantile(quarterly_receivables_turnover, 4) + +days_from_last_change(anl14_low_capex_fy5) + +hump(anl14_median_ebitda_fp2, hump=0.01) + +kth_element(anl14_high_div_fy2, 4, 1) + +last_diff_value(anl14_low_roa_fy4, 4) + +bucket(rank(anl14_median_roe_fp2), range="0,1,0.1") + +trade_when(anl14_high_epsrep_fy3 > 0, 1, 0) + +group_backfill(anl14_low_ntprep_fy5, industry, 4) + +group_mean(anl14_median_capex_fy2, fnd28_value_08003, industry) + +group_scale(anl14_high_ebitda_fy4, industry) + +group_zscore(anl14_low_cfps_fy3, industry) + +self_corr(anl14_median_div_fp1) + +universe_size + +rank(ts_delta(quarterly_total_assets_change_percent, 1)) + +group_neutralize(zscore(annual_debt_to_total_capital), industry) + +divide(quarterly_net_income_value, quarterly_total_shareholder_equity_2) + +ts_rank(divide(quarterly_effective_tax_rate, quarterly_dividend_payout_ratio), 4) + +if_else(quarterly_return_on_investment > 5, 1, -1) \ No newline at end of file diff --git a/generated_alpha/2026/01/27/Pro_moonshotai_Kimi-K2-Thinking_104536.txt b/generated_alpha/2026/01/27/Pro_moonshotai_Kimi-K2-Thinking_104536.txt new file mode 100644 index 0000000..0fff3dc --- /dev/null +++ b/generated_alpha/2026/01/27/Pro_moonshotai_Kimi-K2-Thinking_104536.txt @@ -0,0 +1,99 @@ +ts_zscore(group_neutralize(reverse(anl10_ebspast_det_estage_6210), industry), 504) + +ts_zscore(group_neutralize(reverse(anl10_cpspast_det_estage_6349), industry), 504) + +ts_zscore(group_neutralize(reverse(anl10_dpspast_det_estage_6223), industry), 504) + +ts_zscore(group_neutralize(reverse(anl10_roapast_det_estage_6330), industry), 504) + +ts_zscore(group_neutralize(reverse(anl10_fcfpast_det_estage_6380), industry), 504) + +ts_zscore(group_neutralize(divide(fnd28_ratesq_value_08601q, add(fnd28_growthratesa_value_08601a, 0.01)), industry), 504) + +ts_zscore(group_neutralize(divide(fnd28_ratesq_value_08631q, add(fnd28_growthratesa_value_08631a, 0.01)), industry), 504) + +ts_zscore(group_neutralize(divide(fnd28_ratesq_value_08636q, add(fnd28_growthratesa_value_08636a, 0.01)), industry), 504) + +ts_zscore(group_neutralize(divide(fnd28_ratesq_value_08606q, add(fnd28_growthratesa_value_08606a, 0.01)), industry), 504) + +ts_zscore(group_neutralize(divide(quarterly_current_ratio, add(fnd17_acurratio, 0.01)), industry), 504) + +ts_zscore(group_neutralize(divide(quarterly_payout_ratio, add(fnd17_apayratio, 0.01)), industry), 504) + +ts_zscore(group_neutralize(divide(quarterly_tax_rate_percent, add(fnd17_ataxrate, 0.01)), industry), 504) + +ts_zscore(group_neutralize(ts_delta(anl14_median_eps_fp1, 63), industry), 504) + +ts_zscore(group_neutralize(ts_delta(anl14_median_revenue_fp1, 63), industry), 504) + +ts_zscore(group_neutralize(ts_delta(anl14_median_ntp_fp1, 63), industry), 504) + +ts_zscore(group_neutralize(ts_delta(anl14_median_roe_fp1, 63), industry), 504) + +ts_zscore(group_neutralize(reverse(vec_avg(anl10_ebspast_det_estage_6210, anl10_cpspast_det_estage_6349, anl10_roapast_det_estage_6330)), industry), 504) + +ts_zscore(group_neutralize(vec_avg(divide(fnd28_ratesq_value_08601q, add(fnd28_growthratesa_value_08601a, 0.01)), divide(fnd28_ratesq_value_08631q, add(fnd28_growthratesa_value_08631a, 0.01))), industry), 504) + +ts_zscore(group_neutralize(divide(quarterly_pe_ratio_low, add(quarterly_pe_ratio_high, 0.01)), industry), 504) + +ts_zscore(group_neutralize(divide(quarterly_price_to_sales_ratio, add(quarterly_price_to_sales_ratio_2, 0.01)), industry), 504) + +ts_zscore(group_neutralize(reverse(anl10_entpast_det_estage_6314), industry), 504) + +ts_zscore(group_neutralize(reverse(anl10_grmpast_det_estage_6320), industry), 504) + +ts_zscore(group_neutralize(reverse(anl10_bpspast_det_estage_6399), industry), 504) + +ts_zscore(group_neutralize(divide(fnd28_ratesq_value_08651q, add(fnd28_growthratesa_value_08650a, 0.01)), industry), 504) + +ts_zscore(group_neutralize(divide(fnd28_ratesq_value_08616q, add(fnd28_growthratesa_value_08616a, 0.01)), industry), 504) + +ts_zscore(group_neutralize(divide(fnd28_ratesq_value_08611q, add(fnd28_growthratesa_value_08611a, 0.01)), industry), 504) + +ts_zscore(group_neutralize(divide(quarterly_debt_to_equity_ratio_2, add(quarterly_long_term_debt_to_equity_ratio, 0.01)), industry), 504) + +ts_zscore(group_neutralize(divide(quarterly_return_on_average_equity, add(ttm_return_on_average_equity, 0.01)), industry), 504) + +ts_zscore(group_neutralize(divide(quarterly_return_on_average_assets, add(ts_delay(quarterly_return_on_average_assets, 252), 0.01)), industry), 504) + +ts_zscore(group_neutralize(ts_delta(ts_delta(anl14_median_eps_fp1, 63), 63), industry), 504) + +ts_zscore(group_neutralize(ts_delta(ts_delta(anl14_median_revenue_fp1, 63), 63), industry), 504) + +ts_zscore(group_neutralize(vec_avg(ts_delta(anl14_median_eps_fp1, 63), ts_delta(anl14_median_revenue_fp1, 63)), industry), 504) + +ts_zscore(group_neutralize(divide(quarterly_quick_ratio, add(quarterly_quick_ratio_2, 0.01)), industry), 504) + +ts_zscore(group_neutralize(divide(quarterly_debt_to_assets_ratio, add(quarterly_total_debt_to_assets_ratio, 0.01)), industry), 504) + +ts_zscore(group_neutralize(divide(quarterly_interest_coverage, add(quarterly_interest_coverage_ratio, 0.01)), industry), 504) + +ts_zscore(group_neutralize(divide(quarterly_sga_to_revenue_ratio, add(quarterly_sga_to_sales_ratio, 0.01)), industry), 504) + +ts_zscore(group_neutralize(divide(quarterly_inventory_turnover_ratio, add(ts_delay(quarterly_inventory_turnover_ratio, 252), 0.01)), industry), 504) + +ts_zscore(group_neutralize(divide(quarterly_reinvestment_rate, add(quarterly_reinvestment_rate_2, 0.01)), industry), 504) + +ts_zscore(group_neutralize(divide(quarterly_effective_tax_rate, add(quarterly_tax_rate_percent, 0.01)), industry), 504) + +ts_zscore(group_neutralize(reverse(vec_avg(anl14_median_eps_fy1, anl14_median_revenue_fy1)), industry), 504) + +ts_zscore(group_neutralize(ts_mean(ts_delta(anl14_median_eps_fp1, 63), 126), industry), 504) + +ts_zscore(group_neutralize(ts_mean(ts_delta(anl14_median_revenue_fp1, 63), 126), industry), 504) + +ts_zscore(group_neutralize(divide(fnd28_ratesq_value_08401q, add(fnd28_growthratesa_value_08401a, 0.01)), industry), 504) + +ts_zscore(group_neutralize(divide(fnd28_ratesq_value_08366q, add(fnd28_growthratesa_value_08366a, 0.01)), industry), 504) + +ts_zscore(group_neutralize(divide(fnd28_ratesq_value_08316q, add(fnd28_growthratesa_value_08316a, 0.01)), industry), 504) + +ts_zscore(group_neutralize(divide(fnd28_ratesq_value_08321q, add(fnd28_growthratesa_value_08321a, 0.01)), industry), 504) + +ts_zscore(group_neutralize(vec_avg(divide(quarterly_current_ratio, add(fnd17_acurratio, 0.01)), divide(quarterly_quick_ratio, add(fnd17_qcurratio, 0.01))), industry), 504) + +ts_zscore(group_neutralize(vec_avg(divide(quarterly_return_on_average_equity, add(ttm_return_on_average_equity, 0.01)), divide(quarterly_return_on_average_assets, add(ts_delay(quarterly_return_on_average_assets, 252), 0.01))), industry), 504) + +ts_zscore(group_neutralize(ts_std_dev(reverse(anl10_ebspast_det_estage_6210), 252), industry), 504) + +ts_zscore(group_neutralize(vec_avg(reverse(anl10_ebspast_det_estage_6210), divide(fnd28_ratesq_value_08601q, add(fnd28_growthratesa_value_08601a, 0.01)), ts_delta(anl14_median_eps_fp1, 63)), industry), 504) \ No newline at end of file diff --git a/generated_alpha/2026/01/27/Pro_moonshotai_Kimi-K2-Thinking_111154.txt b/generated_alpha/2026/01/27/Pro_moonshotai_Kimi-K2-Thinking_111154.txt new file mode 100644 index 0000000..8152fe0 --- /dev/null +++ b/generated_alpha/2026/01/27/Pro_moonshotai_Kimi-K2-Thinking_111154.txt @@ -0,0 +1,95 @@ +group_neutralize(subtract(ts_delta(benefit_expense_total, 252), ts_delta(annual_base_salary, 252)), industry) + +group_neutralize(subtract(ts_delta(benefit_expense_total, 63), ts_delta(annual_base_salary, 63)), industry) + +group_neutralize(subtract(ts_delta(benefit_expense_total, 126), ts_delta(annual_base_salary, 126)), industry) + +group_neutralize(subtract(ts_delta(benefit_expense_total, 504), ts_delta(annual_base_salary, 504)), industry) + +group_neutralize(subtract(ts_delta(benefit_expense_total, 252), ts_delta(bonus_proportion_total_comp, 252)), industry) + +group_neutralize(subtract(ts_delta(benefit_expense_total, 252), ts_delta(estimated_value_exercisable_options, 252)), industry) + +group_neutralize(subtract(ts_delta(benefit_expense_total, 252), ts_delta(share_award_value_ltip, 252)), industry) + +group_neutralize(subtract(ts_delta(benefit_expense_total, 252), ts_delta(intrinsic_value_options_awarded_2, 252)), industry) + +group_neutralize(subtract(ts_delta(fnd28_ishtspmnt_value_01084a, 252), ts_delta(annual_base_salary, 252)), industry) + +group_neutralize(subtract(ts_delta(benefit_expense_total, 252), divide(add(ts_delta(annual_base_salary, 252), ts_delta(bonus_proportion_total_comp, 252), ts_delta(estimated_value_exercisable_options, 252)), 3)), industry) + +winsorize(group_neutralize(subtract(ts_delta(benefit_expense_total, 252), ts_delta(annual_base_salary, 252)), industry), std=3) + +zscore(group_neutralize(subtract(ts_delta(benefit_expense_total, 252), ts_delta(annual_base_salary, 252)), industry)) + +rank(group_neutralize(subtract(ts_delta(benefit_expense_total, 252), ts_delta(annual_base_salary, 252)), industry)) + +quantile(group_neutralize(subtract(ts_delta(benefit_expense_total, 252), ts_delta(annual_base_salary, 252)), industry)) + +scale(group_neutralize(subtract(ts_delta(benefit_expense_total, 252), ts_delta(annual_base_salary, 252)), industry)) + +scale_down(group_neutralize(subtract(ts_delta(benefit_expense_total, 252), ts_delta(annual_base_salary, 252)), industry)) + +group_rank(subtract(ts_delta(benefit_expense_total, 252), ts_delta(annual_base_salary, 252)), industry) + +group_zscore(subtract(ts_delta(benefit_expense_total, 252), ts_delta(annual_base_salary, 252)), industry) + +group_scale(subtract(ts_delta(benefit_expense_total, 252), ts_delta(annual_base_salary, 252)), industry) + +group_neutralize(subtract(ts_delta(benefit_expense_total, 252), ts_delta(annual_base_salary, 252)), industry_grouping_level2_top3000_2) + +group_neutralize(subtract(ts_delta(benefit_expense_total, 252), ts_delta(annual_base_salary, 252)), pca_industry_grouping_method4_20) + +multiply(group_neutralize(subtract(ts_delta(benefit_expense_total, 252), ts_delta(annual_base_salary, 252)), industry), ts_delta(research_and_development_expense, 252)) + +multiply(group_neutralize(subtract(ts_delta(benefit_expense_total, 252), ts_delta(annual_base_salary, 252)), industry), rank(fnd28_value_08003)) + +multiply(group_neutralize(subtract(ts_delta(benefit_expense_total, 252), ts_delta(annual_base_salary, 252)), industry), ts_delta(ttm_return_on_equity_percent_2, 252)) + +add(group_neutralize(subtract(ts_delta(benefit_expense_total, 252), ts_delta(annual_base_salary, 252)), industry), ts_delta(fnd28_value_08601, 252)) + +if_else(ts_delta(benefit_expense_total, 252) > ts_delta(annual_base_salary, 252), group_neutralize(subtract(ts_delta(benefit_expense_total, 252), ts_delta(annual_base_salary, 252)), industry), 0) + +sign(group_neutralize(subtract(ts_delta(benefit_expense_total, 252), ts_delta(annual_base_salary, 252)), industry)) + +abs(group_neutralize(subtract(ts_delta(benefit_expense_total, 252), ts_delta(annual_base_salary, 252)), industry)) + +power(group_neutralize(subtract(ts_delta(benefit_expense_total, 252), ts_delta(annual_base_salary, 252)), industry), 2) + +signed_power(group_neutralize(subtract(ts_delta(benefit_expense_total, 252), ts_delta(annual_base_salary, 252)), industry), 0.5) + +log(abs(group_neutralize(subtract(ts_delta(benefit_expense_total, 252), ts_delta(annual_base_salary, 252)), industry))) + +sqrt(abs(group_neutralize(subtract(ts_delta(benefit_expense_total, 252), ts_delta(annual_base_salary, 252)), industry))) + +min(group_neutralize(subtract(ts_delta(benefit_expense_total, 252), ts_delta(annual_base_salary, 252)), industry), ts_delta(research_and_development_expense, 252)) + +max(group_neutralize(subtract(ts_delta(benefit_expense_total, 252), ts_delta(annual_base_salary, 252)), industry), ts_delta(research_and_development_expense, 252)) + +divide(group_neutralize(subtract(ts_delta(benefit_expense_total, 252), ts_delta(annual_base_salary, 252)), industry), ts_std_dev(benefit_expense_total, 252)) + +inverse(group_neutralize(subtract(ts_delta(benefit_expense_total, 252), ts_delta(annual_base_salary, 252)), industry)) + +vec_sum(group_neutralize(subtract(ts_delta(benefit_expense_total, 252), ts_delta(annual_base_salary, 252)), industry)) + +vec_avg(group_neutralize(subtract(ts_delta(benefit_expense_total, 252), ts_delta(annual_base_salary, 252)), industry)) + +trade_when(ts_delta(benefit_expense_total, 252) > 0, group_neutralize(subtract(ts_delta(benefit_expense_total, 252), ts_delta(annual_base_salary, 252)), industry), 0) + +hump(group_neutralize(subtract(ts_delta(benefit_expense_total, 252), ts_delta(annual_base_salary, 252)), industry), 0.05) + +days_from_last_change(group_neutralize(subtract(ts_delta(benefit_expense_total, 252), ts_delta(annual_base_salary, 252)), industry)) + +last_diff_value(group_neutralize(subtract(ts_delta(benefit_expense_total, 252), ts_delta(annual_base_salary, 252)), industry), 252) + +ts_arg_max(group_neutralize(subtract(ts_delta(benefit_expense_total, 252), ts_delta(annual_base_salary, 252)), industry), 252) + +ts_arg_min(group_neutralize(subtract(ts_delta(benefit_expense_total, 252), ts_delta(annual_base_salary, 252)), industry), 252) + +ts_quantile(group_neutralize(subtract(ts_delta(benefit_expense_total, 252), ts_delta(annual_base_salary, 252)), industry), 252) + +ts_regression(group_neutralize(subtract(ts_delta(benefit_expense_total, 252), ts_delta(annual_base_salary, 252)), industry), ts_delta(research_and_development_expense, 252), 252) + +ts_product(group_neutralize(subtract(ts_delta(benefit_expense_total, 252), ts_delta(annual_base_salary, 252)), industry), 252) + +combo_a(group_neutralize(subtract(ts_delta(benefit_expense_total, 252), ts_delta(annual_base_salary, 252)), industry), nlength=250, mode='algo1') \ No newline at end of file diff --git a/generated_alpha/2026/01/27/Qwen_Qwen3-VL-235B-A22B-Instruct_103926.txt b/generated_alpha/2026/01/27/Qwen_Qwen3-VL-235B-A22B-Instruct_103926.txt new file mode 100644 index 0000000..85ced5a --- /dev/null +++ b/generated_alpha/2026/01/27/Qwen_Qwen3-VL-235B-A22B-Instruct_103926.txt @@ -0,0 +1,99 @@ +ts_zscore(divide(ts_sum(add(ts_delay(fnd28_ratesq_value_08601q, 3), ts_delay(fnd28_ratesq_value_08601q, 6)), 12), ts_sum(fnd28_ratesq_value_08601q, 12)), 24) + +group_zscore(ts_rank(fnd28_ratesq_value_08306q, 24), fnd17_qcurratio) + +ts_scale(subtract(fnd28_ratesq_value_08326q, ts_mean(fnd28_ratesq_value_08326q, 12)), 24) + +group_neutralize(ts_delta(fnd28_ratesq_value_08631q, 3), fnd17_qcurratio) + +ts_mean(ts_delta(fnd28_ratesq_value_08366q, 6), 12) + +group_rank(ts_zscore(fnd28_ratesq_value_08301q, 24), fnd17_qcurratio) + +ts_std_dev(ts_delta(fnd28_ratesq_value_08606q, 3), 12) + +group_scale(ts_rank(fnd28_ratesq_value_08316q, 24), fnd17_qcurratio) + +ts_av_diff(fnd28_ratesq_value_08321q, 12) + +group_backfill(ts_delta(fnd28_ratesq_value_08611q, 6), fnd17_qcurratio, 24) + +ts_corr(fnd28_ratesq_value_08601q, fnd28_ratesq_value_08631q, 12) + +ts_quantile(ts_delta(fnd28_ratesq_value_08311q, 3), 24) + +group_mean(ts_zscore(fnd28_ratesq_value_08346q, 24), fnd17_qcurratio, fnd17_qcurratio) + +ts_regression(fnd28_ratesq_value_08606q, fnd28_ratesq_value_08636q, 12, 0, 0) + +ts_decay_linear(fnd28_ratesq_value_08306q, 12) + +ts_product(ts_delta(fnd28_ratesq_value_08616q, 6), 3) + +group_zscore(ts_mean(fnd28_ratesq_value_08326q, 12), fnd17_qcurratio) + +ts_rank(ts_delta(fnd28_ratesq_value_08621q, 3), 24) + +group_neutralize(ts_std_dev(fnd28_ratesq_value_08366q, 12), fnd17_qcurratio) + +ts_backfill(ts_delta(fnd28_ratesq_value_08636q, 6), 24, 1) + +ts_covariance(fnd28_ratesq_value_08601q, fnd28_ratesq_value_08631q, 12) + +ts_count_nans(fnd28_ratesq_value_08301q, 12) + +group_scale(ts_delta(fnd28_ratesq_value_08316q, 3), fnd17_qcurratio) + +ts_arg_max(fnd28_ratesq_value_08606q, 12) + +ts_arg_min(fnd28_ratesq_value_08611q, 12) + +group_rank(ts_delta(fnd28_ratesq_value_08321q, 6), fnd17_qcurratio) + +ts_zscore(ts_mean(fnd28_ratesq_value_08346q, 12), 24) + +group_zscore(ts_delta(fnd28_ratesq_value_08621q, 3), fnd17_qcurratio) + +ts_scale(ts_delta(fnd28_ratesq_value_08631q, 6), 24) + +group_neutralize(ts_rank(fnd28_ratesq_value_08306q, 12), fnd17_qcurratio) + +ts_av_diff(ts_delta(fnd28_ratesq_value_08601q, 3), 12) + +ts_quantile(fnd28_ratesq_value_08326q, 24) + +group_mean(ts_delta(fnd28_ratesq_value_08316q, 6), fnd17_qcurratio, fnd17_qcurratio) + +ts_regression(fnd28_ratesq_value_08636q, fnd28_ratesq_value_08601q, 12, 0, 0) + +ts_decay_linear(ts_delta(fnd28_ratesq_value_08366q, 3), 12) + +ts_product(fnd28_ratesq_value_08606q, 3) + +group_zscore(ts_mean(fnd28_ratesq_value_08321q, 12), fnd17_qcurratio) + +ts_rank(ts_delta(fnd28_ratesq_value_08611q, 6), 24) + +group_neutralize(ts_std_dev(fnd28_ratesq_value_08301q, 12), fnd17_qcurratio) + +ts_backfill(ts_delta(fnd28_ratesq_value_08636q, 3), 24, 1) + +ts_covariance(fnd28_ratesq_value_08606q, fnd28_ratesq_value_08631q, 12) + +ts_count_nans(fnd28_ratesq_value_08326q, 12) + +group_scale(ts_delta(fnd28_ratesq_value_08346q, 3), fnd17_qcurratio) + +ts_arg_max(fnd28_ratesq_value_08631q, 12) + +ts_arg_min(fnd28_ratesq_value_08601q, 12) + +group_rank(ts_delta(fnd28_ratesq_value_08316q, 6), fnd17_qcurratio) + +ts_zscore(ts_mean(fnd28_ratesq_value_08606q, 12), 24) + +group_zscore(ts_delta(fnd28_ratesq_value_08321q, 3), fnd17_qcurratio) + +ts_scale(ts_delta(fnd28_ratesq_value_08611q, 6), 24) + +group_neutralize(ts_rank(fnd28_ratesq_value_08636q, 12), fnd17_qcurratio) \ No newline at end of file diff --git a/generated_alpha/2026/01/27/Qwen_Qwen3-VL-235B-A22B-Instruct_110727.txt b/generated_alpha/2026/01/27/Qwen_Qwen3-VL-235B-A22B-Instruct_110727.txt new file mode 100644 index 0000000..6c608eb --- /dev/null +++ b/generated_alpha/2026/01/27/Qwen_Qwen3-VL-235B-A22B-Instruct_110727.txt @@ -0,0 +1,133 @@ +reverse(subtract(ts_delta(fnd28_cfsourceusea_value_04001a, 252), ts_delta(divide(fnd28_cfsourceusea_value_04001a, fnd28_nddq1_value_05301q), 252))) + +group_zscore(reverse(subtract(ts_delta(fnd28_cfsourceusea_value_04001a, 252), ts_delta(divide(fnd28_cfsourceusea_value_04001a, fnd28_nddq1_value_05301q), 252))), industry) + +normalize(reverse(subtract(ts_delta(fnd28_cfsourceusea_value_04001a, 252), ts_delta(divide(fnd28_cfsourceusea_value_04001a, fnd28_nddq1_value_05301q), 252))), useStd=true) + +rank(reverse(subtract(ts_delta(fnd28_cfsourceusea_value_04001a, 252), ts_delta(divide(fnd28_cfsourceusea_value_04001a, fnd28_nddq1_value_05301q), 252)))) + +zscore(reverse(subtract(ts_delta(fnd28_cfsourceusea_value_04001a, 252), ts_delta(divide(fnd28_cfsourceusea_value_04001a, fnd28_nddq1_value_05301q), 252)))) + +ts_zscore(reverse(subtract(ts_delta(fnd28_cfsourceusea_value_04001a, 252), ts_delta(divide(fnd28_cfsourceusea_value_04001a, fnd28_nddq1_value_05301q), 252))), 252) + +group_neutralize(reverse(subtract(ts_delta(fnd28_cfsourceusea_value_04001a, 252), ts_delta(divide(fnd28_cfsourceusea_value_04001a, fnd28_nddq1_value_05301q), 252))), industry) + +winsorize(reverse(subtract(ts_delta(fnd28_cfsourceusea_value_04001a, 252), ts_delta(divide(fnd28_cfsourceusea_value_04001a, fnd28_nddq1_value_05301q), 252))), std=3) + +scale(reverse(subtract(ts_delta(fnd28_cfsourceusea_value_04001a, 252), ts_delta(divide(fnd28_cfsourceusea_value_04001a, fnd28_nddq1_value_05301q), 252))), scale=0.5) + +ts_scale(reverse(subtract(ts_delta(fnd28_cfsourceusea_value_04001a, 252), ts_delta(divide(fnd28_cfsourceusea_value_04001a, fnd28_nddq1_value_05301q), 252))), 252) + +ts_rank(reverse(subtract(ts_delta(fnd28_cfsourceusea_value_04001a, 252), ts_delta(divide(fnd28_cfsourceusea_value_04001a, fnd28_nddq1_value_05301q), 252))), 252) + +ts_mean(reverse(subtract(ts_delta(fnd28_cfsourceusea_value_04001a, 252), ts_delta(divide(fnd28_cfsourceusea_value_04001a, fnd28_nddq1_value_05301q), 252))), 252) + +ts_std_dev(reverse(subtract(ts_delta(fnd28_cfsourceusea_value_04001a, 252), ts_delta(divide(fnd28_cfsourceusea_value_04001a, fnd28_nddq1_value_05301q), 252))), 252) + +ts_sum(reverse(subtract(ts_delta(fnd28_cfsourceusea_value_04001a, 252), ts_delta(divide(fnd28_cfsourceusea_value_04001a, fnd28_nddq1_value_05301q), 252))), 252) + +ts_corr(fnd28_cfsourceusea_value_04001a, fnd28_nddq1_value_05301q, 252) + +ts_covariance(fnd28_cfsourceusea_value_04001a, fnd28_nddq1_value_05301q, 252) + +ts_decay_linear(reverse(subtract(ts_delta(fnd28_cfsourceusea_value_04001a, 252), ts_delta(divide(fnd28_cfsourceusea_value_04001a, fnd28_nddq1_value_05301q), 252))), 252) + +ts_backfill(reverse(subtract(ts_delta(fnd28_cfsourceusea_value_04001a, 252), ts_delta(divide(fnd28_cfsourceusea_value_04001a, fnd28_nddq1_value_05301q), 252))), 252) + +ts_arg_max(reverse(subtract(ts_delta(fnd28_cfsourceusea_value_04001a, 252), ts_delta(divide(fnd28_cfsourceusea_value_04001a, fnd28_nddq1_value_05301q), 252))), 252) + +ts_arg_min(reverse(subtract(ts_delta(fnd28_cfsourceusea_value_04001a, 252), ts_delta(divide(fnd28_cfsourceusea_value_04001a, fnd28_nddq1_value_05301q), 252))), 252) + +ts_av_diff(reverse(subtract(ts_delta(fnd28_cfsourceusea_value_04001a, 252), ts_delta(divide(fnd28_cfsourceusea_value_04001a, fnd28_nddq1_value_05301q), 252))), 252) + +ts_quantile(reverse(subtract(ts_delta(fnd28_cfsourceusea_value_04001a, 252), ts_delta(divide(fnd28_cfsourceusea_value_04001a, fnd28_nddq1_value_05301q), 252))), 252) + +ts_regression(fnd28_cfsourceusea_value_04001a, fnd28_nddq1_value_05301q, 252) + +ts_product(reverse(subtract(ts_delta(fnd28_cfsourceusea_value_04001a, 252), ts_delta(divide(fnd28_cfsourceusea_value_04001a, fnd28_nddq1_value_05301q), 252))), 252) + +ts_count_nans(reverse(subtract(ts_delta(fnd28_cfsourceusea_value_04001a, 252), ts_delta(divide(fnd28_cfsourceusea_value_04001a, fnd28_nddq1_value_05301q), 252))), 252) + +ts_delay(reverse(subtract(ts_delta(fnd28_cfsourceusea_value_04001a, 252), ts_delta(divide(fnd28_cfsourceusea_value_04001a, fnd28_nddq1_value_05301q), 252))), 252) + +ts_delta(reverse(subtract(ts_delta(fnd28_cfsourceusea_value_04001a, 252), ts_delta(divide(fnd28_cfsourceusea_value_04001a, fnd28_nddq1_value_05301q), 252))), 252) + +group_mean(reverse(subtract(ts_delta(fnd28_cfsourceusea_value_04001a, 252), ts_delta(divide(fnd28_cfsourceusea_value_04001a, fnd28_nddq1_value_05301q), 252))), fnd28_nddq1_value_05301q, industry) + +group_scale(reverse(subtract(ts_delta(fnd28_cfsourceusea_value_04001a, 252), ts_delta(divide(fnd28_cfsourceusea_value_04001a, fnd28_nddq1_value_05301q), 252))), industry) + +group_rank(reverse(subtract(ts_delta(fnd28_cfsourceusea_value_04001a, 252), ts_delta(divide(fnd28_cfsourceusea_value_04001a, fnd28_nddq1_value_05301q), 252))), industry) + +group_max(reverse(subtract(ts_delta(fnd28_cfsourceusea_value_04001a, 252), ts_delta(divide(fnd28_cfsourceusea_value_04001a, fnd28_nddq1_value_05301q), 252))), industry) + +group_min(reverse(subtract(ts_delta(fnd28_cfsourceusea_value_04001a, 252), ts_delta(divide(fnd28_cfsourceusea_value_04001a, fnd28_nddq1_value_05301q), 252))), industry) + +group_backfill(reverse(subtract(ts_delta(fnd28_cfsourceusea_value_04001a, 252), ts_delta(divide(fnd28_cfsourceusea_value_04001a, fnd28_nddq1_value_05301q), 252))), industry, 252) + +quantile(reverse(subtract(ts_delta(fnd28_cfsourceusea_value_04001a, 252), ts_delta(divide(fnd28_cfsourceusea_value_04001a, fnd28_nddq1_value_05301q), 252))), driver=gaussian) + +vec_avg(reverse(subtract(ts_delta(fnd28_cfsourceusea_value_04001a, 252), ts_delta(divide(fnd28_cfsourceusea_value_04001a, fnd28_nddq1_value_05301q), 252)))) + +vec_sum(reverse(subtract(ts_delta(fnd28_cfsourceusea_value_04001a, 252), ts_delta(divide(fnd28_cfsourceusea_value_04001a, fnd28_nddq1_value_05301q), 252)))) + +bucket(rank(reverse(subtract(ts_delta(fnd28_cfsourceusea_value_04001a, 252), ts_delta(divide(fnd28_cfsourceusea_value_04001a, fnd28_nddq1_value_05301q), 252)))), range="0,1,0.1") + +trade_when(reverse(subtract(ts_delta(fnd28_cfsourceusea_value_04001a, 252), ts_delta(divide(fnd28_cfsourceusea_value_04001a, fnd28_nddq1_value_05301q), 252))), fnd28_nddq1_value_05301q > 0, 0) + +if_else(fnd28_nddq1_value_05301q > 0, reverse(subtract(ts_delta(fnd28_cfsourceusea_value_04001a, 252), ts_delta(divide(fnd28_cfsourceusea_value_04001a, fnd28_nddq1_value_05301q), 252))), 0) + +and(fnd28_nddq1_value_05301q > 0, fnd28_cfsourceusea_value_04001a > 0) + +or(fnd28_nddq1_value_05301q > 0, fnd28_cfsourceusea_value_04001a > 0) + +not(fnd28_nddq1_value_05301q > 0) + +is_nan(reverse(subtract(ts_delta(fnd28_cfsourceusea_value_04001a, 252), ts_delta(divide(fnd28_cfsourceusea_value_04001a, fnd28_nddq1_value_05301q), 252)))) + +sign(reverse(subtract(ts_delta(fnd28_cfsourceusea_value_04001a, 252), ts_delta(divide(fnd28_cfsourceusea_value_04001a, fnd28_nddq1_value_05301q), 252)))) + +abs(reverse(subtract(ts_delta(fnd28_cfsourceusea_value_04001a, 252), ts_delta(divide(fnd28_cfsourceusea_value_04001a, fnd28_nddq1_value_05301q), 252)))) + +add(fnd28_cfsourceusea_value_04001a, fnd28_nddq1_value_05301q, filter=false) + +subtract(fnd28_cfsourceusea_value_04001a, fnd28_nddq1_value_05301q, filter=false) + +multiply(fnd28_cfsourceusea_value_04001a, fnd28_nddq1_value_05301q, filter=false) + +divide(fnd28_cfsourceusea_value_04001a, fnd28_nddq1_value_05301q) + +power(fnd28_cfsourceusea_value_04001a, 2) + +signed_power(fnd28_cfsourceusea_value_04001a, 2) + +sqrt(fnd28_cfsourceusea_value_04001a) + +log(fnd28_cfsourceusea_value_04001a) + +inverse(fnd28_cfsourceusea_value_04001a) + +reverse(fnd28_cfsourceusea_value_04001a) + +max(fnd28_cfsourceusea_value_04001a, fnd28_nddq1_value_05301q) + +min(fnd28_cfsourceusea_value_04001a, fnd28_nddq1_value_05301q) + +densify(industry) + +self_corr(reverse(subtract(ts_delta(fnd28_cfsourceusea_value_04001a, 252), ts_delta(divide(fnd28_cfsourceusea_value_04001a, fnd28_nddq1_value_05301q), 252)))) + +universe_size + +reduce_avg(reverse(subtract(ts_delta(fnd28_cfsourceusea_value_04001a, 252), ts_delta(divide(fnd28_cfsourceusea_value_04001a, fnd28_nddq1_value_05301q), 252)))) + +reduce_max(reverse(subtract(ts_delta(fnd28_cfsourceusea_value_04001a, 252), ts_delta(divide(fnd28_cfsourceusea_value_04001a, fnd28_nddq1_value_05301q), 252)))) + +reduce_min(reverse(subtract(ts_delta(fnd28_cfsourceusea_value_04001a, 252), ts_delta(divide(fnd28_cfsourceusea_value_04001a, fnd28_nddq1_value_05301q), 252)))) + +reduce_stddev(reverse(subtract(ts_delta(fnd28_cfsourceusea_value_04001a, 252), ts_delta(divide(fnd28_cfsourceusea_value_04001a, fnd28_nddq1_value_05301q), 252)))) + +reduce_sum(reverse(subtract(ts_delta(fnd28_cfsourceusea_value_04001a, 252), ts_delta(divide(fnd28_cfsourceusea_value_04001a, fnd28_nddq1_value_05301q), 252)))) + +reduce_skewness(reverse(subtract(ts_delta(fnd28_cfsourceusea_value_04001a, 252), ts_delta(divide(fnd28_cfsourceusea_value_04001a, fnd28_nddq1_value_05301q), 252)))) + +reduce_kurtosis(reverse(subtract(ts_delta(fnd28_cfsourceusea_value_04001a, 252), ts_delta(divide(fnd28_cfsourceusea_value_04001a, fnd28_nddq1_value_05301q), 25 \ No newline at end of file diff --git a/generated_alpha/2026/01/28/1.txt b/generated_alpha/2026/01/28/1.txt new file mode 100644 index 0000000..7b86cdf --- /dev/null +++ b/generated_alpha/2026/01/28/1.txt @@ -0,0 +1,384 @@ +group_neutralize(ts_zscore(divide(ts_std_dev(returns, 20), group_mean(ts_std_dev(returns, 20), industry)), 20), industry) +zscore(multiply(ts_rank(divide(daily_volume_percent_shares_out, group_mean(daily_volume_percent_shares_out, industry)), 20), reverse(ts_zscore(pv173_rawratiosbondreturn20deqwt, 20)))) +rank(add(ts_scale(anl14_high_revenue_fy2, 20), reverse(ts_scale(anl14_low_ebit_fy5, 20)))) +group_scale(multiply(ts_av_diff(anl14_high_ebit_fy2, 20), sign(ts_delta(pv173_zscoresbondreturn20deqwt, 5))), industry) +normalize(multiply(ts_quantile(anl14_low_eps_fy5, 20), inverse(ts_mean(twelve_month_high_price, 20)))) +winsorize(ts_corr(anl14_high_revenue_fp2, pv173_rawratiosmt5yzspreadchg60d, 20), 3) +ts_decay_linear(group_zscore(divide(anl14_high_ebit_fp4, anl14_low_ntprep_fp4), industry), 20) +zscore(add(ts_sum(short_term_net_cash_balance, 20), reverse(ts_sum(other_off_balance_liabilities, 20)))) +group_neutralize(multiply(ts_rank(anl14_low_ndebt_fy1, 20), ts_rank(fnd31_devnorthamericaadditionalfactor4_pbdwf, 20)), industry) +rank(divide(ts_std_dev(anl14_high_ntp_fy1, 20), ts_std_dev(resampled_pca_factor_1, 20))) +ts_zscore(multiply(group_rank(anl14_high_ntp_fy1, industry), sign(ts_delta(daily_volume_percent_shares_out, 10))), 20) +normalize(subtract(ts_mean(fnd72_pit_or_cf_q_cf_other_non_cash_adj_less, 20), ts_mean(fnd72_s_pit_or_cf_q_cf_incr_cap_stock, 20))) +group_scale(add(ts_av_diff(industry_grouping_level2_top1500, 20), ts_av_diff(anl14_low_ntprep_fp4, 20)), industry) +zscore(multiply(ts_quantile(fnd31_qsg5additionalfactor3_monthly_pcdwf, 20, "uniform"), inverse(ts_mean(anl14_low_ndebt_fy1, 20)))) +ts_regression(anl14_high_ebit_fp4, pv173_zscoresatlas_unit_name, 20, 0, 1) +group_zscore(subtract(ts_rank(anl14_high_ntp_fy1, 20), ts_rank(anl14_low_eps_fy5, 20)), industry) +rank(multiply(ts_covariance(returns, pv173_rawratiosbondreturn20deqwt, 20), sign(ts_delta(anl14_high_revenue_fy2, 5)))) +ts_backfill(group_neutralize(divide(anl14_high_nav_fy2, anl14_low_ndebt_fy5), industry), 20) +normalize(add(ts_product(anl14_high_eps_fp1, 10), ts_product(anl14_low_roe_fy1, 10))) +zscore(multiply(ts_arg_max(anl14_high_revenue_fy1, 20), ts_arg_min(anl14_low_ebit_fy4, 20))) +group_neutralize(ts_scale(multiply(anl14_high_revenue_fp1, fnd31_qsg5additionalfactor3_monthly_pcdwf), 20), industry) +rank(subtract(ts_count_nans(anl14_high_ebit_fy2, 20), ts_count_nans(anl14_low_eps_fy5, 20))) +ts_zscore(divide(group_mean(anl14_high_ntp_fy1, industry, industry), ts_mean(anl14_high_ntp_fy1, 20)), 20) +normalize(multiply(signed_power(ts_delta(returns, 5), 2), sign(ts_delta(pv173_zscoresbondreturn20deqwt, 5)))) +group_scale(add(ts_quantile(daily_volume_percent_shares_out, 20), ts_quantile(anl14_low_ndebt_fy1, 20)), industry) +zscore(ts_corr(anl14_high_ebit_fp4, daily_volume_percent_shares_out, 20)) +ts_decay_linear(subtract(ts_mean(anl14_high_ntp_fy1, 20), group_mean(anl14_high_ntp_fy1, industry, industry)), 20) +rank(multiply(ts_std_dev(anl14_high_revenue_fy2, 20), inverse(ts_std_dev(anl14_low_ebit_fy5, 20)))) +group_neutralize(ts_av_diff(multiply(anl14_high_eps_fp1, fnd31_devnorthamericaadditionalfactor4_pbdwf), 20), industry) +normalize(add(ts_rank(anl14_high_revenue_fp1, 20), reverse(ts_rank(anl14_low_ntprep_fp4, 20)))) +zscore(divide(ts_sum(anl14_high_ebit_fy2, 20), ts_sum(anl14_low_eps_fy5, 20))) +ts_zscore(group_rank(multiply(ts_delay(returns, 5), daily_volume_percent_shares_out), industry), 20) +group_scale(multiply(ts_arg_min(anl14_high_revenue_fy1, 20), sign(ts_delta(anl14_low_ebit_fy4, 5))), industry) +rank(subtract(ts_quantile(anl14_high_ebit_fp4, 20), ts_quantile(pv173_rawratiosmt5yzspreadchg60d, 20))) +normalize(ts_covariance(anl14_high_ntp_fy1, daily_volume_percent_shares_out, 20)) +zscore(multiply(ts_regression(returns, pv173_zscoresbondreturn20deqwt, 20), sign(ts_delta(anl14_high_revenue_fy2, 5)))) +ts_backfill(divide(group_zscore(anl14_high_nav_fy2, industry), ts_mean(anl14_low_ndebt_fy5, 20)), 20) +group_neutralize(add(ts_product(anl14_high_eps_fp1, 10), reverse(ts_product(anl14_low_roe_fy1, 10))), industry) +rank(multiply(ts_arg_max(anl14_high_revenue_fy1, 20), inverse(ts_arg_min(anl14_low_ebit_fy4, 20)))) +ts_scale(subtract(group_mean(anl14_high_revenue_fp1, industry, industry), ts_mean(fnd31_qsg5additionalfactor3_monthly_pcdwf, 20)), 20) +normalize(multiply(ts_count_nans(anl14_high_ebit_fy2, 20), sign(ts_delta(anl14_low_eps_fy5, 5)))) +zscore(group_zscore(add(ts_std_dev(anl14_high_ntp_fy1, 20), ts_std_dev(resampled_pca_factor_1, 20)), industry)) +ts_decay_linear(multiply(signed_power(ts_delta(returns, 5), 2), daily_volume_percent_shares_out), 20) +group_scale(subtract(ts_quantile(daily_volume_percent_shares_out, 20), ts_quantile(anl14_low_ndebt_fy1, 20)), industry) +rank(ts_corr(anl14_high_ebit_fp4, pv173_rawratiosmt5yzspreadchg60d, 20)) +normalize(divide(ts_mean(anl14_high_ntp_fy1, 20), group_mean(anl14_high_ntp_fy1, industry, industry))) +zscore(add(ts_rank(anl14_high_revenue_fy2, 20), reverse(ts_rank(anl14_low_ebit_fy5, 20)))) +ts_zscore(multiply(ts_delay(returns, 5), ts_rank(daily_volume_percent_shares_out, 20)), 20) +group_neutralize(multiply(ts_arg_min(anl14_high_revenue_fy1, 20), ts_delta(pv173_zscoresbondreturn20deqwt, 5)), industry) +rank(subtract(ts_sum(anl14_high_ebit_fp4, 20), ts_sum(pv173_rawratiosmt5yzspreadchg60d, 20))) +normalize(ts_regression(anl14_high_ntp_fy1, daily_volume_percent_shares_out, 20)) +zscore(divide(group_rank(anl14_high_nav_fy2, industry), ts_std_dev(anl14_low_ndebt_fy5, 20))) +ts_backfill(add(ts_product(anl14_high_eps_fp1, 10), ts_product(anl14_low_roe_fy1, 10)), 20) +group_neutralize(ts_delta(returns, 20), industry_grouping_level2_top2000) +group_neutralize(ts_zscore(ts_std_dev(returns, 20), 60), industry_grouping_level2_top2000) +group_neutralize(ts_zscore(daily_volume_to_shares_outstanding, 60), industry_grouping_level2_top2000) +group_neutralize(ts_corr(returns, daily_volume_to_shares_outstanding, 20), industry_grouping_level2_top2000) +group_neutralize(ts_rank(pv37_high_global2, 20), industry_grouping_level2_top2000) +group_neutralize(ts_rank(pv37_low_global, 20), industry_grouping_level2_top2000) +group_neutralize(ts_delta(pv37_high_global2h, 5), industry_grouping_level2_top2000) +group_neutralize(ts_delta(pv37_low_global1h, 5), industry_grouping_level2_top2000) +group_neutralize(ts_mean(pv37_volume_global, 20), industry_grouping_level2_top2000) +group_neutralize(ts_std_dev(pv37_volume_global, 20), industry_grouping_level2_top2000) +group_neutralize(divide(ts_sum(pv37_volume_global, 5), ts_sum(pv37_volume_global, 20)), industry_grouping_level2_top2000) +group_neutralize(ts_corr(pv37_volume_global, returns, 20), industry_grouping_level2_top2000) +group_neutralize(ts_regression(returns, pv37_volume_global, 20, 0, 1), industry_grouping_level2_top2000) +group_neutralize(signed_power(ts_delta(log(pv37_volume_global), 5), 2), industry_grouping_level2_top2000) +group_neutralize(ts_decay_linear(ts_zscore(pv37_volume_global, 20), 10), industry_grouping_level2_top2000) +group_neutralize(ts_rank(ts_delta(pv37_volume_global, 3), 20), industry_grouping_level2_top2000) +group_neutralize(ts_arg_max(pv37_volume_global, 10), industry_grouping_level2_top2000) +group_neutralize(ts_arg_min(pv37_volume_global, 10), industry_grouping_level2_top2000) +group_neutralize(ts_av_diff(pv37_volume_global, 20), industry_grouping_level2_top2000) +group_neutralize(ts_quantile(pv37_volume_global, 20, "gaussian"), industry_grouping_level2_top2000) +group_neutralize(subtract(ts_max(pv37_volume_global, 5), ts_min(pv37_volume_global, 5)), industry_grouping_level2_top2000) +group_neutralize(divide(ts_mean(pv37_volume_global, 5), ts_std_dev(pv37_volume_global, 5)), industry_grouping_level2_top2000) +group_neutralize(ts_backfill(pv37_volume_global, 20, 1, "NAN"), industry_grouping_level2_top2000) +group_neutralize(ts_count_nans(pv37_volume_global, 20), industry_grouping_level2_top2000) +group_neutralize(ts_covariance(returns, pv37_volume_global, 20), industry_grouping_level2_top2000) +group_neutralize(ts_product(ts_zscore(pv37_volume_global, 5), 5), industry_grouping_level2_top2000) +group_neutralize(ts_sum(if_else(ts_delta(pv37_volume_global, 1) > 0, 1, -1), 10), industry_grouping_level2_top2000) +group_neutralize(multiply(ts_rank(pv37_volume_global, 20), ts_rank(returns, 20)), industry_grouping_level2_top2000) +group_neutralize(divide(ts_delay(pv37_volume_global, 5), ts_delay(pv37_volume_global, 10)), industry_grouping_level2_top2000) +group_neutralize(ts_scale(pv37_volume_global, 20), industry_grouping_level2_top2000) +group_neutralize(subtract(ts_mean(pv37_volume_global, 5), ts_mean(pv37_volume_global, 20)), industry_grouping_level2_top2000) +group_neutralize(add(ts_zscore(pv37_volume_global, 20), ts_zscore(returns, 20)), industry_grouping_level2_top2000) +group_neutralize(sign(ts_delta(pv37_volume_global, 1)), industry_grouping_level2_top2000) +group_neutralize(ts_sum(divide(ts_delta(pv37_volume_global, 1), ts_delay(pv37_volume_global, 1)), 10), industry_grouping_level2_top2000) +group_neutralize(abs(ts_delta(log(pv37_volume_global), 5)), industry_grouping_level2_top2000) +group_neutralize(winsorize(ts_zscore(pv37_volume_global, 20), 3), industry_grouping_level2_top2000) +group_neutralize(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), industry_grouping_level2_top2000) +group_neutralize(group_rank(pv37_volume_global, industry_grouping_level2_top2000), industry_grouping_level2_top2000) +group_neutralize(ts_delta(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 5), industry_grouping_level2_top2000) +group_neutralize(ts_rank(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 20), industry_grouping_level2_top2000) +group_neutralize(ts_corr(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), returns, 20), industry_grouping_level2_top2000) +group_neutralize(subtract(ts_mean(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 10), ts_mean(returns, 10)), industry_grouping_level2_top2000) +group_neutralize(multiply(ts_std_dev(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 20), ts_std_dev(returns, 20)), industry_grouping_level2_top2000) +group_neutralize(divide(ts_sum(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 5), ts_sum(returns, 5)), industry_grouping_level2_top2000) +group_neutralize(ts_decay_linear(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 10), industry_grouping_level2_top2000) +group_neutralize(ts_regression(returns, group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 20, 0, 0), industry_grouping_level2_top2000) +group_neutralize(if_else(ts_delta(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 1) > 0, ts_zscore(returns, 20), ts_zscore(returns, 20) * -1), industry_grouping_level2_top2000) +group_neutralize(signed_power(ts_delta(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 3), 2), industry_grouping_level2_top2000) +group_neutralize(ts_backfill(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 20, 1, "NAN"), industry_grouping_level2_top2000) +group_neutralize(ts_count_nans(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 20), industry_grouping_level2_top2000) +group_neutralize(ts_covariance(returns, group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 20), industry_grouping_level2_top2000) +group_neutralize(ts_product(ts_zscore(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 5), 5), industry_grouping_level2_top2000) +group_neutralize(ts_arg_max(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 10), industry_grouping_level2_top2000) +group_neutralize(ts_arg_min(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 10), industry_grouping_level2_top2000) +group_neutralize(ts_av_diff(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 20), industry_grouping_level2_top2000) +group_neutralize(ts_quantile(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 20, "gaussian"), industry_grouping_level2_top2000) +subtract(ts_std_dev(returns, 20), group_mean(ts_std_dev(returns, 20), 1, industry_grouping_level2_top2000)) + +subtract(ts_zscore(ts_std_dev(returns, 20), 60), group_mean(ts_zscore(ts_std_dev(returns, 20), 60), 1, industry_grouping_level2_top2000)) + +subtract(ts_zscore(daily_volume_to_shares_outstanding, 60), group_mean(ts_zscore(daily_volume_to_shares_outstanding, 60), 1, industry_grouping_level2_top2000)) + +subtract(ts_corr(returns, daily_volume_to_shares_outstanding, 20), group_mean(ts_corr(returns, daily_volume_to_shares_outstanding, 20), 1, industry_grouping_level2_top2000)) + +subtract(ts_rank(pv37_high_global2, 20), group_mean(ts_rank(pv37_high_global2, 20), 1, industry_grouping_level2_top2000)) + +subtract(ts_rank(pv37_low_global, 20), group_mean(ts_rank(pv37_low_global, 20), 1, industry_grouping_level2_top2000)) + +subtract(ts_delta(pv37_high_global2h, 5), group_mean(ts_delta(pv37_high_global2h, 5), 1, industry_grouping_level2_top2000)) + +subtract(ts_delta(pv37_low_global1h, 5), group_mean(ts_delta(pv37_low_global1h, 5), 1, industry_grouping_level2_top2000)) + +subtract(ts_mean(pv37_volume_global, 20), group_mean(ts_mean(pv37_volume_global, 20), 1, industry_grouping_level2_top2000)) + +subtract(ts_std_dev(pv37_volume_global, 20), group_mean(ts_std_dev(pv37_volume_global, 20), 1, industry_grouping_level2_top2000)) + +subtract(divide(ts_sum(pv37_volume_global, 5), ts_sum(pv37_volume_global, 20)), group_mean(divide(ts_sum(pv37_volume_global, 5), ts_sum(pv37_volume_global, 20)), 1, industry_grouping_level2_top2000)) + +subtract(ts_corr(pv37_volume_global, returns, 20), group_mean(ts_corr(pv37_volume_global, returns, 20), 1, industry_grouping_level2_top2000)) + +subtract(ts_regression(returns, pv37_volume_global, 20, 0, 1), group_mean(ts_regression(returns, pv37_volume_global, 20, 0, 1), 1, industry_grouping_level2_top2000)) + +subtract(signed_power(ts_delta(log(pv37_volume_global), 5), 2), group_mean(signed_power(ts_delta(log(pv37_volume_global), 5), 2), 1, industry_grouping_level2_top2000)) + +subtract(ts_decay_linear(ts_zscore(pv37_volume_global, 20), 10), group_mean(ts_decay_linear(ts_zscore(pv37_volume_global, 20), 10), 1, industry_grouping_level2_top2000)) + +subtract(ts_rank(ts_delta(pv37_volume_global, 3), 20), group_mean(ts_rank(ts_delta(pv37_volume_global, 3), 20), 1, industry_grouping_level2_top2000)) + +subtract(ts_arg_max(pv37_volume_global, 10), group_mean(ts_arg_max(pv37_volume_global, 10), 1, industry_grouping_level2_top2000)) + +subtract(ts_arg_min(pv37_volume_global, 10), group_mean(ts_arg_min(pv37_volume_global, 10), 1, industry_grouping_level2_top2000)) + +subtract(ts_av_diff(pv37_volume_global, 20), group_mean(ts_av_diff(pv37_volume_global, 20), 1, industry_grouping_level2_top2000)) + +subtract(ts_quantile(pv37_volume_global, 20, "gaussian"), group_mean(ts_quantile(pv37_volume_global, 20, "gaussian"), 1, industry_grouping_level2_top2000)) + +subtract(subtract(ts_max(pv37_volume_global, 5), ts_min(pv37_volume_global, 5)), group_mean(subtract(ts_max(pv37_volume_global, 5), ts_min(pv37_volume_global, 5)), 1, industry_grouping_level2_top2000)) + +subtract(divide(ts_mean(pv37_volume_global, 5), ts_std_dev(pv37_volume_global, 5)), group_mean(divide(ts_mean(pv37_volume_global, 5), ts_std_dev(pv37_volume_global, 5)), 1, industry_grouping_level2_top2000)) + +subtract(ts_backfill(pv37_volume_global, 20, 1, "NAN"), group_mean(ts_backfill(pv37_volume_global, 20, 1, "NAN"), 1, industry_grouping_level2_top2000)) + +subtract(ts_count_nans(pv37_volume_global, 20), group_mean(ts_count_nans(pv37_volume_global, 20), 1, industry_grouping_level2_top2000)) + +subtract(ts_covariance(returns, pv37_volume_global, 20), group_mean(ts_covariance(returns, pv37_volume_global, 20), 1, industry_grouping_level2_top2000)) + +subtract(ts_product(ts_zscore(pv37_volume_global, 5), 5), group_mean(ts_product(ts_zscore(pv37_volume_global, 5), 5), 1, industry_grouping_level2_top2000)) + +subtract(ts_sum(if_else(ts_delta(pv37_volume_global, 1) > 0, 1, -1), 10), group_mean(ts_sum(if_else(ts_delta(pv37_volume_global, 1) > 0, 1, -1), 10), 1, industry_grouping_level2_top2000)) + +subtract(multiply(ts_rank(pv37_volume_global, 20), ts_rank(returns, 20)), group_mean(multiply(ts_rank(pv37_volume_global, 20), ts_rank(returns, 20)), 1, industry_grouping_level2_top2000)) + +subtract(divide(ts_delay(pv37_volume_global, 5), ts_delay(pv37_volume_global, 10)), group_mean(divide(ts_delay(pv37_volume_global, 5), ts_delay(pv37_volume_global, 10)), 1, industry_grouping_level2_top2000)) + +subtract(ts_scale(pv37_volume_global, 20), group_mean(ts_scale(pv37_volume_global, 20), 1, industry_grouping_level2_top2000)) + +subtract(subtract(ts_mean(pv37_volume_global, 5), ts_mean(pv37_volume_global, 20)), group_mean(subtract(ts_mean(pv37_volume_global, 5), ts_mean(pv37_volume_global, 20)), 1, industry_grouping_level2_top2000)) + +subtract(add(ts_zscore(pv37_volume_global, 20), ts_zscore(returns, 20)), group_mean(add(ts_zscore(pv37_volume_global, 20), ts_zscore(returns, 20)), 1, industry_grouping_level2_top2000)) + +subtract(sign(ts_delta(pv37_volume_global, 1)), group_mean(sign(ts_delta(pv37_volume_global, 1)), 1, industry_grouping_level2_top2000)) + +subtract(ts_sum(divide(ts_delta(pv37_volume_global, 1), ts_delay(pv37_volume_global, 1)), 10), group_mean(ts_sum(divide(ts_delta(pv37_volume_global, 1), ts_delay(pv37_volume_global, 1)), 10), 1, industry_grouping_level2_top2000)) + +subtract(abs(ts_delta(log(pv37_volume_global), 5)), group_mean(abs(ts_delta(log(pv37_volume_global), 5)), 1, industry_grouping_level2_top2000)) + +subtract(winsorize(ts_zscore(pv37_volume_global, 20), 3), group_mean(winsorize(ts_zscore(pv37_volume_global, 20), 3), 1, industry_grouping_level2_top2000)) + +subtract(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), group_mean(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 1, industry_grouping_level2_top2000)) + +subtract(group_rank(pv37_volume_global, industry_grouping_level2_top2000), group_mean(group_rank(pv37_volume_global, industry_grouping_level2_top2000), 1, industry_grouping_level2_top2000)) + +subtract(ts_delta(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 5), group_mean(ts_delta(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 5), 1, industry_grouping_level2_top2000)) + +subtract(ts_rank(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 20), group_mean(ts_rank(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 20), 1, industry_grouping_level2_top2000)) + +subtract(ts_corr(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), returns, 20), group_mean(ts_corr(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), returns, 20), 1, industry_grouping_level2_top2000)) + +subtract(subtract(ts_mean(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 10), ts_mean(returns, 10)), group_mean(subtract(ts_mean(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 10), ts_mean(returns, 10)), 1, industry_grouping_level2_top2000)) + +subtract(multiply(ts_std_dev(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 20), ts_std_dev(returns, 20)), group_mean(multiply(ts_std_dev(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 20), ts_std_dev(returns, 20)), 1, industry_grouping_level2_top2000)) + +subtract(divide(ts_sum(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 5), ts_sum(returns, 5)), group_mean(divide(ts_sum(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 5), ts_sum(returns, 5)), 1, industry_grouping_level2_top2000)) + +subtract(ts_decay_linear(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 10), group_mean(ts_decay_linear(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 10), 1, industry_grouping_level2_top2000)) + +subtract(ts_regression(returns, group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 20, 0, 0), group_mean(ts_regression(returns, group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 20, 0, 0), 1, industry_grouping_level2_top2000)) + +subtract(if_else(ts_delta(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 1) > 0, ts_zscore(returns, 20), ts_zscore(returns, 20) * -1), group_mean(if_else(ts_delta(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 1) > 0, ts_zscore(returns, 20), ts_zscore(returns, 20) * -1), 1, industry_grouping_level2_top2000)) + +subtract(signed_power(ts_delta(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 3), 2), group_mean(signed_power(ts_delta(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 3), 2), 1, industry_grouping_level2_top2000)) + +subtract(ts_backfill(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 20, 1, "NAN"), group_mean(ts_backfill(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 20, 1, "NAN"), 1, industry_grouping_level2_top2000)) + +subtract(ts_count_nans(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 20), group_mean(ts_count_nans(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 20), 1, industry_grouping_level2_top2000)) + +subtract(ts_covariance(returns, group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 20), group_mean(ts_covariance(returns, group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 20), 1, industry_grouping_level2_top2000)) + +subtract(ts_product(ts_zscore(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 5), 5), group_mean(ts_product(ts_zscore(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 5), 5), 1, industry_grouping_level2_top2000)) + +subtract(ts_arg_max(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 10), group_mean(ts_arg_max(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 10), 1, industry_grouping_level2_top2000)) + +subtract(ts_arg_min(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 10), group_mean(ts_arg_min(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 10), 1, industry_grouping_level2_top2000)) + +subtract(ts_av_diff(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 20), group_mean(ts_av_diff(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 20), 1, industry_grouping_level2_top2000)) + +subtract(ts_quantile(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 20, "gaussian"), group_mean(ts_quantile(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 20, "gaussian"), 1, industry_grouping_level2_top2000)) + +sign(subtract(ts_sum(fnd72_pit_or_cf_a_cf_net_inc, 250), ts_sum(fnd72_pit_or_is_a_is_net_inc_avail_com_shrhldrs, 250))) + +rank(ts_corr(ts_delta(fnd72_pit_or_bs_q_bs_accrued_income_taxes, 60), ts_delta(fnd72_pit_or_is_q_is_tot_cash_com_dvd, 60), 250)) + +subtract(rank(ts_mean(fnd72_pit_or_cf_q_cf_cash_from_oper, 20)), rank(ts_mean(fnd72_pit_or_cf_q_cf_other_fnc_act, 20))) + +ts_decay_linear(rank(subtract(ts_sum(fnd72_pit_or_bs_q_bs_other_cur_asset, 60), ts_sum(fnd72_pit_or_bs_q_bs_other_cur_liab, 60))), 5) + +multiply(sign(ts_delta(fnd72_pit_or_is_q_net_income, 10)), ts_rank(fnd72_pit_or_is_q_is_invest_income, 30)) + +ts_zscore(ts_sum(subtract(fnd72_pit_or_is_q_is_abnormal_item, fnd72_pit_or_is_q_is_net_non_oper_loss), 60)) + +rank(ts_covariance(ts_mean(fnd72_pit_or_bs_q_bs_future_min_oper_lease_oblig, 30), ts_mean(fnd72_pit_or_is_q_is_eqy_earn_from_invest_assoc, 30), 60)) + +subtract(ts_rank(fnd72_pit_or_cf_a_cf_cash_from_fnc_act, 20), ts_rank(fnd72_pit_or_cf_a_cf_other_fnc_act, 20)) + +ts_scale(ts_sum(subtract(fnd72_pit_or_bs_q_bs_total_avail_line_of_credit, fnd72_pit_or_bs_q_bs_options_outstanding), 20)) + +multiply(rank(ts_delta(fnd72_s_pit_or_is_q_is_invest_income, 5)), ts_rank(fnd72_s_pit_or_is_q_net_income, 10)) + +ts_arg_max(ts_sum(fnd72_s_pit_or_bs_q_bs_accrued_income_taxes, 30), 60) + +ts_arg_min(ts_sum(fnd72_s_pit_or_bs_q_bs_future_min_oper_lease_oblig, 30), 60) + +rank(ts_corr(ts_mean(fnd72_s_pit_or_cf_q_cf_cash_from_oper, 20), ts_mean(fnd72_s_pit_or_cf_q_cf_other_non_cash_adjust, 20), 50)) + +subtract(ts_rank(fnd72_s_pit_or_bs_q_bs_sh_cap_and_apic, 30), ts_rank(fnd72_s_pit_or_bs_q_bs_total_avail_line_of_credit, 30)) + +ts_decay_linear(rank(ts_delta(fnd72_s_pit_or_is_q_is_net_non_oper_loss, 10)), 5) + +multiply(sign(ts_sum(fnd31_devnorthamericaadditionalfactor4_apg, 20)), ts_rank(fnd31_devnorthamericaadditionalfactor4_mpg, 30)) + +ts_zscore(ts_sum(subtract(fnd31_devnorthamericaadditionalfactor4_pbdwf, fnd31_devnorthamericaadditionalfactor4_pcwt), 60)) + +rank(ts_covariance(ts_mean(fnd31_devnorthamericaadditionalfactor4_rev6fy2, 30), ts_mean(fnd31_devnorthamericaadditionalfactor4_tw_ebitdaev, 30), 60)) + +subtract(ts_rank(fnd31_devnorthamericaadditionalfactor4_y5speq4vc, 20), ts_rank(fnd31_devnorthamericaadditionalfactor4_divgp, 20)) + +ts_scale(ts_sum(subtract(fnd31_qsg5additionalfactor3_monthly_earningstorpedo, fnd31_qsg5additionalfactor3_monthly_pcdwf), 20)) + +multiply(rank(ts_delta(fnd31_qsg5additionalfactor3_monthly_fwdebitdaev, 5)), ts_rank(fnd31_qsg5additionalfactor3_monthly_pbwt, 10)) + +ts_arg_max(ts_sum(fnd31_qsg5additionalfactor3_monthly_rev6fy2, 30), 60) + +ts_arg_min(ts_sum(fnd31_qsg5additionalfactor3_monthly_tw_ebitdaev, 30), 60) + +rank(ts_corr(ts_mean(fnd31_qsg5additionalfactor3_monthly_y5speq4rqsr, 20), ts_mean(fnd31_qsg5additionalfactor3_monthly_yoychgroepct, 20), 50)) + +subtract(ts_rank(fnd31_qsg5additionalfactor3_monthly_mpgghcy3, 30), ts_rank(fnd31_qsg5additionalfactor3_monthly_numrevy2, 30)) + +ts_decay_linear(rank(ts_delta(fnd44_perc_accruals, 10)), 5) + +multiply(sign(ts_sum(fnd44_probability_restatement, 20)), ts_rank(fnd44_working_capital_accruals, 30)) + +ts_zscore(ts_sum(subtract(quarterly_net_income_3, quarterly_total_revenue_3), 60)) + +rank(ts_covariance(ts_mean(quarterly_cash_flow_per_share_2, 30), ts_mean(quarterly_free_cash_flow_per_share_2, 30), 60)) + +subtract(ts_rank(quarterly_long_term_debt_to_equity_ratio, 20), ts_rank(quarterly_total_debt_to_equity_2, 20)) + +ts_scale(ts_sum(subtract(quarterly_total_assets_change_yoy, quarterly_total_liabilities_5), 20)) + +multiply(rank(ts_delta(quarterly_pe_ratio_high, 5)), ts_rank(quarterly_price_to_sales_ratio_3, 10)) + +ts_arg_max(ts_sum(quarterly_quick_ratio_2, 30), 60) + +ts_arg_min(ts_sum(quarterly_receivables_turnover_2, 30), 60) + +rank(ts_corr(ts_mean(quarterly_sga_to_sales_ratio, 20), ts_mean(quarterly_working_capital_per_share_to_price_2, 20), 50)) + +subtract(ts_rank(quarterly_book_value_per_share_5, 30), ts_rank(quarterly_tangible_book_value_per_share_3, 30)) + +ts_decay_linear(rank(ts_delta(annual_net_income_incl_extraordinary, 10)), 5) + +multiply(sign(ts_sum(annual_total_revenue, 20)), ts_rank(annual_debt_to_total_capital, 30)) + +ts_zscore(ts_sum(subtract(book_value_per_share_quarterly, cash_per_share), 60)) + +rank(ts_covariance(ts_mean(net_income_available_to_common_incl_extraordinary_annual, 30), ts_mean(normalized_net_income_annual, 30), 60)) + +subtract(ts_rank(operating_income_2, 20), ts_rank(sales_revenue_total, 20)) + +ts_scale(ts_sum(subtract(total_current_assets_4, long_term_debt_total), 20)) + +multiply(rank(ts_delta(accounts_receivable_current_assets, 5)), ts_rank(accounts_total_current_assets, 10)) + +ts_arg_max(ts_sum(short_term_debt_issued, 30), 60) + +ts_arg_min(ts_sum(long_term_secured_debt, 30), 60) + +rank(ts_corr(ts_mean(investment_contracts_total, 20), ts_mean(value_investment_assets_total, 20), 50)) + +subtract(ts_rank(share_based_awards, 30), ts_rank(share_based_deferred_tax, 30)) + +ts_decay_linear(rank(ts_delta(share_capital_ordinary_quarterly, 10)), 5) + +multiply(sign(ts_sum(shareholders_other_equity_2, 20)), ts_rank(short_term_bank_debt_amount, 30)) + +ts_zscore(ts_sum(subtract(short_term_loans_debt, short_term_payables_other), 60)) + +rank(ts_covariance(ts_mean(long_term_liabilities_total_3, 30), ts_mean(long_term_minority_interest, 30), 60)) + +subtract(ts_rank(long_term_trade_debt, 20), ts_rank(long_term_other_current_liabilities, 20)) + +ts_scale(ts_sum(subtract(voting_common_shares_total, voting_investment_total), 20)) + +multiply(rank(ts_delta(fnd17_arev, 5)), ts_rank(fnd17_arevps, 10)) + +ts_arg_max(ts_sum(fnd17_apayratio, 30), 60) + +ts_arg_min(ts_sum(fnd17_qcurratio2, 30), 60) + +rank(divide(fnd72_pit_or_cf_q_cf_cash_from_oper, fnd72_pit_or_is_q_net_income)) +rank(subtract(1, divide(fnd44_perc_accruals, ts_mean(fnd72_pit_or_is_q_net_income, 12)))) +zscore(multiply(divide(quarterly_cash_flow_per_share_nonannualized, net_income_quarterly_4), inverse(ts_std_dev(normalized_net_income, 24)))) +normalize(add(divide(fnd72_pit_or_cf_a_cf_cash_from_fnc_act, fnd72_pit_or_is_a_is_net_inc_avail_com_shrhldrs), inverse(ts_rank(fnd44_working_capital_accruals, 6)))) +rank(multiply(divide(quarterly_free_cash_flow_per_share, book_value_per_share_quarterly), sign(subtract(ts_mean(quarterly_asset_turnover, 12), ts_mean(quarterly_asset_turnover, 24))))) +zscore(divide(fnd72_pit_or_cf_q_cf_cash_from_oper, add(fnd72_pit_or_is_q_net_income, abs(fnd44_perc_accruals)))) +rank(subtract(divide(quarterly_cash_per_share_3, net_income_available_to_common_incl_extraordinary_annual), fnd44_probability_restatement)) +normalize(multiply(inverse(ts_std_dev(quarterly_net_income_3, 18)), divide(quarterly_free_cash_flow, quarterly_total_revenue_3))) +zscore(add(divide(fnd72_s_pit_or_cf_q_cf_cash_from_oper, fnd72_s_pit_or_is_q_net_income), inverse(fnd44_perc_accruals))) +rank(multiply(divide(quarterly_cash_flow_per_share_2, quarterly_earnings_before_tax_5), inverse(ts_std_dev(quarterly_operating_margin_percent, 12)))) +normalize(subtract(1, divide(fnd44_perc_accruals, ts_mean(fnd72_pit_or_is_a_is_net_inc_avail_com_shrhldrs, 36)))) +zscore(divide(quarterly_free_cash_flow_per_share_non_annualized_2, multiply(net_income_annual, ts_rank(fnd44_working_capital_accruals, 12)))) +rank(add(divide(fnd72_pit_or_cf_a_cf_net_inc, fnd72_pit_or_is_a_is_net_interest_expense), inverse(ts_std_dev(quarterly_net_income_available_to_common_2, 24)))) +normalize(multiply(divide(quarterly_cash_flow_per_share_alt, normalized_net_income_available_to_common_annual), sign(ts_delta(quarterly_asset_turnover, 6)))) +zscore(subtract(divide(quarterly_free_cash_flow_per_share_alt_2, net_income_after_tax_2), multiply(fnd44_probability_restatement, 0.1))) +rank(divide(add(fnd72_pit_or_cf_q_cf_cash_from_oper, fnd72_pit_or_cf_a_cf_cash_from_fnc_act), add(fnd72_pit_or_is_q_net_income, abs(fnd44_perc_accruals)))) +normalize(multiply(inverse(ts_std_dev(quarterly_operating_margin_percent, 18)), divide(quarterly_cash, net_income_current_period))) +zscore(add(divide(quarterly_cash_flow_per_share_non_annualized, book_value_per_share_quarterly), inverse(ts_rank(fnd44_perc_accruals, 24)))) +rank(subtract(divide(quarterly_free_cash_flow_per_share, quarterly_pe_ratio_high), fnd44_probability_restatement)) +normalize(divide(fnd72_s_pit_or_cf_q_cf_cash_from_oper, multiply(fnd72_s_pit_or_is_q_net_income, ts_std_dev(normalized_net_income_annual, 12)))) +zscore(multiply(divide(quarterly_cash_per_share_value, net_income_quarterly_4), inverse(ts_std_dev(quarterly_receivables_turnover_2, 24)))) +rank(add(divide(quarterly_free_cash_flow, quarterly_total_assets_change_percent), inverse(fnd44_working_capital_accruals))) +normalize(subtract(divide(fnd72_pit_or_cf_a_cf_cash_from_fnc_act, fnd72_pit_or_is_a_is_net_inc_avail_com_shrhldrs), ts_rank(fnd44_perc_accruals, 6))) +zscore(divide(quarterly_cash_flow_per_share_alt_2, add(net_income_available_to_common_incl_extraordinary_annual, abs(fnd44_perc_accruals)))) +rank(multiply(divide(quarterly_free_cash_flow_per_share_non_annualized, quarterly_book_value_per_share_5), sign(ts_delta(quarterly_asset_turnover, 12)))) +normalize(add(divide(fnd72_pit_or_cf_q_cf_net_inc, fnd72_pit_or_is_q_net_income), inverse(ts_std_dev(quarterly_net_income_3, 36)))) +zscore(subtract(1, divide(fnd44_perc_accruals, ts_mean(quarterly_net_income_available_to_common_2, 24)))) +rank(divide(multiply(quarterly_cash_flow_per_share_2, quarterly_asset_turnover), add(net_income_annual, fnd44_probability_restatement))) +normalize(multiply(inverse(ts_std_dev(quarterly_operating_margin_percent, 6)), divide(quarterly_free_cash_flow_per_share, quarterly_total_revenue_3))) +zscore(add(divide(fnd72_s_pit_or_cf_q_cf_cash_from_oper, fnd72_s_pit_or_is_q_net_income), inverse(ts_rank(fnd44_working_capital_accruals, 18)))) +rank(subtract(divide(quarterly_cash_per_share_3, normalized_net_income), fnd44_perc_accruals)) +normalize(divide(quarterly_free_cash_flow_per_share_alt_2, multiply(net_income_after_tax_2, ts_std_dev(quarterly_net_income_3, 12)))) +zscore(multiply(divide(quarterly_cash_flow_per_share_non_annualized_prior, quarterly_earnings_before_tax), inverse(ts_std_dev(quarterly_operating_margin_percent, 36)))) +rank(add(divide(fnd72_pit_or_cf_a_cf_cash_from_fnc_act, fnd72_pit_or_is_a_is_net_interest_expense), inverse(fnd44_probability_restatement))) +normalize(subtract(divide(quarterly_cash_flow_per_share, net_income_quarterly_4), ts_rank(fnd44_perc_accruals, 24))) +zscore(divide(quarterly_free_cash_flow, add(fnd72_pit_or_is_q_net_income, abs(fnd44_working_capital_accruals)))) +rank(multiply(divide(quarterly_cash_per_share_value, book_value_per_share_quarterly), sign(ts_delta(quarterly_receivables_turnover_2, 6)))) +normalize(add(inverse(ts_std_dev(normalized_net_income_available_to_common_annual, 18)), divide(quarterly_free_cash_flow_per_share_non_annualized, net_income_annual))) +zscore(subtract(1, divide(fnd44_perc_accruals, ts_mean(quarterly_net_income_available_to_common_2, 12)))) +rank(divide(add(quarterly_cash_flow_per_share_alt, fnd72_pit_or_cf_q_cf_cash_from_oper), add(net_income_current_period, abs(fnd44_perc_accruals)))) +normalize(multiply(divide(quarterly_free_cash_flow_per_share, quarterly_pe_ratio_maximum), inverse(ts_std_dev(quarterly_asset_turnover, 24)))) +zscore(add(divide(fnd72_pit_or_cf_a_cf_net_inc, fnd72_pit_or_is_a_is_net_inc_avail_com_shrhldrs), inverse(ts_rank(fnd44_working_capital_accruals, 36)))) +rank(subtract(divide(quarterly_cash_per_share_3, quarterly_book_value_per_share_2), multiply(fnd44_probability_restatement, 0.05))) +normalize(divide(quarterly_cash_flow_per_share_non_annualized, multiply(net_income_available_to_common_incl_extraordinary_annual, ts_std_dev(quarterly_operating_margin_percent, 6)))) +zscore(multiply(divide(quarterly_free_cash_flow_per_share_alt_2, quarterly_earnings_before_tax_5), sign(subtract(ts_mean(quarterly_receivables_turnover_2, 12), ts_mean(quarterly_receivables_turnover_2, 24))))) +rank(add(divide(fnd72_s_pit_or_cf_q_cf_cash_from_oper, fnd72_s_pit_or_is_q_net_income), inverse(fnd44_perc_accruals))) +normalize(subtract(divide(quarterly_free_cash_flow, quarterly_total_revenue_3), ts_rank(fnd44_probability_restatement, 12))) +zscore(divide(quarterly_cash_flow_per_share_2, add(normalized_net_income, abs(fnd44_working_capital_accruals)))) +rank(multiply(inverse(ts_std_dev(quarterly_net_income_3, 24)), divide(quarterly_cash, net_income_annual))) +normalize(add(divide(quarterly_cash_flow_per_share_alt, book_value_per_share_quarterly), inverse(ts_rank(fnd44_perc_accruals, 18)))) +zscore(subtract(1, divide(fnd44_working_capital_accruals, ts_mean(fnd72_pit_or_is_a_is_net_inc_avail_com_shrhldrs, 24)))) \ No newline at end of file diff --git a/generated_alpha/2026/01/28/Pro_deepseek-ai_DeepSeek-V3.1-Terminus_171838.txt b/generated_alpha/2026/01/28/Pro_deepseek-ai_DeepSeek-V3.1-Terminus_171838.txt new file mode 100644 index 0000000..70573c7 --- /dev/null +++ b/generated_alpha/2026/01/28/Pro_deepseek-ai_DeepSeek-V3.1-Terminus_171838.txt @@ -0,0 +1,117 @@ +ts_std_dev(returns, 20) + +zscore(ts_std_dev(returns, 20)) + +group_neutralize(ts_std_dev(returns, 20), industry) + +divide(ts_std_dev(returns, 20), group_mean(ts_std_dev(returns, 20), sharesout, industry)) + +ts_corr(ts_std_dev(returns, 20), resampled_pca_factor_1, 20) + +ts_delta(ts_std_dev(returns, 20), 5) + +ts_rank(ts_std_dev(returns, 20), 20) + +multiply(ts_std_dev(returns, 20), ts_mean(pv37_volume_14, 20)) + +ts_corr(ts_std_dev(returns, 20), fnd31_ohlsonscore, 20) + +add(ts_std_dev(returns, 20), ts_std_dev(returns, 10)) + +subtract(ts_std_dev(returns, 20), ts_std_dev(returns, 10)) + +multiply(ts_std_dev(returns, 20), sign(ts_delta(returns, 1))) + +group_zscore(ts_std_dev(returns, 20), industry) + +ts_decay_linear(ts_std_dev(returns, 20), 10) + +normalize(ts_std_dev(returns, 20)) + +rank(ts_std_dev(returns, 20)) + +quantile(ts_std_dev(returns, 20)) + +winsorize(ts_std_dev(returns, 20), 4) + +log(ts_std_dev(returns, 20)) + +zscore(anl14_high_revenue_fy2) + +group_neutralize(anl14_low_ebit_fy5, industry) + +ts_corr(returns, ts_backfill(anl14_high_eps_fp1, 250), 20) + +multiply(ts_std_dev(returns, 20), zscore(fnd31_ohlsonscore)) + +add(zscore(anl14_high_revenue_fy2), zscore(anl14_high_ebit_fy2)) + +ts_mean(returns, 20) + +ts_std_dev(ts_mean(returns, 5), 20) + +ts_corr(ts_mean(returns, 5), ts_std_dev(returns, 20), 20) + +group_zscore(pv37_volume_14, industry) + +ts_delta(pv37_volume_14, 5) + +zscore(pv37_low_14) + +subtract(pv37_high_global, pv37_low_global) + +ts_rank(pv37_volume_global, 20) + +multiply(ts_std_dev(returns, 20), ts_rank(pv37_volume_14, 20)) + +group_neutralize(fnd86_fundamental_score, industry) + +ts_corr(returns, resampled_pca_factor_1, 20) + +zscore(ts_corr(returns, resampled_pca_factor_1, 20)) + +ts_decay_linear(returns, 10) + +ts_sum(returns, 20) + +power(ts_sum(returns, 20), 2) + +zscore(pv173_rawratiosbondreturn20deqwt) + +ts_corr(returns, pv173_rawratiosbondreturn20deqwt, 20) + +group_mean(ts_std_dev(returns, 20), sharesout, industry) + +divide(ts_std_dev(returns, 20), group_mean(ts_std_dev(returns, 20), sharesout, industry)) + +zscore(divide(ts_std_dev(returns, 20), group_mean(ts_std_dev(returns, 20), sharesout, industry))) + +multiply(zscore(divide(ts_std_dev(returns, 20), group_mean(ts_std_dev(returns, 20), sharesout, industry))), ts_mean(resampled_pca_factor_1, 20)) + +ts_backfill(anl14_high_revenue_fy2, 250) + +zscore(ts_backfill(anl14_high_revenue_fy2, 250)) + +ts_delta(ts_backfill(anl14_high_revenue_fy2, 250), 5) + +ts_corr(returns, ts_backfill(anl14_high_revenue_fy2, 250), 20) + +rank(fnd44_score) + +group_neutralize(rank(fnd44_score), industry) + +multiply(ts_std_dev(returns, 20), rank(fnd44_score)) + +ts_zscore(returns, 20) + +ts_av_diff(returns, 20) + +ts_quantile(returns, 20) + +ts_arg_max(returns, 20) + +ts_arg_min(returns, 20) + +ts_count_nans(returns, 20) + +ts_covariance(returns, resampled_pca_factor_1, 20) \ No newline at end of file diff --git a/generated_alpha/2026/01/28/Pro_deepseek-ai_DeepSeek-V3.1-Terminus_174630.txt b/generated_alpha/2026/01/28/Pro_deepseek-ai_DeepSeek-V3.1-Terminus_174630.txt new file mode 100644 index 0000000..c506302 --- /dev/null +++ b/generated_alpha/2026/01/28/Pro_deepseek-ai_DeepSeek-V3.1-Terminus_174630.txt @@ -0,0 +1,99 @@ +rank(divide(fnd72_pit_or_cf_q_cf_cash_from_oper, net_income_quarterly_4)) + +zscore(fnd44_perc_accruals) + +normalize(divide(quarterly_free_cash_flow_per_share, net_income_quarterly_4)) + +rank(divide(net_income_quarterly_4, annual_total_revenue)) + +zscore(divide(operating_income_2, annual_total_revenue)) + +normalize(divide(quarterly_cash_flow_per_share, net_income_quarterly_4)) + +rank(divide(quarterly_earnings_before_tax_5, annual_total_revenue)) + +zscore(fnd31_devnorthamericaadditionalfactor4_apg) + +normalize(divide(quarterly_research_and_development_expense_2, annual_total_revenue)) + +rank(quarterly_sga_to_sales_ratio) + +zscore(divide(quarterly_total_assets, quarterly_total_liabilities)) + +normalize(quarterly_working_capital_per_share_to_price_2) + +rank(quarterly_long_term_debt_to_equity_ratio) + +zscore(quarterly_interest_coverage_ratio) + +normalize(quarterly_quick_ratio) + +rank(quarterly_current_ratio_alt) + +zscore(quarterly_receivables_turnover_2) + +normalize(quarterly_asset_turnover) + +rank(quarterly_return_on_assets_percent_2) + +zscore(anl14_high_eps_fy2) + +normalize(anl14_low_eps_fy2) + +rank(anl14_high_roe_fy2) + +zscore(anl14_low_roe_fy2) + +normalize(fnd31_devnorthamericaadditionalfactor4_tw_ebitdaev) + +rank(fnd31_devnorthamericaadditionalfactor4_ps_wt) + +zscore(fnd31_devnorthamericaadditionalfactor4_pcwt) + +normalize(fnd31_devnorthamericaadditionalfactor4_pbdwf) + +rank(fnd44_probability_restatement) + +zscore(fnd44_working_capital_accruals) + +normalize(srp_earnings_score) + +rank(divide(quarterly_net_income_available_to_common_2, quarterly_total_revenue_3)) + +zscore(divide(quarterly_free_cash_flow_per_share_alt_2, net_income_quarterly_4)) + +normalize(divide(quarterly_dividend_per_share, quarterly_earnings_before_tax)) + +rank(divide(quarterly_long_term_debt_to_assets, quarterly_total_assets)) + +zscore(divide(quarterly_price_to_cash_flow_per_share_2, 1)) + +normalize(divide(quarterly_price_to_sales_ratio, 1)) + +rank(divide(anl14_high_ebit_fy2, annual_total_revenue)) + +zscore(divide(anl14_low_ebit_fy2, annual_total_revenue)) + +normalize(divide(anl14_high_ebitda_fy2, annual_total_revenue)) + +rank(divide(anl14_low_ebitda_fy2, annual_total_revenue)) + +zscore(divide(quarterly_total_shareholder_equity, quarterly_total_assets)) + +normalize(divide(quarterly_net_income_3, quarterly_total_assets)) + +rank(divide(quarterly_operating_margin_percent, 1)) + +zscore(divide(quarterly_pe_ratio_high, 1)) + +normalize(divide(anl69_best_net_gaap, 1)) + +rank(divide(anl69_best_px_cps_ratio, 1)) + +zscore(divide(fnd17_arevps, 1)) + +normalize(divide(fnd17_pehigh, 1)) + +rank(divide(quarterly_book_value_per_share_5, 1)) + +zscore(divide(quarterly_tangible_book_value_per_share_3, 1)) \ No newline at end of file diff --git a/generated_alpha/2026/01/28/Pro_moonshotai_Kimi-K2-Thinking_172450.txt b/generated_alpha/2026/01/28/Pro_moonshotai_Kimi-K2-Thinking_172450.txt new file mode 100644 index 0000000..a319426 --- /dev/null +++ b/generated_alpha/2026/01/28/Pro_moonshotai_Kimi-K2-Thinking_172450.txt @@ -0,0 +1,99 @@ +rank(ts_std_dev(returns, 20)) + +divide(rank(ts_std_dev(returns, 20)), group_mean(rank(ts_std_dev(returns, 20)), 1, industry_grouping_level2_top1500)) + +rank(ts_mean(daily_volume_percent_shares_out, 20)) + +ts_delta(ts_std_dev(returns, 20), 5) + +ts_std_dev(returns, 60) + +group_neutralize(rank(ts_std_dev(returns, 20)), industry_grouping_level2_top1500) + +multiply(rank(ts_std_dev(returns, 20)), rank(daily_volume_to_shares_outstanding)) + +reverse(ts_delta(rank(ts_std_dev(returns, 20)), 10)) + +ts_av_diff(rank(ts_std_dev(returns, 20)), 60) + +multiply(ts_std_dev(returns, 20), ts_std_dev(returns, 60)) + +subtract(group_max(ts_std_dev(returns, 20), industry_grouping_level2_top1500), group_min(ts_std_dev(returns, 20), industry_grouping_level2_top1500)) + +ts_corr(ts_std_dev(returns, 20), daily_volume_percent_shares_out, 20) + +ts_std_dev(ts_std_dev(returns, 20), 20) + +zscore(ts_std_dev(returns, 20)) + +ts_decay_linear(rank(ts_std_dev(returns, 20)), 20) + +add(rank(ts_std_dev(returns, 10)), add(rank(ts_std_dev(returns, 20)), rank(ts_std_dev(returns, 40)))) + +multiply(ts_delta(daily_volume_percent_shares_out, 3), ts_std_dev(returns, 20)) + +divide(rank(daily_volume_to_shares_outstanding), group_mean(rank(daily_volume_to_shares_outstanding), 1, industry_grouping_level2_top1500)) + +ts_sum(ts_delta(ts_std_dev(returns, 20), 1), 20) + +subtract(ts_max(ts_std_dev(returns, 20), 20), ts_std_dev(returns, 20)) + +if_else(rank(daily_volume_percent_shares_out) < 0.5, rank(ts_std_dev(returns, 20)), reverse(rank(ts_std_dev(returns, 20)))) + +reverse(rank(ts_std_dev(returns, 20))) + +divide(ts_std_dev(returns, 20), group_mean(ts_std_dev(returns, 20), 1, industry_grouping_level2_top1500)) + +multiply(group_rank(ts_std_dev(returns, 20), industry_grouping_level2_top1500), rank(daily_volume_percent_shares_out)) + +ts_corr(ts_std_dev(returns, 20), ts_delay(ts_std_dev(returns, 20), 1), 20) + +ts_delta(ts_std_dev(returns, 60), 10) + +subtract(ts_std_dev(returns, 10), ts_std_dev(returns, 60)) + +subtract(rank(ts_std_dev(returns, 20)), rank(ts_mean(daily_volume_to_shares_outstanding, 20))) + +group_neutralize(multiply(ts_std_dev(returns, 20), ts_std_dev(returns, 60)), industry_grouping_level2_top1500) + +multiply(ts_std_dev(ts_std_dev(returns, 20), 20), ts_std_dev(returns, 20)) + +multiply(ts_std_dev(returns, 20), ts_corr(returns, ts_delay(returns, 1), 20)) + +divide(ts_std_dev(returns, 20), ts_mean(daily_volume_percent_shares_out, 20)) + +ts_delta(ts_std_dev(returns, 20), 1) + +ts_delta(group_mean(ts_std_dev(returns, 20), 1, industry_grouping_level2_top1500), 10) + +add(multiply(rank(ts_std_dev(returns, 20)), 2), multiply(rank(daily_volume_percent_shares_out), 1.5)) + +subtract(rank(ts_std_dev(returns, 5)), rank(ts_std_dev(returns, 60))) + +multiply(ts_delta(rank(daily_volume_to_shares_outstanding), 3), ts_delta(rank(ts_std_dev(returns, 20)), 3)) + +multiply(group_rank(ts_std_dev(returns, 20), industry_grouping_level2_top1500), rank(ts_std_dev(returns, 20))) + +divide(rank(ts_std_dev(returns, 20)), add(ts_std_dev(returns, 60), 0.001)) + +ts_arg_min(rank(ts_std_dev(returns, 20)), 60) + +add(rank(ts_std_dev(returns, 20)), multiply(rank(ts_mean(daily_volume_percent_shares_out, 20)), 0.5)) + +subtract(rank(ts_std_dev(returns, 20)), group_zscore(ts_std_dev(returns, 20), industry_grouping_level2_top1500)) + +multiply(ts_delta(rank(ts_std_dev(returns, 20)), 5), ts_delta(rank(daily_volume_to_shares_outstanding), 5)) + +divide(ts_std_dev(returns, 20), ts_max(ts_std_dev(returns, 60), 60)) + +multiply(ts_std_dev(returns, 20), group_mean(ts_std_dev(returns, 20), 1, industry_grouping_level2_top1500)) + +abs(ts_av_diff(ts_std_dev(returns, 20), 20)) + +group_mean(multiply(ts_std_dev(returns, 20), daily_volume_percent_shares_out), 1, industry_grouping_level2_top1500) + +if_else(ts_std_dev(returns, 60) > ts_mean(ts_std_dev(returns, 60), 120), reverse(rank(ts_std_dev(returns, 20))), rank(ts_std_dev(returns, 20))) + +ts_product(rank(ts_std_dev(returns, 20)), 10) + +group_neutralize(add(multiply(rank(ts_std_dev(returns, 20)), 3), multiply(rank(ts_mean(daily_volume_to_shares_outstanding, 20)), 2)), industry_grouping_level2_top1500) \ No newline at end of file diff --git a/generated_alpha/2026/01/28/Pro_moonshotai_Kimi-K2-Thinking_175352.txt b/generated_alpha/2026/01/28/Pro_moonshotai_Kimi-K2-Thinking_175352.txt new file mode 100644 index 0000000..20c077f --- /dev/null +++ b/generated_alpha/2026/01/28/Pro_moonshotai_Kimi-K2-Thinking_175352.txt @@ -0,0 +1,99 @@ +rank(fnd44_perc_accruals) + +rank(fnd44_working_capital_accruals) + +rank(divide(quarterly_cash_flow_per_share_2, add(net_income_quarterly_4, 0.01))) + +rank(divide(quarterly_free_cash_flow_per_share_2, add(quarterly_net_income_3, 0.01))) + +rank(divide(quarterly_cash_flow_per_share_2, add(fnd13_statementq_sopi, 0.01))) + +rank(ts_std_dev(ttm_net_income_value, 252)) + +rank(ts_std_dev(quarterly_asset_turnover, 252)) + +subtract(rank(divide(quarterly_free_cash_flow_per_share_2, add(net_income_quarterly_4, 0.01))), rank(fnd44_perc_accruals)) + +rank(fnd44_probability_restatement) + +scale(fnd44_perc_accruals) + +scale(divide(quarterly_cash_flow_per_share_2, add(quarterly_earnings_before_tax_5, 0.01))) + +add(rank(fnd44_perc_accruals), rank(divide(quarterly_cash_flow_per_share_2, add(net_income_quarterly_4, 0.01)))) + +winsorize(fnd44_perc_accruals, std=3) + +winsorize(divide(quarterly_cash_flow_per_share_2, add(quarterly_net_income_3, 0.01)), std=3) + +scale(subtract(rank(divide(quarterly_free_cash_flow_per_share_2, add(net_income_quarterly_4, 0.01))), rank(fnd44_perc_accruals))) + +zscore(fnd44_perc_accruals) + +zscore(divide(quarterly_cash_flow_per_share_2, add(net_income_quarterly_4, 0.01))) + +subtract(divide(quarterly_free_cash_flow_per_share_2, add(quarterly_net_income_3, 0.01)), fnd44_perc_accruals) + +divide(divide(quarterly_free_cash_flow_per_share_2, add(net_income_quarterly_4, 0.01)), add(fnd44_perc_accruals, 0.01)) + +rank(ts_corr(quarterly_cash_flow_per_share_2, quarterly_net_income_3, 252)) + +rank(quarterly_return_on_assets_percent_2) + +add(add(rank(divide(quarterly_free_cash_flow_per_share_2, add(net_income_quarterly_4, 0.01))), rank(quarterly_asset_turnover)), reverse(rank(fnd44_perc_accruals))) + +log(add(abs(fnd44_perc_accruals), 0.01)) + +inverse(add(fnd44_working_capital_accruals, 0.01)) + +signed_power(subtract(rank(divide(quarterly_free_cash_flow_per_share_2, add(quarterly_earnings_before_tax_5, 0.01))), rank(fnd44_perc_accruals)), 2) + +quantile(subtract(rank(divide(quarterly_cash_flow_per_share_2, add(net_income_quarterly_4, 0.01))), rank(fnd44_perc_accruals))) + +bucket(rank(subtract(divide(quarterly_free_cash_flow_per_share_2, add(quarterly_net_income_3, 0.01)), fnd44_perc_accruals)), range="0,1,0.2") + +ts_delay(rank(fnd44_working_capital_accruals), 21) + +ts_delta(rank(fnd44_perc_accruals), 21) + +ts_mean(rank(divide(quarterly_cash_flow_per_share_2, add(net_income_quarterly_4, 0.01))), 252) + +ts_min(rank(fnd44_perc_accruals), 252) + +ts_max(rank(fnd44_working_capital_accruals), 252) + +rank(ts_std_dev(quarterly_pe_ratio_high, 126)) + +rank(ts_std_dev(quarterly_asset_turnover, 126)) + +subtract(rank(ts_std_dev(ttm_net_income_value, 252)), rank(ts_std_dev(quarterly_asset_turnover, 252))) + +vector_neut(rank(fnd44_perc_accruals), rank(book_value_per_share_quarterly)) + +scale_down(rank(subtract(divide(quarterly_free_cash_flow_per_share_2, add(net_income_quarterly_4, 0.01)), fnd44_perc_accruals))) + +normalize(rank(subtract(divide(quarterly_cash_flow_per_share_2, add(fnd13_statementq_sopi, 0.01)), fnd44_working_capital_accruals))) + +hump(rank(subtract(divide(quarterly_free_cash_flow_per_share_2, add(quarterly_net_income_3, 0.01)), fnd44_perc_accruals)), hump=0.05) + +jump_decay(rank(subtract(divide(quarterly_cash_flow_per_share_2, add(net_income_quarterly_4, 0.01)), fnd44_perc_accruals)), 21, sensitivity=0.5, force=0.1) + +ts_decay_linear(rank(subtract(divide(quarterly_free_cash_flow_per_share_2, add(net_income_quarterly_4, 0.01)), fnd44_perc_accruals)), 252, dense=false) + +rank(add(rank(divide(quarterly_cash_flow_per_share_2, add(net_income_quarterly_4, 0.01))), rank(divide(quarterly_free_cash_flow_per_share_2, add(quarterly_net_income_3, 0.01))))) + +subtract(rank(divide(quarterly_cash_flow_per_share_2, add(fnd13_statementq_sopi, 0.01))), rank(fnd44_working_capital_accruals)) + +rank(divide(quarterly_total_shareholder_equity, add(quarterly_total_debt_4, 0.01))) + +multiply(rank(divide(quarterly_free_cash_flow_per_share_2, add(net_income_quarterly_4, 0.01))), rank(quarterly_current_ratio_alt)) + +rank(subtract(quarterly_working_capital_per_share_to_price_2, fnd44_working_capital_accruals)) + +ts_rank(subtract(rank(divide(quarterly_free_cash_flow_per_share_2, add(net_income_quarterly_4, 0.01))), rank(fnd44_perc_accruals)), 252) + +ts_arg_max(rank(divide(quarterly_cash_flow_per_share_2, add(quarterly_earnings_before_tax_5, 0.01))), 252) + +ts_arg_min(rank(fnd44_working_capital_accruals), 252) + +rank(add(add(rank(divide(quarterly_free_cash_flow_per_share_2, add(net_income_quarterly_4, 0.01))), rank(quarterly_asset_turnover)), subtract(rank(reverse(fnd44_perc_accruals)), rank(fnd44_probability_restatement)))) \ No newline at end of file diff --git a/generated_alpha/2026/01/28/Qwen_Qwen3-VL-235B-A22B-Instruct_171924.txt b/generated_alpha/2026/01/28/Qwen_Qwen3-VL-235B-A22B-Instruct_171924.txt new file mode 100644 index 0000000..e3d7a58 --- /dev/null +++ b/generated_alpha/2026/01/28/Qwen_Qwen3-VL-235B-A22B-Instruct_171924.txt @@ -0,0 +1,61 @@ +ts_std_dev(returns, 20) +ts_mean(returns, 20) +group_zscore(ts_std_dev(returns, 20), industry) +ts_zscore(ts_std_dev(returns, 20), 20) +group_neutralize(ts_std_dev(returns, 20), industry) +ts_delta(ts_std_dev(returns, 20), 1) +ts_rank(ts_std_dev(returns, 20), 20) +ts_corr(ts_std_dev(returns, 20), ts_mean(returns, 20), 20) +ts_decay_linear(ts_std_dev(returns, 20), 20) +ts_backfill(ts_std_dev(returns, 20), 20) +ts_arg_max(ts_std_dev(returns, 20), 20) +ts_arg_min(ts_std_dev(returns, 20), 20) +ts_av_diff(ts_std_dev(returns, 20), 20) +ts_quantile(ts_std_dev(returns, 20), 20) +ts_covariance(ts_std_dev(returns, 20), ts_mean(returns, 20), 20) +ts_product(ts_std_dev(returns, 20), 20) +ts_scale(ts_std_dev(returns, 20), 20) +ts_sum(ts_std_dev(returns, 20), 20) +ts_count_nans(ts_std_dev(returns, 20), 20) +ts_regression(ts_std_dev(returns, 20), ts_mean(returns, 20), 20) +ts_target_tvr_decay(ts_std_dev(returns, 20), 0, 1, 0.1) +ts_target_tvr_delta_limit(ts_std_dev(returns, 20), ts_mean(returns, 20), 0, 1, 0.1) +group_mean(ts_std_dev(returns, 20), ts_mean(returns, 20), industry) +group_scale(ts_std_dev(returns, 20), industry) +group_rank(ts_std_dev(returns, 20), industry) +group_backfill(ts_std_dev(returns, 20), industry, 20) +normalize(ts_std_dev(returns, 20), false, 0) +quantile(ts_std_dev(returns, 20), gaussian, 1.0) +rank(ts_std_dev(returns, 20), 2) +scale(ts_std_dev(returns, 20), 1, 1, 1) +winsorize(ts_std_dev(returns, 20), 4) +zscore(ts_std_dev(returns, 20)) +vec_avg(ts_std_dev(returns, 20)) +vec_sum(ts_std_dev(returns, 20)) +bucket(rank(ts_std_dev(returns, 20)), "0,1,0.1") +trade_when(ts_std_dev(returns, 20), ts_mean(returns, 20), ts_delta(ts_std_dev(returns, 20), 1)) +add(ts_std_dev(returns, 20), ts_mean(returns, 20), false) +subtract(ts_std_dev(returns, 20), ts_mean(returns, 20), false) +multiply(ts_std_dev(returns, 20), ts_mean(returns, 20), false) +divide(ts_std_dev(returns, 20), ts_mean(returns, 20)) +power(ts_std_dev(returns, 20), 2) +signed_power(ts_std_dev(returns, 20), 2) +sqrt(ts_std_dev(returns, 20)) +reverse(ts_std_dev(returns, 20)) +sign(ts_std_dev(returns, 20)) +abs(ts_std_dev(returns, 20)) +log(ts_std_dev(returns, 20)) +inverse(ts_std_dev(returns, 20)) +max(ts_std_dev(returns, 20), ts_mean(returns, 20)) +min(ts_std_dev(returns, 20), ts_mean(returns, 20)) +and(ts_std_dev(returns, 20) > 0, ts_mean(returns, 20) > 0) +or(ts_std_dev(returns, 20) > 0, ts_mean(returns, 20) > 0) +not(ts_std_dev(returns, 20) > 0) +is_nan(ts_std_dev(returns, 20)) +if_else(ts_std_dev(returns, 20) > ts_mean(returns, 20), 1, 0) +ts_step(1) +days_from_last_change(ts_std_dev(returns, 20)) +hump(ts_std_dev(returns, 20), 0.01) +kth_element(ts_std_dev(returns, 20), 20, 1) +last_diff_value(ts_std_dev(returns, 20), 20) +jump_decay(ts_std_dev(returns, 20), 20, 0.5, 0.1) \ No newline at end of file diff --git a/generated_alpha/2026/01/28/Qwen_Qwen3-VL-235B-A22B-Instruct_174755.txt b/generated_alpha/2026/01/28/Qwen_Qwen3-VL-235B-A22B-Instruct_174755.txt new file mode 100644 index 0000000..10de990 --- /dev/null +++ b/generated_alpha/2026/01/28/Qwen_Qwen3-VL-235B-A22B-Instruct_174755.txt @@ -0,0 +1,49 @@ +rank(divide(fnd72_pit_or_cf_q_cf_cash_from_oper, fnd72_pit_or_is_q_net_income)) +rank(divide(fnd72_pit_or_cf_q_cf_cash_from_oper, fnd72_pit_or_is_q_net_income)) +rank(divide(fnd72_pit_or_cf_q_cf_cash_from_oper, fnd72_pit_or_is_q_net_income)) +rank(divide(fnd72_pit_or_cf_q_cf_cash_from_oper, fnd72_pit_or_is_q_net_income)) +rank(divide(fnd72_pit_or_cf_q_cf_cash_from_oper, fnd72_pit_or_is_q_net_income)) +rank(divide(fnd72_pit_or_cf_q_cf_cash_from_oper, fnd72_pit_or_is_q_net_income)) +rank(divide(fnd72_pit_or_cf_q_cf_cash_from_oper, fnd72_pit_or_is_q_net_income)) +rank(divide(fnd72_pit_or_cf_q_cf_cash_from_oper, fnd72_pit_or_is_q_net_income)) +rank(divide(fnd72_pit_or_cf_q_cf_cash_from_oper, fnd72_pit_or_is_q_net_income)) +rank(divide(fnd72_pit_or_cf_q_cf_cash_from_oper, fnd72_pit_or_is_q_net_income)) +rank(divide(fnd72_pit_or_cf_q_cf_cash_from_oper, fnd72_pit_or_is_q_net_income)) +rank(divide(fnd72_pit_or_cf_q_cf_cash_from_oper, fnd72_pit_or_is_q_net_income)) +rank(divide(fnd72_pit_or_cf_q_cf_cash_from_oper, fnd72_pit_or_is_q_net_income)) +rank(divide(fnd72_pit_or_cf_q_cf_cash_from_oper, fnd72_pit_or_is_q_net_income)) +rank(divide(fnd72_pit_or_cf_q_cf_cash_from_oper, fnd72_pit_or_is_q_net_income)) +rank(divide(fnd72_pit_or_cf_q_cf_cash_from_oper, fnd72_pit_or_is_q_net_income)) +rank(divide(fnd72_pit_or_cf_q_cf_cash_from_oper, fnd72_pit_or_is_q_net_income)) +rank(divide(fnd72_pit_or_cf_q_cf_cash_from_oper, fnd72_pit_or_is_q_net_income)) +rank(divide(fnd72_pit_or_cf_q_cf_cash_from_oper, fnd72_pit_or_is_q_net_income)) +rank(divide(fnd72_pit_or_cf_q_cf_cash_from_oper, fnd72_pit_or_is_q_net_income)) +rank(divide(fnd72_pit_or_cf_q_cf_cash_from_oper, fnd72_pit_or_is_q_net_income)) +rank(divide(fnd72_pit_or_cf_q_cf_cash_from_oper, fnd72_pit_or_is_q_net_income)) +rank(divide(fnd72_pit_or_cf_q_cf_cash_from_oper, fnd72_pit_or_is_q_net_income)) +rank(divide(fnd72_pit_or_cf_q_cf_cash_from_oper, fnd72_pit_or_is_q_net_income)) +rank(divide(fnd72_pit_or_cf_q_cf_cash_from_oper, fnd72_pit_or_is_q_net_income)) +rank(divide(fnd72_pit_or_cf_q_cf_cash_from_oper, fnd72_pit_or_is_q_net_income)) +rank(divide(fnd72_pit_or_cf_q_cf_cash_from_oper, fnd72_pit_or_is_q_net_income)) +rank(divide(fnd72_pit_or_cf_q_cf_cash_from_oper, fnd72_pit_or_is_q_net_income)) +rank(divide(fnd72_pit_or_cf_q_cf_cash_from_oper, fnd72_pit_or_is_q_net_income)) +rank(divide(fnd72_pit_or_cf_q_cf_cash_from_oper, fnd72_pit_or_is_q_net_income)) +rank(divide(fnd72_pit_or_cf_q_cf_cash_from_oper, fnd72_pit_or_is_q_net_income)) +rank(divide(fnd72_pit_or_cf_q_cf_cash_from_oper, fnd72_pit_or_is_q_net_income)) +rank(divide(fnd72_pit_or_cf_q_cf_cash_from_oper, fnd72_pit_or_is_q_net_income)) +rank(divide(fnd72_pit_or_cf_q_cf_cash_from_oper, fnd72_pit_or_is_q_net_income)) +rank(divide(fnd72_pit_or_cf_q_cf_cash_from_oper, fnd72_pit_or_is_q_net_income)) +rank(divide(fnd72_pit_or_cf_q_cf_cash_from_oper, fnd72_pit_or_is_q_net_income)) +rank(divide(fnd72_pit_or_cf_q_cf_cash_from_oper, fnd72_pit_or_is_q_net_income)) +rank(divide(fnd72_pit_or_cf_q_cf_cash_from_oper, fnd72_pit_or_is_q_net_income)) +rank(divide(fnd72_pit_or_cf_q_cf_cash_from_oper, fnd72_pit_or_is_q_net_income)) +rank(divide(fnd72_pit_or_cf_q_cf_cash_from_oper, fnd72_pit_or_is_q_net_income)) +rank(divide(fnd72_pit_or_cf_q_cf_cash_from_oper, fnd72_pit_or_is_q_net_income)) +rank(divide(fnd72_pit_or_cf_q_cf_cash_from_oper, fnd72_pit_or_is_q_net_income)) +rank(divide(fnd72_pit_or_cf_q_cf_cash_from_oper, fnd72_pit_or_is_q_net_income)) +rank(divide(fnd72_pit_or_cf_q_cf_cash_from_oper, fnd72_pit_or_is_q_net_income)) +rank(divide(fnd72_pit_or_cf_q_cf_cash_from_oper, fnd72_pit_or_is_q_net_income)) +rank(divide(fnd72_pit_or_cf_q_cf_cash_from_oper, fnd72_pit_or_is_q_net_income)) +rank(divide(fnd72_pit_or_cf_q_cf_cash_from_oper, fnd72_pit_or_is_q_net_income)) +rank(divide(fnd72_pit_or_cf_q_cf_cash_from_oper, fnd72_pit_or_is_q_net_income)) +rank(divide(fnd72_pit_or_cf_q_cf_cash_from_oper, fnd72_pit_or_is_q_net_income)) \ No newline at end of file diff --git a/main.py b/main.py index 10fd0d7..05e3cdf 100644 --- a/main.py +++ b/main.py @@ -31,7 +31,7 @@ MAX_DATA_COUNT = 800 RANDOM_DATA_COUNT = 500 # 数据库搜索字段 -REGION = 'USA' +REGION = 'GLB' UNIVERSE = 'TOP3000' @@ -54,7 +54,7 @@ MODELS = [ 'Pro/deepseek-ai/DeepSeek-V3.1-Terminus', # 'deepseek-ai/DeepSeek-V3.2-Exp', 'Qwen/Qwen3-VL-235B-A22B-Instruct', - # 'Pro/moonshotai/Kimi-K2-Thinking', + 'Pro/moonshotai/Kimi-K2-Thinking', # 'MiniMaxAI/MiniMax-M2', # 'zai-org/GLM-4.6', # 'inclusionAI/Ring-flash-2.0', diff --git a/manual_prompt/2026/01/26/manual_prompt_20260126170731.txt b/manual_prompt/2026/01/26/manual_prompt_20260126170731.txt new file mode 100644 index 0000000..c55dcbe --- /dev/null +++ b/manual_prompt/2026/01/26/manual_prompt_20260126170731.txt @@ -0,0 +1,438 @@ +供应链议价能力反转效应 +假设 +在供应链关系中，若企业对少数核心供应商或客户依赖度过高，其议价能力较弱，易受外部冲击影响，导致未来盈利波动性上升和股价下行风险增加；反之，议价能力强的企业（表现为分散的上下游关系或强势账期）往往具备更稳定的现金流与成本控制能力，市场对其风险定价不足，从而产生系统性超额收益。多头应选择议价能力较强、供应链结构稳健的公司，空头则为议价弱势、集中度高的公司。 +实施方案 +核心指标包括：前五大供应商采购占比、前五大客户销售占比、应付账款周转天数与应收账款周转天数之差（反映净议价能力）。需对原始比率进行行业中性化处理，并采用过去12个月滚动窗口计算以平滑短期波动；对缺失值使用行业时间序列中位数填充。可引入毛利率稳定性、营业成本同比波动率作为辅助验证变量，检验因子是否捕捉到真实议价优势而非单纯规模效应。 + +*=========================================================================================* +输出格式： +输出必须是且仅是纯文本。 +每一行是一个完整、独立、语法正确的WebSim表达式。 +严禁任何形式的解释、编号、标点包裹（如引号）、Markdown格式或额外文本。 +===================== !!! 重点(输出方式) !!! ===================== +现在，请严格遵守以上所有规则，开始生成可立即在WebSim中运行的复合因子表达式。 +不要自行假设, 你需要用到的操作符 和 数据集, 必须从我提供给你的里面查找, 并严格按照里面的使用方法进行组合 +**输出格式**(一行一个表达式, 每个表达式中间需要添加一个空行, 只要表达式本身, 不需要赋值, 不要解释, 不需要序号, 也不要输出多余的东西)： +表达式 +表达式 +表达式 +... +表达式 +================================================================= +重申：请确保所有表达式都使用WorldQuant WebSim平台函数，不要使用pandas、numpy或其他Python库函数。输出必须是一行有效的WQ表达式。 +以下是我的账号有权限使用的操作符, 请严格按照操作符, 以及我提供的数据集, 进行生成,组合 50 个 alpha： +不要自行假设, 你需要用到的操作符 和 数据集, 必须从我提供给你的里面查找, 并严格按照里面的使用方法进行组合 +================================================================= +**操作符汇总 +**算术运算符 (Arithmetic): +abs(x) - 绝对值 +add(x, y, filter=false) - 加法 (x + y) +densify(x) - 分组字段稠密化 +divide(x, y) - 除法 (x / y) +inverse(x) - 倒数 (1/x) +log(x) - 自然对数 +max(x, y, ..) - 最大值 +min(x, y, ..) - 最小值 +multiply(x, y, filter=false) - 乘法 (x * y) +power(x, y) - 幂运算 (x^y) +reverse(x) - 取反 (-x) +sign(x) - 符号函数 +signed_power(x, y) - 保留符号的幂运算 +sqrt(x) - 平方根 +subtract(x, y, filter=false) - 减法 (x - y) +to_nan(x, value=0, reverse=false) - 值与NaN转换 +**逻辑运算符 (Logical): +and(input1, input2) - 逻辑与 +if_else(input1, input2, input3) - 条件判断 +input1 < input2 - 小于比较 +input1 <= input2 - 小于等于 +input1 == input2 - 等于比较 +input1 > input2 - 大于比较 +input1 >= input2 - 大于等于 +input1 != input2 - 不等于 +is_nan(input) - 是否为NaN +not(x) - 逻辑非 +or(input1, input2) - 逻辑或 +**时间序列运算符 (Time Series): +days_from_last_change(x) - 上次变化天数 +hump(x, hump=0.01) - 限制变化幅度 +jump_decay(x, d, sensitivity=0.5, force=0.1) - 跳跃衰减 +kth_element(x, d, k) - 第K个值 +last_diff_value(x, d) - 最后一个不同值 +ts_arg_max(x, d) - 最大值相对索引 +ts_arg_min(x, d) - 最小值相对索引 +ts_av_diff(x, d) - 与均值的差 +ts_backfill(x, lookback=d, k=1, ignore="NAN") - 回填 +ts_corr(x, y, d) - 时间序列相关性 +ts_count_nans(x, d) - NaN计数 +ts_covariance(y, x, d) - 协方差 +ts_decay_linear(x, d, dense=false) - 线性衰减 +ts_delay(x, d) - 延迟值 +ts_delta(x, d) - 差值 (x - 延迟值) +ts_max(x, d) - 时间序列最大值 +ts_mean(x, d) - 时间序列均值 +ts_min(x, d) - 时间序列最小值 +ts_product(x, d) - 时间序列乘积 +ts_quantile(x, d, driver="gaussian") - 分位数 +ts_rank(x, d, constant=0) - 时间序列排名 +ts_regression(y, x, d, lag=0, rettype=0) - 回归分析 +ts_scale(x, d, constant=0) - 时间序列缩放 +ts_std_dev(x, d) - 时间序列标准差 +ts_step(1) - 天数计数器 +ts_sum(x, d) - 时间序列求和 +ts_target_tvr_decay(x, lambda_min=0, lambda_max=1, target_tvr=0.1) - 目标换手率衰减 +ts_target_tvr_delta_limit(x, y, lambda_min=0, lambda_max=1, target_tvr=0.1) - 目标换手率差值限制 +ts_zscore(x, d) - 时间序列Z分数 +**横截面运算符 (Cross Sectional): +normalize(x, useStd=false, limit=0.0) - 标准化 +quantile(x, driver=gaussian, sigma=1.0) - 分位数转换 +rank(x, rate=2) - 排名 +scale(x, scale=1, longscale=1, shortscale=1) - 缩放 +scale_down(x, constant=0) - 按比例缩放 +vector_neut(x, y) - 向量中性化 +winsorize(x, std=4) - 缩尾处理 +zscore(x) - Z分数 +**向量运算符 (Vector): +vec_avg(x) - 向量均值 +vec_max(x) - 向量最大值 +vec_min(x) - 向量最小值 +vec_sum(x) - 向量求和 +**变换运算符 (Transformational): +bucket(rank(x), range="0,1,0.1" or buckets="2,5,6,7,10") - 分桶 +generate_stats(alpha) - 生成统计量 +trade_when(x, y, z) - 条件交易 +**分组运算符 (Group): +combo_a(alpha, nlength=250, mode='algo1') - 组合Alpha +group_backfill(x, group, d, std=4.0) - 分组回填 +group_cartesian_product(g1, g2) - 笛卡尔积分组 +group_max(x, group) - 分组最大值 +group_mean(x, weight, group) - 分组均值 +group_min(x, group) - 分组最小值 +group_neutralize(x, group) - 分组中性化 +group_rank(x, group) - 分组排名 +group_scale(x, group) - 分组缩放 +group_zscore(x, group) - 分组Z分数 +**特殊运算符 (Special): +in - 包含判断 +self_corr(input) - 自相关性 +universe_size - 宇宙大小 +**归约运算符 (Reduce): +reduce_avg(input, threshold=0) - 平均值归约 +reduce_choose(input, nth, ignoreNan=true) - 选择归约 +reduce_count(input, threshold) - 计数归约 +reduce_ir(input) - IR归约 +reduce_kurtosis(input) - 峰度归约 +reduce_max(input) - 最大值归约 +reduce_min(input) - 最小值归约 +reduce_norm(input) - 范数归约 +reduce_percentage(input, percentage=0.5) - 百分比归约 +reduce_powersum(input, constant=2, precise=false) - 幂和归约 +reduce_range(input) - 范围归约 +reduce_skewness(input) - 偏度归约 +reduce_stddev(input, threshold=0) - 标准差归约 +reduce_sum(input) - 求和归约 + +以下是我的账号有权限使用的操作符, 请严格按照操作符, 进行生成,组合因子 + +========================= 操作符开始 ======================================= +注意: Operator: 后面的是操作符(是可以使用的), +Description: 此字段后面的是操作符对应的描述或使用说明(禁止使用, 仅供参考), Description字段后面的内容是使用说明, 不是操作符 +特别注意!!!! 必须按照操作符字段Operator的使用说明生成 alphaOperator: abs(x) +Description: Absolute value of x +Operator: add(x, y, filter = false) +Description: Add all inputs (at least 2 inputs required). If filter = true, filter all input NaN to 0 before adding +Operator: densify(x) +Description: Converts a grouping field of many buckets into lesser number of only available buckets so as to make working with grouping fields computationally efficient +Operator: divide(x, y) +Description: x / y +Operator: inverse(x) +Description: 1 / x +Operator: log(x) +Description: Natural logarithm. For example: Log(high/low) uses natural logarithm of high/low ratio as stock weights. +Operator: max(x, y, ..) +Description: Maximum value of all inputs. At least 2 inputs are required +Operator: min(x, y ..) +Description: Minimum value of all inputs. At least 2 inputs are required +Operator: multiply(x ,y, ... , filter=false) +Description: Multiply all inputs. At least 2 inputs are required. Filter sets the NaN values to 1 +Operator: power(x, y) +Description: x ^ y +Operator: reverse(x) +Description: - x +Operator: sign(x) +Description: if input > 0, return 1; if input < 0, return -1; if input = 0, return 0; if input = NaN, return NaN; +Operator: signed_power(x, y) +Description: x raised to the power of y such that final result preserves sign of x +Operator: sqrt(x) +Description: Square root of x +Operator: subtract(x, y, filter=false) +Description: x-y. If filter = true, filter all input NaN to 0 before subtracting +Operator: and(input1, input2) +Description: Logical AND operator, returns true if both operands are true and returns false otherwise +Operator: if_else(input1, input2, input 3) +Description: If input1 is true then return input2 else return input3. +Operator: input1 < input2 +Description: If input1 < input2 return true, else return false +Operator: input1 <= input2 +Description: Returns true if input1 <= input2, return false otherwise +Operator: input1 == input2 +Description: Returns true if both inputs are same and returns false otherwise +Operator: input1 > input2 +Description: Logic comparison operators to compares two inputs +Operator: input1 >= input2 +Description: Returns true if input1 >= input2, return false otherwise +Operator: input1!= input2 +Description: Returns true if both inputs are NOT the same and returns false otherwise +Operator: is_nan(input) +Description: If (input == NaN) return 1 else return 0 +Operator: not(x) +Description: Returns the logical negation of x. If x is true (1), it returns false (0), and if input is false (0), it returns true (1). +Operator: or(input1, input2) +Description: Logical OR operator returns true if either or both inputs are true and returns false otherwise +Operator: days_from_last_change(x) +Description: Amount of days since last change of x +Operator: hump(x, hump = 0.01) +Description: Limits amount and magnitude of changes in input (thus reducing turnover) +Operator: kth_element(x, d, k) +Description: Returns K-th value of input by looking through lookback days. This operator can be used to backfill missing data if k=1 +Operator: last_diff_value(x, d) +Description: Returns last x value not equal to current x value from last d days +Operator: ts_arg_max(x, d) +Description: Returns the relative index of the max value in the time series for the past d days. If the current day has the max value for the past d days, it returns 0. If previous day has the max value for the past d days, it returns 1 +Operator: ts_arg_min(x, d) +Description: Returns the relative index of the min value in the time series for the past d days; If the current day has the min value for the past d days, it returns 0; If previous day has the min value for the past d days, it returns 1. +Operator: ts_av_diff(x, d) +Description: Returns x - tsmean(x, d), but deals with NaNs carefully. That is NaNs are ignored during mean computation +Operator: ts_backfill(x,lookback = d, k=1, ignore="NAN") +Description: Backfill is the process of replacing the NAN or 0 values by a meaningful value (i.e., a first non-NaN value) +Operator: ts_corr(x, y, d) +Description: Returns correlation of x and y for the past d days +Operator: ts_count_nans(x ,d) +Description: Returns the number of NaN values in x for the past d days +Operator: ts_covariance(y, x, d) +Description: Returns covariance of y and x for the past d days +Operator: ts_decay_linear(x, d, dense = false) +Description: Returns the linear decay on x for the past d days. Dense parameter=false means operator works in sparse mode and we treat NaN as 0. In dense mode we do not. +Operator: ts_delay(x, d) +Description: Returns x value d days ago +Operator: ts_delta(x, d) +Description: Returns x - ts_delay(x, d) +Operator: ts_mean(x, d) +Description: Returns average value of x for the past d days. +Operator: ts_product(x, d) +Description: Returns product of x for the past d days +Operator: ts_quantile(x,d, driver="gaussian" ) +Description: It calculates ts_rank and apply to its value an inverse cumulative density function from driver distribution. Possible values of driver (optional ) are "gaussian", "uniform", "cauchy" distribution where "gaussian" is the default. +Operator: ts_rank(x, d, constant = 0) +Description: Rank the values of x for each instrument over the past d days, then return the rank of the current value + constant. If not specified, by default, constant = 0. +Operator: ts_regression(y, x, d, lag = 0, rettype = 0) +Description: Returns various parameters related to regression function +Operator: ts_scale(x, d, constant = 0) +Description: Returns (x - ts_min(x, d)) / (ts_max(x, d) - ts_min(x, d)) + constant. This operator is similar to scale down operator but acts in time series space +Operator: ts_std_dev(x, d) +Description: Returns standard deviation of x for the past d days +Operator: ts_step(1) +Description: Returns days' counter +Operator: ts_sum(x, d) +Description: Sum values of x for the past d days. +Operator: ts_zscore(x, d) +Description: Z-score is a numerical measurement that describes a value's relationship to the mean of a group of values. Z-score is measured in terms of standard deviations from the mean: (x - tsmean(x,d)) / tsstddev(x,d). This operator may help reduce outliers and drawdown. +Operator: normalize(x, useStd = false, limit = 0.0) +Description: Calculates the mean value of all valid alpha values for a certain date, then subtracts that mean from each element +Operator: quantile(x, driver = gaussian, sigma = 1.0) +Description: Rank the raw vector, shift the ranked Alpha vector, apply distribution (gaussian, cauchy, uniform). If driver is uniform, it simply subtract each Alpha value with the mean of all Alpha values in the Alpha vector +Operator: rank(x, rate=2) +Description: Ranks the input among all the instruments and returns an equally distributed number between 0.0 and 1.0. For precise sort, use the rate as 0 +Operator: scale(x, scale=1, longscale=1, shortscale=1) +Description: Scales input to booksize. We can also scale the long positions and short positions to separate scales by mentioning additional parameters to the operator +Operator: winsorize(x, std=4) +Description: Winsorizes x to make sure that all values in x are between the lower and upper limits, which are specified as multiple of std. +Operator: zscore(x) +Description: Z-score is a numerical measurement that describes a value's relationship to the mean of a group of values. Z-score is measured in terms of standard deviations from the mean +Operator: vec_avg(x) +Description: Taking mean of the vector field x +Operator: vec_sum(x) +Description: Sum of vector field x +Operator: bucket(rank(x), range="0, 1, 0.1" or buckets = "2,5,6,7,10") +Description: Convert float values into indexes for user-specified buckets. Bucket is useful for creating group values, which can be passed to GROUP as input +Operator: trade_when(x, y, z) +Description: Used in order to change Alpha values only under a specified condition and to hold Alpha values in other cases. It also allows to close Alpha positions (assign NaN values) under a specified condition +Operator: group_backfill(x, group, d, std = 4.0) +Description: If a certain value for a certain date and instrument is NaN, from the set of same group instruments, calculate winsorized mean of all non-NaN values over last d days +Operator: group_mean(x, weight, group) +Description: All elements in group equals to the mean +Operator: group_neutralize(x, group) +Description: Neutralizes Alpha against groups. These groups can be subindustry, industry, sector, country or a constant +Operator: group_rank(x, group) +Description: Each elements in a group is assigned the corresponding rank in this group +Operator: group_scale(x, group) +Description: Normalizes the values in a group to be between 0 and 1. (x - groupmin) / (groupmax - groupmin) +Operator: group_zscore(x, group) +Description: Calculates group Z-score - numerical measurement that describes a value's relationship to the mean of a group of values. Z-score is measured in terms of standard deviations from the mean. zscore = (data - mean) / stddev of x for each instrument within its group. +========================= 操作符结束 ======================================= + +========================= 数据字段开始 ======================================= +注意: data_set_name: 后面的是数据字段(可以使用), description: 此字段后面的是数据字段对应的描述或使用说明(不能使用) + +{'id': 61979, 'data_set_name': '可以使用:quarterly_asset_turnover', 'description': '不可使用,仅供参考:Annualized ratio of sales to average assets for the quarter.'} +{'id': 61980, 'data_set_name': '可以使用:quarterly_asset_turnover_2', 'description': '不可使用,仅供参考:Annualized ratio of sales to average assets for the quarter.'} +{'id': 61981, 'data_set_name': '可以使用:quarterly_asset_turnover_db', 'description': '不可使用,仅供参考:Asset turnover for the most recent quarter (alternate source).'} +{'id': 62012, 'data_set_name': '可以使用:quarterly_cost_of_goods_sold', 'description': '不可使用,仅供参考:Cost of goods sold for the most recent quarter.'} +{'id': 62013, 'data_set_name': '可以使用:quarterly_cost_of_goods_sold_2', 'description': '不可使用,仅供参考:Cost of goods sold for the most recent quarter.'} +{'id': 62014, 'data_set_name': '可以使用:quarterly_cost_of_goods_sold_3', 'description': '不可使用,仅供参考:Total cost of goods sold for the most recent quarter.'} +{'id': 62063, 'data_set_name': '可以使用:quarterly_gross_margin_percent', 'description': '不可使用,仅供参考:Gross margin for the quarter, expressed as a percentage of revenue.'} +{'id': 62064, 'data_set_name': '可以使用:quarterly_gross_margin_percent_2', 'description': '不可使用,仅供参考:Gross margin percentage for the most recent quarter.'} +{'id': 62065, 'data_set_name': '可以使用:quarterly_gross_margin_percent_3', 'description': '不可使用,仅供参考:Gross margin for the most recent quarter, expressed as a percentage.'} +{'id': 62074, 'data_set_name': '可以使用:quarterly_inventory_turnover', 'description': '不可使用,仅供参考:Annualized ratio of cost of goods sold to average inventory for the quarter.'} +{'id': 62075, 'data_set_name': '可以使用:quarterly_inventory_turnover_2', 'description': '不可使用,仅供参考:Annualized cost of goods sold divided by average inventory for the quarter.'} +{'id': 62076, 'data_set_name': '可以使用:quarterly_inventory_turnover_ratio', 'description': '不可使用,仅供参考:Inventory turnover ratio for the most recent quarter.'} +{'id': 62111, 'data_set_name': '可以使用:quarterly_net_profit_margin_percent', 'description': '不可使用,仅供参考:Net income divided by revenue for the quarter, expressed as a percentage.'} +{'id': 62112, 'data_set_name': '可以使用:quarterly_net_profit_margin_percent_2', 'description': '不可使用,仅供参考:Net profit margin percentage for the most recent quarter.'} +{'id': 62113, 'data_set_name': '可以使用:quarterly_net_profit_margin_percent_3', 'description': '不可使用,仅供参考:Net income divided by revenue for the quarter, as a percentage.'} +{'id': 62119, 'data_set_name': '可以使用:quarterly_operating_margin_percent', 'description': '不可使用,仅供参考:Operating income divided by revenue for the quarter, expressed as a percentage.'} +{'id': 62120, 'data_set_name': '可以使用:quarterly_operating_margin_percent_2', 'description': '不可使用,仅供参考:Operating margin percentage for the most recent quarter.'} +{'id': 62121, 'data_set_name': '可以使用:quarterly_operating_margin_percent_3', 'description': '不可使用,仅供参考:Operating income divided by revenue for the quarter, expressed as a percentage.'} +{'id': 62129, 'data_set_name': '可以使用:quarterly_pretax_margin_percent', 'description': '不可使用,仅供参考:Pretax income divided by revenue for the quarter, expressed as a percentage.'} +{'id': 62130, 'data_set_name': '可以使用:quarterly_pretax_margin_percent_2', 'description': '不可使用,仅供参考:Pretax margin percentage for the most recent quarter.'} +{'id': 62131, 'data_set_name': '可以使用:quarterly_pretax_margin_percent_3', 'description': '不可使用,仅供参考:Pretax income divided by revenue for the quarter, as a percentage.'} +{'id': 62134, 'data_set_name': '可以使用:quarterly_price_to_sales_ratio', 'description': '不可使用,仅供参考:Price divided by sales per share for the quarter.'} +{'id': 62135, 'data_set_name': '可以使用:quarterly_price_to_sales_ratio_2', 'description': '不可使用,仅供参考:Ratio of price to sales for the most recent quarter.'} +{'id': 62136, 'data_set_name': '可以使用:quarterly_price_to_sales_ratio_3', 'description': '不可使用,仅供参考:Price divided by sales per share for the quarter.'} +{'id': 62143, 'data_set_name': '可以使用:quarterly_receivables', 'description': '不可使用,仅供参考:Receivables reported for the most recent quarter.'} +{'id': 62144, 'data_set_name': '可以使用:quarterly_receivables_2', 'description': '不可使用,仅供参考:Total receivables reported for the most recent quarter.'} +{'id': 62145, 'data_set_name': '可以使用:quarterly_receivables_db', 'description': '不可使用,仅供参考:Receivables reported for the most recent quarter (alternate source).'} +{'id': 62146, 'data_set_name': '可以使用:quarterly_receivables_turnover', 'description': '不可使用,仅供参考:Annualized ratio of sales to average receivables for the quarter.'} +{'id': 62147, 'data_set_name': '可以使用:quarterly_receivables_turnover_2', 'description': '不可使用,仅供参考:Receivables turnover ratio for the most recent quarter.'} +{'id': 62173, 'data_set_name': '可以使用:quarterly_sga_to_sales_ratio', 'description': '不可使用,仅供参考:Selling, general, and administrative expenses as a percent of revenue for the quarter.'} +{'id': 62174, 'data_set_name': '可以使用:quarterly_sga_to_sales_ratio_2', 'description': '不可使用,仅供参考:SG&A expenses as a percent of revenue for the quarter.'} +{'id': 62214, 'data_set_name': '可以使用:receivables_turnover_quarterly', 'description': '不可使用,仅供参考:Annualized ratio of total revenue to average receivables for the most recent quarter.'} +{'id': 62286, 'data_set_name': '可以使用:anl44_2_grossmargin_coveredby', 'description': '不可使用,仅供参考:grossmargin coveredby'} +{'id': 62287, 'data_set_name': '可以使用:anl44_2_grossmargin_lastactccy', 'description': '不可使用,仅供参考:grossmargin lastactccy'} +{'id': 62288, 'data_set_name': '可以使用:anl44_2_grossmargin_lastactvalue', 'description': '不可使用,仅供参考:grossmargin lastactvalue'} +{'id': 62289, 'data_set_name': '可以使用:anl44_2_grossmargin_prevalue', 'description': '不可使用,仅供参考:grossmargin prevalue'} +{'id': 62290, 'data_set_name': '可以使用:anl44_2_grossmargin_value', 'description': '不可使用,仅供参考:grossmargin value'} +{'id': 62336, 'data_set_name': '可以使用:anl44_2_sales_coveredby', 'description': '不可使用,仅供参考:sales coveredby'} +{'id': 62337, 'data_set_name': '可以使用:anl44_2_sales_lastactccy', 'description': '不可使用,仅供参考:sales lastactccy'} +{'id': 62338, 'data_set_name': '可以使用:anl44_2_sales_lastactvalue', 'description': '不可使用,仅供参考:sales lastactvalue'} +{'id': 62339, 'data_set_name': '可以使用:anl44_2_sales_prevalue', 'description': '不可使用,仅供参考:sales prevalue'} +{'id': 62340, 'data_set_name': '可以使用:anl44_2_sales_value', 'description': '不可使用,仅供参考:sales value'} +{'id': 62361, 'data_set_name': '可以使用:forecast_currency_sales', 'description': '不可使用,仅供参考:Currency in which the sales forecast is denominated.'} +{'id': 62363, 'data_set_name': '可以使用:grossmargin_currency_code', 'description': '不可使用,仅供参考:Currency code in which the gross margin forecast is denominated.'} +{'id': 62442, 'data_set_name': '可以使用:anl69_best_ebit_to_sales', 'description': '不可使用,仅供参考:EBIT Margin'} +{'id': 62725, 'data_set_name': '可以使用:anl69_sales_best_crncy_iso', 'description': '不可使用,仅供参考:Currency (ISO)'} +{'id': 62726, 'data_set_name': '可以使用:anl69_sales_best_cur_fiscal_qtr_period', 'description': '不可使用,仅供参考:Current Fiscal Quarter Period D'} +{'id': 62727, 'data_set_name': '可以使用:anl69_sales_best_cur_fiscal_semi_year_period', 'description': '不可使用,仅供参考:Current Fiscal Semi-Year Period'} +{'id': 62728, 'data_set_name': '可以使用:anl69_sales_best_cur_fiscal_year_period', 'description': '不可使用,仅供参考:Current Fiscal Year Period Date'} +{'id': 62729, 'data_set_name': '可以使用:anl69_sales_best_eeps_cur_yr', 'description': '不可使用,仅供参考:Est Earnings Per Share Curr Year'} +{'id': 62730, 'data_set_name': '可以使用:anl69_sales_best_eeps_nxt_yr', 'description': '不可使用,仅供参考:Est Earnings Per Share Next Year'} +{'id': 62731, 'data_set_name': '可以使用:anl69_sales_best_fperiod_override', 'description': '不可使用,仅供参考:Fiscal Period Override'} +{'id': 62732, 'data_set_name': '可以使用:anl69_sales_best_sales', 'description': '不可使用,仅供参考:Sales'} +{'id': 62733, 'data_set_name': '可以使用:anl69_sales_best_sales_4wk_chg', 'description': '不可使用,仅供参考:Sales 4 Wk Chg'} +{'id': 62734, 'data_set_name': '可以使用:anl69_sales_best_sales_hi', 'description': '不可使用,仅供参考:Sales High'} +{'id': 62735, 'data_set_name': '可以使用:anl69_sales_best_sales_lo', 'description': '不可使用,仅供参考:Sales Low'} +{'id': 62736, 'data_set_name': '可以使用:anl69_sales_best_sales_numest', 'description': '不可使用,仅供参考:Sales # Ests'} +{'id': 62737, 'data_set_name': '可以使用:anl69_sales_best_sales_stddev', 'description': '不可使用,仅供参考:Sales Std Dev'} +{'id': 62738, 'data_set_name': '可以使用:anl69_sales_expected_report_dt', 'description': '不可使用,仅供参考:Expected Earnings Report Date'} +{'id': 62739, 'data_set_name': '可以使用:anl69_sales_expected_report_time', 'description': '不可使用,仅供参考:Expected Earnings Report Time'} +{'id': 62740, 'data_set_name': '可以使用:anl69_saless_best_crncy_iso', 'description': '不可使用,仅供参考:Currency (ISO)'} +{'id': 62741, 'data_set_name': '可以使用:anl69_saless_best_cur_fiscal_qtr_period', 'description': '不可使用,仅供参考:Current Fiscal Quarter Period D'} +{'id': 62742, 'data_set_name': '可以使用:anl69_saless_best_cur_fiscal_semi_year_period', 'description': '不可使用,仅供参考:Current Fiscal Semi-Year Period'} +{'id': 62743, 'data_set_name': '可以使用:anl69_saless_best_cur_fiscal_year_period', 'description': '不可使用,仅供参考:Current Fiscal Year Period Date'} +{'id': 62744, 'data_set_name': '可以使用:anl69_saless_best_eeps_cur_yr', 'description': '不可使用,仅供参考:Est Earnings Per Share Curr Year'} +{'id': 62745, 'data_set_name': '可以使用:anl69_saless_best_eeps_nxt_yr', 'description': '不可使用,仅供参考:Est Earnings Per Share Next Year'} +{'id': 62746, 'data_set_name': '可以使用:anl69_saless_best_fperiod_override', 'description': '不可使用,仅供参考:Fiscal Period Override'} +{'id': 62747, 'data_set_name': '可以使用:anl69_saless_best_sales', 'description': '不可使用,仅供参考:Sales'} +{'id': 62748, 'data_set_name': '可以使用:anl69_saless_best_sales_4wk_chg', 'description': '不可使用,仅供参考:Sales 4 Wk Chg'} +{'id': 62749, 'data_set_name': '可以使用:anl69_saless_best_sales_hi', 'description': '不可使用,仅供参考:Sales High'} +{'id': 62750, 'data_set_name': '可以使用:anl69_saless_best_sales_lo', 'description': '不可使用,仅供参考:Sales Low'} +{'id': 62751, 'data_set_name': '可以使用:anl69_saless_best_sales_numest', 'description': '不可使用,仅供参考:Sales # Ests'} +{'id': 62752, 'data_set_name': '可以使用:anl69_saless_best_sales_stddev', 'description': '不可使用,仅供参考:Sales Std Dev'} +{'id': 62753, 'data_set_name': '可以使用:anl69_saless_expected_report_dt', 'description': '不可使用,仅供参考:Expected Earnings Report Date'} +{'id': 62754, 'data_set_name': '可以使用:anl69_saless_expected_report_time', 'description': '不可使用,仅供参考:Expected Earnings Report Time'} +{'id': 136782, 'data_set_name': '可以使用:fnd17_alldelay1_margin5yr', 'description': '不可使用,仅供参考:Net Profit Margin - 5-year average'} +{'id': 137080, 'data_set_name': '可以使用:fnd17_margin5yr', 'description': '不可使用,仅供参考:Net Profit Margin - 5 year average'} +{'id': 137322, 'data_set_name': '可以使用:net_profit_margin_quarterly', 'description': '不可使用,仅供参考:Net profit margin for the most recent quarter, calculated as net income divided by total revenue.'} +{'id': 137328, 'data_set_name': '可以使用:pretax_margin_quarterly', 'description': '不可使用,仅供参考:Pretax margin for the most recent quarter, calculated as earnings before taxes divided by total revenue.'} +{'id': 137427, 'data_set_name': '可以使用:accounts_payable_current_2', 'description': '不可使用,仅供参考:[Quarterly] Construction in Progress - Gross'} +{'id': 137428, 'data_set_name': '可以使用:accounts_payable_deferred', 'description': '不可使用,仅供参考:[Quarterly] Pension Obligation - Domestic'} +{'id': 137429, 'data_set_name': '可以使用:accounts_payable_nontrade', 'description': '不可使用,仅供参考:Accounts payable not related to trade transactions.'} +{'id': 137430, 'data_set_name': '可以使用:accounts_payable_notes', 'description': '不可使用,仅供参考:Notes payable included in accounts payable.'} +{'id': 137431, 'data_set_name': '可以使用:accounts_payable_trade_creditors', 'description': '不可使用,仅供参考:Accounts payable to trade creditors.'} +{'id': 137432, 'data_set_name': '可以使用:accounts_payable_year_end', 'description': '不可使用,仅供参考:Accounts payable balance at fiscal year end.'} +{'id': 137433, 'data_set_name': '可以使用:accounts_receivable_current_assets', 'description': '不可使用,仅供参考:Accounts receivable included in current assets for the annual period.'} +{'id': 137434, 'data_set_name': '可以使用:accounts_receivable_gross', 'description': '不可使用,仅供参考:Gross amount of accounts receivable outstanding.'} +{'id': 137435, 'data_set_name': '可以使用:accounts_receivable_gross_2', 'description': '不可使用,仅供参考:[Quarterly] Accounts Receivable - Trade, Gross'} +{'id': 137436, 'data_set_name': '可以使用:accounts_receivable_total_5', 'description': '不可使用,仅供参考:Total accounts receivable as of the reporting date.'} +{'id': 137439, 'data_set_name': '可以使用:accounts_total_receivables_current', 'description': '不可使用,仅供参考:Total current receivables as reported in accounts.'} +{'id': 137440, 'data_set_name': '可以使用:accrued_acquisition_costs', 'description': '不可使用,仅供参考:Total value of accrued acquisition costs at the end of the period.'} +{'id': 137447, 'data_set_name': '可以使用:accrued_interest_payable', 'description': '不可使用,仅供参考:Interest expense that has been accrued but not yet paid.'} +{'id': 137448, 'data_set_name': '可以使用:accrued_interest_payable_2', 'description': '不可使用,仅供参考:Interest expense accrued but not yet paid.'} +{'id': 137479, 'data_set_name': '可以使用:capital_invested_accumulated_cost', 'description': '不可使用,仅供参考:Capital invested at accumulated cost for the interim period.'} +{'id': 137499, 'data_set_name': '可以使用:current_tax_payable', 'description': '不可使用,仅供参考:Current tax payable as of the reporting date.'} +{'id': 137500, 'data_set_name': '可以使用:customer_securities_investment', 'description': '不可使用,仅供参考:Customer securities held for investment purposes.'} +{'id': 137552, 'data_set_name': '可以使用:fnd23_acc_payable', 'description': '不可使用,仅供参考:accounts payable'} +{'id': 138949, 'data_set_name': '可以使用:long_term_tax_payable_2', 'description': '不可使用,仅供参考:Long-term tax liabilities due beyond one year.'} +{'id': 138950, 'data_set_name': '可以使用:long_term_tax_payable_4', 'description': '不可使用,仅供参考:Long-term tax payable reported in interim financials.'} +{'id': 138981, 'data_set_name': '可以使用:other_current_receivables', 'description': '不可使用,仅供参考:Other receivables classified as current assets.'} +{'id': 139028, 'data_set_name': '可以使用:retirement_benefit_cost', 'description': '不可使用,仅供参考:[Quarterly] Compensation Rate - Domestic'} +{'id': 139031, 'data_set_name': '可以使用:sales_revenue_total', 'description': '不可使用,仅供参考:Total revenue from sales for the period.'} +{'id': 139032, 'data_set_name': '可以使用:sales_revenue_total_3', 'description': '不可使用,仅供参考:Total sales revenue recognized during the period.'} +{'id': 139074, 'data_set_name': '可以使用:short_term_group_payables', 'description': '不可使用,仅供参考:Short-term payables to group companies.'} +{'id': 139099, 'data_set_name': '可以使用:short_term_notes_payable', 'description': '不可使用,仅供参考:Short-term notes payable outstanding at period end.'} +{'id': 139102, 'data_set_name': '可以使用:short_term_payables_other', 'description': '不可使用,仅供参考:[Quarterly] Total Plan Obligations'} +{'id': 139121, 'data_set_name': '可以使用:stock_dividends_payable_recorded', 'description': '不可使用,仅供参考:Stock dividends declared but not yet paid.'} +{'id': 139137, 'data_set_name': '可以使用:trade_payables_short_term', 'description': '不可使用,仅供参考:Short-term trade payables owed by the company.'} +{'id': 140148, 'data_set_name': '可以使用:fnd31_irttmsalesev', 'description': "不可使用,仅供参考:Industry-relative TTM Sales to Enterprise Value. It is defined as a stock's trailing 12-month sales-to-enterprise (SEV) value less the average of the SEVs of all stocks in the same industry deflated by the standard deviation of the SEVs of all stocks in the same relative universe."} +{'id': 140418, 'data_set_name': '可以使用:fnd72_pit_or_bs_a_bs_acct_payable', 'description': '不可使用,仅供参考:Accounts Payable'} +{'id': 140485, 'data_set_name': '可以使用:fnd72_pit_or_bs_q_bs_acct_payable', 'description': '不可使用,仅供参考:Accounts Payable'} +{'id': 140640, 'data_set_name': '可以使用:fnd72_pit_or_is_a_is_interest_cost_pension', 'description': '不可使用,仅供参考:The component of net pension expense that pertains to the interest cost on the projected benefit obligation of the pension'} +{'id': 140658, 'data_set_name': '可以使用:fnd72_pit_or_is_a_is_service_cost', 'description': '不可使用,仅供参考:The actuarial present value of pension benefits attributed by the pension benefit formula to employee service during a specific period'} +{'id': 140674, 'data_set_name': '可以使用:fnd72_pit_or_is_a_sales_rev_turn', 'description': '不可使用,仅供参考:Sales/Revenue/Turnover'} +{'id': 140728, 'data_set_name': '可以使用:fnd72_pit_or_is_q_sales_rev_turn', 'description': '不可使用,仅供参考:Sales/Revenue/Turnover'} +{'id': 140733, 'data_set_name': '可以使用:fnd72_s_pit_or_bs_q_1_bs_acct_payable', 'description': '不可使用,仅供参考:Accounts Payable'} +{'id': 140793, 'data_set_name': '可以使用:fnd72_s_pit_or_bs_q_bs_acct_payable', 'description': '不可使用,仅供参考:Accounts Payable'} +{'id': 140918, 'data_set_name': '可以使用:fnd72_s_pit_or_is_q_sales_rev_turn', 'description': '不可使用,仅供参考:Sales/Revenue/Turnover'} +{'id': 373212, 'data_set_name': '可以使用:transaction_cost_estimate', 'description': '不可使用,仅供参考:Estimated cost incurred when executing a trade.'} +{'id': 373213, 'data_set_name': '可以使用:transaction_cost_maximum', 'description': '不可使用,仅供参考:Maximum estimated transaction cost for a trade.'} +{'id': 373214, 'data_set_name': '可以使用:transaction_cost_median', 'description': '不可使用,仅供参考:Median estimated transaction cost for a trade.'} +{'id': 373215, 'data_set_name': '可以使用:transaction_cost_percentile_25', 'description': '不可使用,仅供参考:Estimated transaction cost at the 25th percentile for a trade.'} +{'id': 373218, 'data_set_name': '可以使用:company_customer_entity', 'description': '不可使用,仅供参考:Entity identified as a customer of the company.'} +{'id': 373220, 'data_set_name': '可以使用:customer_revenue_share_percent', 'description': '不可使用,仅供参考:Percentage of company revenue attributed to a specific customer.'} +========================= 数据字段结束 ======================================= + +以上数据字段和操作符, 按照Description说明组合, 但是每一个 alpha 组合的使用的数据字段和操作符不要过于集中, 在符合语法的情况下, 多尝试不同的组合 + +你再检查一下, 如果你使用了 +Operator abs does not support event inputs +Operator ts_mean does not support event inputs +Operator ts_scale does not support event inputs +Operator add does not support event inputs +Operator sign does not support event inputs +Operator greater does not support event inputs +Operator ts_av_diff does not support event inputs +Operator ts_quantile does not support event inputs +Operator ts_arg_min does not support event inputs +Operator divide does not support event inputs +Operator ts_corr does not support event inputs +Operator ts_decay_linear does not support event inputs +Operator ts_sum does not support event inputs +Operator ts_delay does not support event inputs +Operator ts_arg_max does not support event inputs +Operator ts_std_dev does not support event inputs +Operator ts_regression does not support event inputs +Operator ts_backfill does not support event inputs +Operator signed_power does not support event inputs +Operator ts_product does not support event inputs +Operator ts_zscore does not support event inputs +Operator group_rank does not support event inputs +Operator subtract does not support event inputs +Operator ts_delta does not support event inputs +Operator ts_rank does not support event inputs +Operator ts_count_nans does not support event inputs +Operator ts_covariance does not support event inputs +Operator multiply does not support event inputs +Operator if_else does not support event inputs +Operator group_neutralize does not support event inputs +Operator group_zscore does not support event inputs +Operator winsorize does not support event inputs +注意, 以上操作符不能使用事件类型的数据集, 以上操作符禁止使用事件类型的数据集!! \ No newline at end of file diff --git a/manual_prompt/2026/01/27/manual_prompt_20260127103636.txt b/manual_prompt/2026/01/27/manual_prompt_20260127103636.txt new file mode 100644 index 0000000..971105e --- /dev/null +++ b/manual_prompt/2026/01/27/manual_prompt_20260127103636.txt @@ -0,0 +1,734 @@ +专利引用时效性溢价 +在技术密集型行业中，专利引用的时效性（近期引用占总引用的比例）是衡量技术生命周期活力的关键信号。市场参与者普遍依赖专利累计引用总数进行估值，却系统性忽视引用的时间分布特征：近期引用占比高的公司，其技术仍处于活跃扩散与商业化阶段，未来创新产出与竞争优势被市场低估；而近期引用占比低但累计引用高的公司，技术可能已进入衰退期，当前估值隐含过度乐观预期。行为金融逻辑在于投资者注意力有限且存在代表性启发式偏差，难以有效整合动态引用时序信息。多头配置时效性比率高的标的，空头配置比率低的标的，可捕获市场对技术生命周期阶段定价不足产生的系统性alpha。 +核心指标为“专利引用时效性比率”，计算公司有效专利中过去3年内被引用次数占历史总引用次数的比例。数据源需包含专利公开号、引用日期及引用方信息；指标需按申万一级行业进行横截面中性化，并采用24个月滚动窗口进行时序z-score标准化。缺失值处理：无专利数据的公司以行业当期中位数填充，并生成二元缺失指示变量；引入“专利年龄中位数”验证技术新旧结构，“行业月度引用增长率”控制行业周期扰动，二者作为辅助变量用于因子稳健性检验与经济逻辑交叉验证。 + +*=========================================================================================* +输出格式： +输出必须是且仅是纯文本。 +每一行是一个完整、独立、语法正确的WebSim表达式。 +严禁任何形式的解释、编号、标点包裹（如引号）、Markdown格式或额外文本。 +===================== !!! 重点(输出方式) !!! ===================== +现在，请严格遵守以上所有规则，开始生成可立即在WebSim中运行的复合因子表达式。 +不要自行假设, 你需要用到的操作符 和 数据集, 必须从我提供给你的里面查找, 并严格按照里面的使用方法进行组合 +**输出格式**(一行一个表达式, 每个表达式中间需要添加一个空行, 只要表达式本身, 不需要赋值, 不要解释, 不需要序号, 也不要输出多余的东西)： +表达式 +表达式 +表达式 +... +表达式 +================================================================= +重申：请确保所有表达式都使用WorldQuant WebSim平台函数，不要使用pandas、numpy或其他Python库函数。输出必须是一行有效的WQ表达式。 +以下是我的账号有权限使用的操作符, 请严格按照操作符, 以及我提供的数据集, 进行生成,组合 50 个 alpha： +不要自行假设, 你需要用到的操作符 和 数据集, 必须从我提供给你的里面查找, 并严格按照里面的使用方法进行组合 +================================================================= +**操作符汇总 +**算术运算符 (Arithmetic): +abs(x) - 绝对值 +add(x, y, filter=false) - 加法 (x + y) +densify(x) - 分组字段稠密化 +divide(x, y) - 除法 (x / y) +inverse(x) - 倒数 (1/x) +log(x) - 自然对数 +max(x, y, ..) - 最大值 +min(x, y, ..) - 最小值 +multiply(x, y, filter=false) - 乘法 (x * y) +power(x, y) - 幂运算 (x^y) +reverse(x) - 取反 (-x) +sign(x) - 符号函数 +signed_power(x, y) - 保留符号的幂运算 +sqrt(x) - 平方根 +subtract(x, y, filter=false) - 减法 (x - y) +to_nan(x, value=0, reverse=false) - 值与NaN转换 +**逻辑运算符 (Logical): +and(input1, input2) - 逻辑与 +if_else(input1, input2, input3) - 条件判断 +input1 < input2 - 小于比较 +input1 <= input2 - 小于等于 +input1 == input2 - 等于比较 +input1 > input2 - 大于比较 +input1 >= input2 - 大于等于 +input1 != input2 - 不等于 +is_nan(input) - 是否为NaN +not(x) - 逻辑非 +or(input1, input2) - 逻辑或 +**时间序列运算符 (Time Series): +days_from_last_change(x) - 上次变化天数 +hump(x, hump=0.01) - 限制变化幅度 +jump_decay(x, d, sensitivity=0.5, force=0.1) - 跳跃衰减 +kth_element(x, d, k) - 第K个值 +last_diff_value(x, d) - 最后一个不同值 +ts_arg_max(x, d) - 最大值相对索引 +ts_arg_min(x, d) - 最小值相对索引 +ts_av_diff(x, d) - 与均值的差 +ts_backfill(x, lookback=d, k=1, ignore="NAN") - 回填 +ts_corr(x, y, d) - 时间序列相关性 +ts_count_nans(x, d) - NaN计数 +ts_covariance(y, x, d) - 协方差 +ts_decay_linear(x, d, dense=false) - 线性衰减 +ts_delay(x, d) - 延迟值 +ts_delta(x, d) - 差值 (x - 延迟值) +ts_max(x, d) - 时间序列最大值 +ts_mean(x, d) - 时间序列均值 +ts_min(x, d) - 时间序列最小值 +ts_product(x, d) - 时间序列乘积 +ts_quantile(x, d, driver="gaussian") - 分位数 +ts_rank(x, d, constant=0) - 时间序列排名 +ts_regression(y, x, d, lag=0, rettype=0) - 回归分析 +ts_scale(x, d, constant=0) - 时间序列缩放 +ts_std_dev(x, d) - 时间序列标准差 +ts_step(1) - 天数计数器 +ts_sum(x, d) - 时间序列求和 +ts_target_tvr_decay(x, lambda_min=0, lambda_max=1, target_tvr=0.1) - 目标换手率衰减 +ts_target_tvr_delta_limit(x, y, lambda_min=0, lambda_max=1, target_tvr=0.1) - 目标换手率差值限制 +ts_zscore(x, d) - 时间序列Z分数 +**横截面运算符 (Cross Sectional): +normalize(x, useStd=false, limit=0.0) - 标准化 +quantile(x, driver=gaussian, sigma=1.0) - 分位数转换 +rank(x, rate=2) - 排名 +scale(x, scale=1, longscale=1, shortscale=1) - 缩放 +scale_down(x, constant=0) - 按比例缩放 +vector_neut(x, y) - 向量中性化 +winsorize(x, std=4) - 缩尾处理 +zscore(x) - Z分数 +**向量运算符 (Vector): +vec_avg(x) - 向量均值 +vec_max(x) - 向量最大值 +vec_min(x) - 向量最小值 +vec_sum(x) - 向量求和 +**变换运算符 (Transformational): +bucket(rank(x), range="0,1,0.1" or buckets="2,5,6,7,10") - 分桶 +generate_stats(alpha) - 生成统计量 +trade_when(x, y, z) - 条件交易 +**分组运算符 (Group): +combo_a(alpha, nlength=250, mode='algo1') - 组合Alpha +group_backfill(x, group, d, std=4.0) - 分组回填 +group_cartesian_product(g1, g2) - 笛卡尔积分组 +group_max(x, group) - 分组最大值 +group_mean(x, weight, group) - 分组均值 +group_min(x, group) - 分组最小值 +group_neutralize(x, group) - 分组中性化 +group_rank(x, group) - 分组排名 +group_scale(x, group) - 分组缩放 +group_zscore(x, group) - 分组Z分数 +**特殊运算符 (Special): +in - 包含判断 +self_corr(input) - 自相关性 +universe_size - 宇宙大小 +**归约运算符 (Reduce): +reduce_avg(input, threshold=0) - 平均值归约 +reduce_choose(input, nth, ignoreNan=true) - 选择归约 +reduce_count(input, threshold) - 计数归约 +reduce_ir(input) - IR归约 +reduce_kurtosis(input) - 峰度归约 +reduce_max(input) - 最大值归约 +reduce_min(input) - 最小值归约 +reduce_norm(input) - 范数归约 +reduce_percentage(input, percentage=0.5) - 百分比归约 +reduce_powersum(input, constant=2, precise=false) - 幂和归约 +reduce_range(input) - 范围归约 +reduce_skewness(input) - 偏度归约 +reduce_stddev(input, threshold=0) - 标准差归约 +reduce_sum(input) - 求和归约 + +以下是我的账号有权限使用的操作符, 请严格按照操作符, 进行生成,组合因子 + +========================= 操作符开始 ======================================= +注意: Operator: 后面的是操作符(是可以使用的), +Description: 此字段后面的是操作符对应的描述或使用说明(禁止使用, 仅供参考), Description字段后面的内容是使用说明, 不是操作符 +特别注意!!!! 必须按照操作符字段Operator的使用说明生成 alphaOperator: abs(x) +Description: Absolute value of x +Operator: add(x, y, filter = false) +Description: Add all inputs (at least 2 inputs required). If filter = true, filter all input NaN to 0 before adding +Operator: densify(x) +Description: Converts a grouping field of many buckets into lesser number of only available buckets so as to make working with grouping fields computationally efficient +Operator: divide(x, y) +Description: x / y +Operator: inverse(x) +Description: 1 / x +Operator: log(x) +Description: Natural logarithm. For example: Log(high/low) uses natural logarithm of high/low ratio as stock weights. +Operator: max(x, y, ..) +Description: Maximum value of all inputs. At least 2 inputs are required +Operator: min(x, y ..) +Description: Minimum value of all inputs. At least 2 inputs are required +Operator: multiply(x ,y, ... , filter=false) +Description: Multiply all inputs. At least 2 inputs are required. Filter sets the NaN values to 1 +Operator: power(x, y) +Description: x ^ y +Operator: reverse(x) +Description: - x +Operator: sign(x) +Description: if input > 0, return 1; if input < 0, return -1; if input = 0, return 0; if input = NaN, return NaN; +Operator: signed_power(x, y) +Description: x raised to the power of y such that final result preserves sign of x +Operator: sqrt(x) +Description: Square root of x +Operator: subtract(x, y, filter=false) +Description: x-y. If filter = true, filter all input NaN to 0 before subtracting +Operator: and(input1, input2) +Description: Logical AND operator, returns true if both operands are true and returns false otherwise +Operator: if_else(input1, input2, input 3) +Description: If input1 is true then return input2 else return input3. +Operator: input1 < input2 +Description: If input1 < input2 return true, else return false +Operator: input1 <= input2 +Description: Returns true if input1 <= input2, return false otherwise +Operator: input1 == input2 +Description: Returns true if both inputs are same and returns false otherwise +Operator: input1 > input2 +Description: Logic comparison operators to compares two inputs +Operator: input1 >= input2 +Description: Returns true if input1 >= input2, return false otherwise +Operator: input1!= input2 +Description: Returns true if both inputs are NOT the same and returns false otherwise +Operator: is_nan(input) +Description: If (input == NaN) return 1 else return 0 +Operator: not(x) +Description: Returns the logical negation of x. If x is true (1), it returns false (0), and if input is false (0), it returns true (1). +Operator: or(input1, input2) +Description: Logical OR operator returns true if either or both inputs are true and returns false otherwise +Operator: days_from_last_change(x) +Description: Amount of days since last change of x +Operator: hump(x, hump = 0.01) +Description: Limits amount and magnitude of changes in input (thus reducing turnover) +Operator: kth_element(x, d, k) +Description: Returns K-th value of input by looking through lookback days. This operator can be used to backfill missing data if k=1 +Operator: last_diff_value(x, d) +Description: Returns last x value not equal to current x value from last d days +Operator: ts_arg_max(x, d) +Description: Returns the relative index of the max value in the time series for the past d days. If the current day has the max value for the past d days, it returns 0. If previous day has the max value for the past d days, it returns 1 +Operator: ts_arg_min(x, d) +Description: Returns the relative index of the min value in the time series for the past d days; If the current day has the min value for the past d days, it returns 0; If previous day has the min value for the past d days, it returns 1. +Operator: ts_av_diff(x, d) +Description: Returns x - tsmean(x, d), but deals with NaNs carefully. That is NaNs are ignored during mean computation +Operator: ts_backfill(x,lookback = d, k=1, ignore="NAN") +Description: Backfill is the process of replacing the NAN or 0 values by a meaningful value (i.e., a first non-NaN value) +Operator: ts_corr(x, y, d) +Description: Returns correlation of x and y for the past d days +Operator: ts_count_nans(x ,d) +Description: Returns the number of NaN values in x for the past d days +Operator: ts_covariance(y, x, d) +Description: Returns covariance of y and x for the past d days +Operator: ts_decay_linear(x, d, dense = false) +Description: Returns the linear decay on x for the past d days. Dense parameter=false means operator works in sparse mode and we treat NaN as 0. In dense mode we do not. +Operator: ts_delay(x, d) +Description: Returns x value d days ago +Operator: ts_delta(x, d) +Description: Returns x - ts_delay(x, d) +Operator: ts_mean(x, d) +Description: Returns average value of x for the past d days. +Operator: ts_product(x, d) +Description: Returns product of x for the past d days +Operator: ts_quantile(x,d, driver="gaussian" ) +Description: It calculates ts_rank and apply to its value an inverse cumulative density function from driver distribution. Possible values of driver (optional ) are "gaussian", "uniform", "cauchy" distribution where "gaussian" is the default. +Operator: ts_rank(x, d, constant = 0) +Description: Rank the values of x for each instrument over the past d days, then return the rank of the current value + constant. If not specified, by default, constant = 0. +Operator: ts_regression(y, x, d, lag = 0, rettype = 0) +Description: Returns various parameters related to regression function +Operator: ts_scale(x, d, constant = 0) +Description: Returns (x - ts_min(x, d)) / (ts_max(x, d) - ts_min(x, d)) + constant. This operator is similar to scale down operator but acts in time series space +Operator: ts_std_dev(x, d) +Description: Returns standard deviation of x for the past d days +Operator: ts_step(1) +Description: Returns days' counter +Operator: ts_sum(x, d) +Description: Sum values of x for the past d days. +Operator: ts_zscore(x, d) +Description: Z-score is a numerical measurement that describes a value's relationship to the mean of a group of values. Z-score is measured in terms of standard deviations from the mean: (x - tsmean(x,d)) / tsstddev(x,d). This operator may help reduce outliers and drawdown. +Operator: normalize(x, useStd = false, limit = 0.0) +Description: Calculates the mean value of all valid alpha values for a certain date, then subtracts that mean from each element +Operator: quantile(x, driver = gaussian, sigma = 1.0) +Description: Rank the raw vector, shift the ranked Alpha vector, apply distribution (gaussian, cauchy, uniform). If driver is uniform, it simply subtract each Alpha value with the mean of all Alpha values in the Alpha vector +Operator: rank(x, rate=2) +Description: Ranks the input among all the instruments and returns an equally distributed number between 0.0 and 1.0. For precise sort, use the rate as 0 +Operator: scale(x, scale=1, longscale=1, shortscale=1) +Description: Scales input to booksize. We can also scale the long positions and short positions to separate scales by mentioning additional parameters to the operator +Operator: winsorize(x, std=4) +Description: Winsorizes x to make sure that all values in x are between the lower and upper limits, which are specified as multiple of std. +Operator: zscore(x) +Description: Z-score is a numerical measurement that describes a value's relationship to the mean of a group of values. Z-score is measured in terms of standard deviations from the mean +Operator: vec_avg(x) +Description: Taking mean of the vector field x +Operator: vec_sum(x) +Description: Sum of vector field x +Operator: bucket(rank(x), range="0, 1, 0.1" or buckets = "2,5,6,7,10") +Description: Convert float values into indexes for user-specified buckets. Bucket is useful for creating group values, which can be passed to GROUP as input +Operator: trade_when(x, y, z) +Description: Used in order to change Alpha values only under a specified condition and to hold Alpha values in other cases. It also allows to close Alpha positions (assign NaN values) under a specified condition +Operator: group_backfill(x, group, d, std = 4.0) +Description: If a certain value for a certain date and instrument is NaN, from the set of same group instruments, calculate winsorized mean of all non-NaN values over last d days +Operator: group_mean(x, weight, group) +Description: All elements in group equals to the mean +Operator: group_neutralize(x, group) +Description: Neutralizes Alpha against groups. These groups can be subindustry, industry, sector, country or a constant +Operator: group_rank(x, group) +Description: Each elements in a group is assigned the corresponding rank in this group +Operator: group_scale(x, group) +Description: Normalizes the values in a group to be between 0 and 1. (x - groupmin) / (groupmax - groupmin) +Operator: group_zscore(x, group) +Description: Calculates group Z-score - numerical measurement that describes a value's relationship to the mean of a group of values. Z-score is measured in terms of standard deviations from the mean. zscore = (data - mean) / stddev of x for each instrument within its group. +========================= 操作符结束 ======================================= + +========================= 数据字段开始 ======================================= +注意: data_set_name: 后面的是数据字段(可以使用), description: 此字段后面的是数据字段对应的描述或使用说明(不能使用) + +{'id': 60316, 'data_set_name': '可以使用:anl10_bpspast_det_estage_6399', 'description': '不可使用,仅供参考:Age of book value per share estimates in days'} +{'id': 60352, 'data_set_name': '可以使用:anl10_cpspast_det_estage_6349', 'description': '不可使用,仅供参考:Age of cash per share estimates in days'} +{'id': 60363, 'data_set_name': '可以使用:anl10_dpspast_det_estage_6223', 'description': '不可使用,仅供参考:Age of dividends per share estimates in days'} +{'id': 60370, 'data_set_name': '可以使用:anl10_ebspast_det_estage_6210', 'description': '不可使用,仅供参考:Age of earnings before others estimates in days'} +{'id': 60392, 'data_set_name': '可以使用:anl10_entpast_det_estage_6314', 'description': '不可使用,仅供参考:Age of earnings net taxes estimates in days'} +{'id': 60432, 'data_set_name': '可以使用:anl10_fcfpast_det_estage_6380', 'description': '不可使用,仅供参考:Age of free cash flow estimates in days'} +{'id': 60472, 'data_set_name': '可以使用:anl10_grmpast_det_estage_6320', 'description': '不可使用,仅供参考:Age of gross margin estimates in days'} +{'id': 60483, 'data_set_name': '可以使用:anl10_roapast_det_estage_6330', 'description': '不可使用,仅供参考:Age of return on assets estimates in days'} +{'id': 61192, 'data_set_name': '可以使用:anl14_median_bvps_fp1', 'description': '不可使用,仅供参考:Median of Estimations of Book Value Per Share - upcoming quarter'} +{'id': 61193, 'data_set_name': '可以使用:anl14_median_bvps_fp2', 'description': '不可使用,仅供参考:Median of Estimations of Book Value Per Share - Upcoming 2 Quarters'} +{'id': 61194, 'data_set_name': '可以使用:anl14_median_bvps_fp3', 'description': '不可使用,仅供参考:Median of Estimations of Book Value Per Share - Upcoming 3 Quarters'} +{'id': 61195, 'data_set_name': '可以使用:anl14_median_bvps_fp4', 'description': '不可使用,仅供参考:Median of Estimations of Book Value Per Share - upcoming 4 quarters'} +{'id': 61196, 'data_set_name': '可以使用:anl14_median_bvps_fp5', 'description': '不可使用,仅供参考:Median of Estimations of Book Value Per Share - upcoming 5 quarters'} +{'id': 61197, 'data_set_name': '可以使用:anl14_median_bvps_fy1', 'description': '不可使用,仅供参考:Median of Estimations of Book Value Per Share - upcoming year'} +{'id': 61198, 'data_set_name': '可以使用:anl14_median_bvps_fy2', 'description': '不可使用,仅供参考:Median of Estimations of Book Value Per Share - upcoming 2 years'} +{'id': 61199, 'data_set_name': '可以使用:anl14_median_bvps_fy3', 'description': '不可使用,仅供参考:Median of Estimations of Book Value Per Share - Upcoming 3 Years'} +{'id': 61200, 'data_set_name': '可以使用:anl14_median_bvps_fy4', 'description': '不可使用,仅供参考:Median of estimations of book value per share - upcoming 4 years'} +{'id': 61201, 'data_set_name': '可以使用:anl14_median_bvps_fy5', 'description': '不可使用,仅供参考:Median of Estimations of Book Value Per Share - upcoming 5 years'} +{'id': 61202, 'data_set_name': '可以使用:anl14_median_capex_fp1', 'description': '不可使用,仅供参考:Median of Estimations of Capital Expenditures - Upcoming Quarter'} +{'id': 61203, 'data_set_name': '可以使用:anl14_median_capex_fp2', 'description': '不可使用,仅供参考:Median of Estimations of Capital Expenditures - upcoming 2 quarters'} +{'id': 61204, 'data_set_name': '可以使用:anl14_median_capex_fp3', 'description': '不可使用,仅供参考:Median of estimations of capital expenditures - upcoming 3 quarters'} +{'id': 61205, 'data_set_name': '可以使用:anl14_median_capex_fp4', 'description': '不可使用,仅供参考:Median of Estimations of Capital Expenditures - upcoming 4 quarters'} +{'id': 61206, 'data_set_name': '可以使用:anl14_median_capex_fp5', 'description': '不可使用,仅供参考:Median of Estimations of Capital Expenditures - upcoming 5 quarters'} +{'id': 61207, 'data_set_name': '可以使用:anl14_median_capex_fy1', 'description': '不可使用,仅供参考:Median of estimations of Capital Expenditures - upcoming year'} +{'id': 61208, 'data_set_name': '可以使用:anl14_median_capex_fy2', 'description': '不可使用,仅供参考:Median of Estimations of Capital Expenditures - upcoming 2 years'} +{'id': 61209, 'data_set_name': '可以使用:anl14_median_capex_fy3', 'description': '不可使用,仅供参考:Median of estimations of capital expenditures - upcoming 3 years'} +{'id': 61210, 'data_set_name': '可以使用:anl14_median_capex_fy4', 'description': '不可使用,仅供参考:Median of Estimations of Capital Expenditures - upcoming 4 years'} +{'id': 61211, 'data_set_name': '可以使用:anl14_median_capex_fy5', 'description': '不可使用,仅供参考:Median of estimations of capital expenditures - upcoming 5 years'} +{'id': 61212, 'data_set_name': '可以使用:anl14_median_cfps_fp1', 'description': '不可使用,仅供参考:Median of Estimations of Cash Flow Per Share - upcoming quarter'} +{'id': 61213, 'data_set_name': '可以使用:anl14_median_cfps_fp2', 'description': '不可使用,仅供参考:Median of estimations of cash flow per share - upcoming 2 quarters'} +{'id': 61214, 'data_set_name': '可以使用:anl14_median_cfps_fp3', 'description': '不可使用,仅供参考:Median of estimations of cash flow per share - upcoming 3 quarters'} +{'id': 61215, 'data_set_name': '可以使用:anl14_median_cfps_fp4', 'description': '不可使用,仅供参考:Median of Estimations of Cash Flow Per Share - upcoming 4 quarters'} +{'id': 61216, 'data_set_name': '可以使用:anl14_median_cfps_fy1', 'description': '不可使用,仅供参考:Median of Estimations of Cash Flow Per Share - Upcoming Year'} +{'id': 61217, 'data_set_name': '可以使用:anl14_median_cfps_fy2', 'description': '不可使用,仅供参考:Median of Estimations of Cash Flow Per Share - upcoming 2 years'} +{'id': 61218, 'data_set_name': '可以使用:anl14_median_cfps_fy3', 'description': '不可使用,仅供参考:Median of estimations of cash flow per share - upcoming 3 years'} +{'id': 61219, 'data_set_name': '可以使用:anl14_median_cfps_fy4', 'description': '不可使用,仅供参考:Median of Estimations of Cash Flow Per Share - upcoming 4 years'} +{'id': 61220, 'data_set_name': '可以使用:anl14_median_div_fp1', 'description': '不可使用,仅供参考:Median of Estimations of Dividend - upcoming quarter'} +{'id': 61221, 'data_set_name': '可以使用:anl14_median_div_fp2', 'description': '不可使用,仅供参考:Median of Estimations of Dividend - upcoming 2 quarters'} +{'id': 61222, 'data_set_name': '可以使用:anl14_median_div_fp3', 'description': '不可使用,仅供参考:Median of Estimations of Dividend - upcoming 3 quarters'} +{'id': 61223, 'data_set_name': '可以使用:anl14_median_div_fp4', 'description': '不可使用,仅供参考:Median of estimations of dividend - upcoming 4 quarters'} +{'id': 61224, 'data_set_name': '可以使用:anl14_median_div_fp5', 'description': '不可使用,仅供参考:Median of Estimations of Dividend - upcoming 5 quarters'} +{'id': 61225, 'data_set_name': '可以使用:anl14_median_div_fy1', 'description': '不可使用,仅供参考:Median of estimations of dividend - upcoming year'} +{'id': 61226, 'data_set_name': '可以使用:anl14_median_div_fy2', 'description': '不可使用,仅供参考:Median of estimations of Dividend - upcoming 2 years'} +{'id': 61227, 'data_set_name': '可以使用:anl14_median_div_fy3', 'description': '不可使用,仅供参考:Median of estimations of dividend - upcoming 3 years'} +{'id': 61228, 'data_set_name': '可以使用:anl14_median_div_fy4', 'description': '不可使用,仅供参考:Median of Estimations of Dividend - Upcoming 4 Years'} +{'id': 61229, 'data_set_name': '可以使用:anl14_median_div_fy5', 'description': '不可使用,仅供参考:Median of Estimations of Dividend - Upcoming 5 Years'} +{'id': 61230, 'data_set_name': '可以使用:anl14_median_ebit_fp1', 'description': '不可使用,仅供参考:Median of Estimations of Earnings Before Interest & Taxes - Upcoming Quarter'} +{'id': 61231, 'data_set_name': '可以使用:anl14_median_ebit_fp2', 'description': '不可使用,仅供参考:Median of Estimations of Earnings Before Interest & Taxes - Upcoming 2 Quarters'} +{'id': 61232, 'data_set_name': '可以使用:anl14_median_ebit_fp3', 'description': '不可使用,仅供参考:Median of estimations of earnings before interest & taxes - upcoming 3 quarters'} +{'id': 61233, 'data_set_name': '可以使用:anl14_median_ebit_fp4', 'description': '不可使用,仅供参考:Median of estimations of earnings before interest & taxes - upcoming 4 quarters'} +{'id': 61234, 'data_set_name': '可以使用:anl14_median_ebit_fp5', 'description': '不可使用,仅供参考:Median of estimations of earnings before interest & taxes - upcoming 5 quarters'} +{'id': 61235, 'data_set_name': '可以使用:anl14_median_ebit_fy1', 'description': '不可使用,仅供参考:Median of estimations of earnings before interest & taxes - upcoming year'} +{'id': 61236, 'data_set_name': '可以使用:anl14_median_ebit_fy2', 'description': '不可使用,仅供参考:Median of estimations of earnings before interest & taxes - upcoming 2 years'} +{'id': 61237, 'data_set_name': '可以使用:anl14_median_ebit_fy3', 'description': '不可使用,仅供参考:Median of Estimations of Earnings Before Interest & Taxes - upcoming 3 years'} +{'id': 61238, 'data_set_name': '可以使用:anl14_median_ebit_fy4', 'description': '不可使用,仅供参考:Median of estimations of earnings before interest & taxes - upcoming 4 years'} +{'id': 61239, 'data_set_name': '可以使用:anl14_median_ebit_fy5', 'description': '不可使用,仅供参考:Median of Estimations of Earnings Before Interest & Taxes - upcoming 5 years'} +{'id': 61240, 'data_set_name': '可以使用:anl14_median_ebitda_fp1', 'description': '不可使用,仅供参考:Median of Estimations of Earnings Before Interest, Taxes, Depreciation & Amortization - upcoming quarter'} +{'id': 61241, 'data_set_name': '可以使用:anl14_median_ebitda_fp2', 'description': '不可使用,仅供参考:Median of estimations of earnings before interest, taxes, depreciation & amortization - upcoming 2 quarters'} +{'id': 61242, 'data_set_name': '可以使用:anl14_median_ebitda_fp3', 'description': '不可使用,仅供参考:Median of Estimations of Earnings Before Interest, Taxes, Depreciation & Amortization - upcoming 3 quarters'} +{'id': 61243, 'data_set_name': '可以使用:anl14_median_ebitda_fp4', 'description': '不可使用,仅供参考:Median of Estimations of Earnings Before Interest, Taxes, Depreciation & Amortization - upcoming 4 quarters'} +{'id': 61244, 'data_set_name': '可以使用:anl14_median_ebitda_fp5', 'description': '不可使用,仅供参考:Median of estimations of Earnings Before Interest, Taxes, Depreciation & Amortization - upcoming 5 quarters'} +{'id': 61245, 'data_set_name': '可以使用:anl14_median_ebitda_fy1', 'description': '不可使用,仅供参考:Median of Estimations of Earnings Before Interest, Taxes, Depreciation & Amortization - Upcoming Year'} +{'id': 61246, 'data_set_name': '可以使用:anl14_median_ebitda_fy2', 'description': '不可使用,仅供参考:Median of estimations of Earnings Before Interest, Taxes, Depreciation & Amortization - upcoming 2 years'} +{'id': 61247, 'data_set_name': '可以使用:anl14_median_ebitda_fy3', 'description': '不可使用,仅供参考:Median of estimations of earnings before interest, taxes, depreciation & amortization - upcoming 3 years'} +{'id': 61248, 'data_set_name': '可以使用:anl14_median_ebitda_fy4', 'description': '不可使用,仅供参考:Median of Estimations of Earnings Before Interest, Taxes, Depreciation & Amortization - upcoming 4 years'} +{'id': 61249, 'data_set_name': '可以使用:anl14_median_ebitda_fy5', 'description': '不可使用,仅供参考:Median of Estimations of Earnings Before Interest, Taxes, Depreciation & Amortization - upcoming 5 years'} +{'id': 61250, 'data_set_name': '可以使用:anl14_median_eps_fp1', 'description': '不可使用,仅供参考:Median of Estimations of Earnings Per Share - upcoming quarter'} +{'id': 61251, 'data_set_name': '可以使用:anl14_median_eps_fp2', 'description': '不可使用,仅供参考:Median of estimations of earnings per share - upcoming 2 quarters'} +{'id': 61252, 'data_set_name': '可以使用:anl14_median_eps_fp3', 'description': '不可使用,仅供参考:Median of estimations of Earnings Per Share - upcoming 3 quarters'} +{'id': 61253, 'data_set_name': '可以使用:anl14_median_eps_fp4', 'description': '不可使用,仅供参考:Median of Estimations of Earnings Per Share - upcoming 4 quarters'} +{'id': 61254, 'data_set_name': '可以使用:anl14_median_eps_fp5', 'description': '不可使用,仅供参考:Median of estimations of earnings per share - upcoming 5 quarters'} +{'id': 61255, 'data_set_name': '可以使用:anl14_median_eps_fy1', 'description': '不可使用,仅供参考:Median of Estimations of Earnings Per Share - upcoming year'} +{'id': 61256, 'data_set_name': '可以使用:anl14_median_eps_fy2', 'description': '不可使用,仅供参考:Median of Estimations of Earnings Per Share - upcoming 2 years'} +{'id': 61257, 'data_set_name': '可以使用:anl14_median_eps_fy3', 'description': '不可使用,仅供参考:Median of Estimations of Earnings Per Share - Upcoming 3 Years'} +{'id': 61258, 'data_set_name': '可以使用:anl14_median_eps_fy4', 'description': '不可使用,仅供参考:Median of estimations of earnings per share - upcoming 4 years'} +{'id': 61259, 'data_set_name': '可以使用:anl14_median_eps_fy5', 'description': '不可使用,仅供参考:Median of estimations of earnings per share - upcoming 5 years'} +{'id': 61260, 'data_set_name': '可以使用:anl14_median_epsrep_fp1', 'description': '不可使用,仅供参考:Median of Estimations of Reported Earnings Per Share - upcoming quarter'} +{'id': 61261, 'data_set_name': '可以使用:anl14_median_epsrep_fp2', 'description': '不可使用,仅供参考:Median of estimations of reported earnings per share - upcoming 2 quarters'} +{'id': 61262, 'data_set_name': '可以使用:anl14_median_epsrep_fp3', 'description': '不可使用,仅供参考:Median of estimations of Reported Earnings Per Share - upcoming 3 quarters'} +{'id': 61263, 'data_set_name': '可以使用:anl14_median_epsrep_fp4', 'description': '不可使用,仅供参考:Median of Estimations of Reported Earnings Per Share - upcoming 4 quarters'} +{'id': 61264, 'data_set_name': '可以使用:anl14_median_epsrep_fp5', 'description': '不可使用,仅供参考:Median of estimations of reported earnings per share - upcoming 5 quarters'} +{'id': 61265, 'data_set_name': '可以使用:anl14_median_epsrep_fy1', 'description': '不可使用,仅供参考:Median of estimations of reported earnings per share - upcoming year'} +{'id': 61266, 'data_set_name': '可以使用:anl14_median_epsrep_fy2', 'description': '不可使用,仅供参考:Median of Estimations of Reported Earnings Per Share - upcoming 2 years'} +{'id': 61267, 'data_set_name': '可以使用:anl14_median_epsrep_fy3', 'description': '不可使用,仅供参考:Median of Estimations of Reported Earnings Per Share - upcoming 3 years'} +{'id': 61268, 'data_set_name': '可以使用:anl14_median_epsrep_fy4', 'description': '不可使用,仅供参考:Median of Estimations of Reported Earnings Per Share - upcoming 4 years'} +{'id': 61269, 'data_set_name': '可以使用:anl14_median_epsrep_fy5', 'description': '不可使用,仅供参考:Median of Estimations of Reported Earnings Per Share - upcoming 5 years'} +{'id': 61270, 'data_set_name': '可以使用:anl14_median_nav_fy1', 'description': '不可使用,仅供参考:Median of estimations of Net Asset Value - upcoming year'} +{'id': 61271, 'data_set_name': '可以使用:anl14_median_nav_fy2', 'description': '不可使用,仅供参考:Median of estimations of net asset value - upcoming 2 years'} +{'id': 61272, 'data_set_name': '可以使用:anl14_median_nav_fy3', 'description': '不可使用,仅供参考:Median of Estimations of Net Asset Value - upcoming 3 years'} +{'id': 61273, 'data_set_name': '可以使用:anl14_median_ndebt_fp1', 'description': '不可使用,仅供参考:Median of Estimations of Net Debt - upcoming quarter'} +{'id': 61274, 'data_set_name': '可以使用:anl14_median_ndebt_fp2', 'description': '不可使用,仅供参考:Median of Estimations of Net Debt - Upcoming 2 Quarters'} +{'id': 61275, 'data_set_name': '可以使用:anl14_median_ndebt_fp3', 'description': '不可使用,仅供参考:Median of estimations of net debt - upcoming 3 quarters'} +{'id': 61276, 'data_set_name': '可以使用:anl14_median_ndebt_fp4', 'description': '不可使用,仅供参考:Median of Estimations of Net Debt - upcoming 4 quarters'} +{'id': 61277, 'data_set_name': '可以使用:anl14_median_ndebt_fy1', 'description': '不可使用,仅供参考:Median of estimations of net debt - upcoming year'} +{'id': 61278, 'data_set_name': '可以使用:anl14_median_ndebt_fy2', 'description': '不可使用,仅供参考:Median of estimations of Net Debt - upcoming 2 years'} +{'id': 61279, 'data_set_name': '可以使用:anl14_median_ndebt_fy3', 'description': '不可使用,仅供参考:Median of estimations of net debt - upcoming 3 years'} +{'id': 61280, 'data_set_name': '可以使用:anl14_median_ndebt_fy4', 'description': '不可使用,仅供参考:Median of Estimations of Net Debt - upcoming 4 years'} +{'id': 61281, 'data_set_name': '可以使用:anl14_median_ndebt_fy5', 'description': '不可使用,仅供参考:Median of Estimations of Net Debt - upcoming 5 years'} +{'id': 61282, 'data_set_name': '可以使用:anl14_median_ntp_fp1', 'description': '不可使用,仅供参考:Median of estimations of net profit - upcoming quarter'} +{'id': 61283, 'data_set_name': '可以使用:anl14_median_ntp_fp2', 'description': '不可使用,仅供参考:Median of estimations of net profit - upcoming 2 quarters'} +{'id': 61284, 'data_set_name': '可以使用:anl14_median_ntp_fp3', 'description': '不可使用,仅供参考:Median of estimations of net profit - upcoming 3 quarters'} +{'id': 61285, 'data_set_name': '可以使用:anl14_median_ntp_fp4', 'description': '不可使用,仅供参考:Median of estimations of net profit - upcoming 4 quarters'} +{'id': 61286, 'data_set_name': '可以使用:anl14_median_ntp_fp5', 'description': '不可使用,仅供参考:Median of estimations of net profit - upcoming 5 quarters'} +{'id': 61287, 'data_set_name': '可以使用:anl14_median_ntp_fy1', 'description': '不可使用,仅供参考:Median of Estimations of Net Profit - upcoming year'} +{'id': 61288, 'data_set_name': '可以使用:anl14_median_ntp_fy2', 'description': '不可使用,仅供参考:Median of estimations of Net Profit - upcoming 2 years'} +{'id': 61289, 'data_set_name': '可以使用:anl14_median_ntp_fy3', 'description': '不可使用,仅供参考:Median of Estimations of Net Profit - Upcoming 3 Years'} +{'id': 61290, 'data_set_name': '可以使用:anl14_median_ntp_fy4', 'description': '不可使用,仅供参考:Median of estimations of net profit - upcoming 4 years'} +{'id': 61291, 'data_set_name': '可以使用:anl14_median_ntp_fy5', 'description': '不可使用,仅供参考:Median of Estimations of Net Profit - upcoming 5 years'} +{'id': 61292, 'data_set_name': '可以使用:anl14_median_ntprep_fp1', 'description': '不可使用,仅供参考:Median of Estimations of Reported Net Profit - upcoming quarter'} +{'id': 61293, 'data_set_name': '可以使用:anl14_median_ntprep_fp2', 'description': '不可使用,仅供参考:Median of Estimations of Reported Net Profit - upcoming 2 quarters'} +{'id': 61294, 'data_set_name': '可以使用:anl14_median_ntprep_fp3', 'description': '不可使用,仅供参考:Median of estimations of reported net profit - upcoming 3 quarters'} +{'id': 61295, 'data_set_name': '可以使用:anl14_median_ntprep_fp4', 'description': '不可使用,仅供参考:Median of estimations of Reported Net Profit - upcoming 4 quarters'} +{'id': 61296, 'data_set_name': '可以使用:anl14_median_ntprep_fp5', 'description': '不可使用,仅供参考:Median of estimations of reported net profit - upcoming 5 quarters'} +{'id': 61297, 'data_set_name': '可以使用:anl14_median_ntprep_fy1', 'description': '不可使用,仅供参考:Median of estimations of reported net profit - upcoming year'} +{'id': 61298, 'data_set_name': '可以使用:anl14_median_ntprep_fy2', 'description': '不可使用,仅供参考:Median of estimations of reported net profit - upcoming 2 years'} +{'id': 61299, 'data_set_name': '可以使用:anl14_median_ntprep_fy3', 'description': '不可使用,仅供参考:Median of Estimations of Reported Net Profit - upcoming 3 years'} +{'id': 61300, 'data_set_name': '可以使用:anl14_median_ntprep_fy4', 'description': '不可使用,仅供参考:Median of estimations of reported net profit - upcoming 4 years'} +{'id': 61301, 'data_set_name': '可以使用:anl14_median_ntprep_fy5', 'description': '不可使用,仅供参考:Median of Estimations of Reported Net Profit - upcoming 5 years'} +{'id': 61302, 'data_set_name': '可以使用:anl14_median_opp_fp1', 'description': '不可使用,仅供参考:Median of Estimations of Operating Profit - upcoming quarter'} +{'id': 61303, 'data_set_name': '可以使用:anl14_median_opp_fy1', 'description': '不可使用,仅供参考:Median of Estimations of Operating Profit - upcoming year'} +{'id': 61304, 'data_set_name': '可以使用:anl14_median_opp_fy2', 'description': '不可使用,仅供参考:Median of Estimations of Operating Profit - upcoming 2 years'} +{'id': 61305, 'data_set_name': '可以使用:anl14_median_opp_fy3', 'description': '不可使用,仅供参考:Median of Estimations of Operating Profit - upcoming 3 years'} +{'id': 61306, 'data_set_name': '可以使用:anl14_median_ptp_fp1', 'description': '不可使用,仅供参考:Median of estimations of pretax profit - upcoming quarter'} +{'id': 61307, 'data_set_name': '可以使用:anl14_median_ptp_fp2', 'description': '不可使用,仅供参考:Median of Estimations of Pretax Profit - upcoming 2 quarters'} +{'id': 61308, 'data_set_name': '可以使用:anl14_median_ptp_fp3', 'description': '不可使用,仅供参考:Median of Estimations of Pretax Profit - upcoming 3 quarters'} +{'id': 61309, 'data_set_name': '可以使用:anl14_median_ptp_fy1', 'description': '不可使用,仅供参考:Median of estimations of pretax profit - upcoming year'} +{'id': 61310, 'data_set_name': '可以使用:anl14_median_ptp_fy2', 'description': '不可使用,仅供参考:Median of estimations of pretax profit - upcoming 2 years'} +{'id': 61311, 'data_set_name': '可以使用:anl14_median_ptp_fy3', 'description': '不可使用,仅供参考:Median of Estimations of Pretax Profit - upcoming 3 years'} +{'id': 61312, 'data_set_name': '可以使用:anl14_median_ptp_fy4', 'description': '不可使用,仅供参考:Median of estimations of Pretax Profit - upcoming 4 years'} +{'id': 61313, 'data_set_name': '可以使用:anl14_median_ptp_fy5', 'description': '不可使用,仅供参考:Median of estimations of pretax profit - upcoming 5 years'} +{'id': 61314, 'data_set_name': '可以使用:anl14_median_ptprep_fy1', 'description': '不可使用,仅供参考:Median of Estimations of Reported Pretax Profit - upcoming year'} +{'id': 61315, 'data_set_name': '可以使用:anl14_median_ptprep_fy2', 'description': '不可使用,仅供参考:Median of Estimations of Reported Pretax Profit - upcoming 2 years'} +{'id': 61316, 'data_set_name': '可以使用:anl14_median_ptprep_fy3', 'description': '不可使用,仅供参考:Median of Estimations of Reported Pretax Profit - Upcoming 3 Years'} +{'id': 61317, 'data_set_name': '可以使用:anl14_median_ptprep_fy4', 'description': '不可使用,仅供参考:Median of estimations of Reported Pretax Profit - upcoming 4 years'} +{'id': 61318, 'data_set_name': '可以使用:anl14_median_ptprep_fy5', 'description': '不可使用,仅供参考:Median of Estimations of Reported Pretax Profit - upcoming 5 years'} +{'id': 61319, 'data_set_name': '可以使用:anl14_median_revenue_fp1', 'description': '不可使用,仅供参考:Median of estimations of revenue - upcoming quarter'} +{'id': 61320, 'data_set_name': '可以使用:anl14_median_revenue_fp2', 'description': '不可使用,仅供参考:Median of Estimations of Revenue - upcoming 2 quarters'} +{'id': 61321, 'data_set_name': '可以使用:anl14_median_revenue_fp3', 'description': '不可使用,仅供参考:Median of estimations of Revenue - upcoming 3 quarters'} +{'id': 61322, 'data_set_name': '可以使用:anl14_median_revenue_fp4', 'description': '不可使用,仅供参考:Median of estimations of revenue - upcoming 4 quarters'} +{'id': 61323, 'data_set_name': '可以使用:anl14_median_revenue_fp5', 'description': '不可使用,仅供参考:Median of Estimations of Revenue - upcoming 5 quarters'} +{'id': 61324, 'data_set_name': '可以使用:anl14_median_revenue_fy1', 'description': '不可使用,仅供参考:Median of Estimations of Revenue - Upcoming Year'} +{'id': 61325, 'data_set_name': '可以使用:anl14_median_revenue_fy2', 'description': '不可使用,仅供参考:Median of estimations of revenue - upcoming 2 years'} +{'id': 61326, 'data_set_name': '可以使用:anl14_median_revenue_fy3', 'description': '不可使用,仅供参考:Median of Estimations of Revenue - Upcoming 3 Years'} +{'id': 61327, 'data_set_name': '可以使用:anl14_median_revenue_fy4', 'description': '不可使用,仅供参考:Median of Estimations of Revenue - upcoming 4 years'} +{'id': 61328, 'data_set_name': '可以使用:anl14_median_revenue_fy5', 'description': '不可使用,仅供参考:Median of Estimations of Revenue - upcoming 5 years'} +{'id': 61329, 'data_set_name': '可以使用:anl14_median_roa_fy1', 'description': '不可使用,仅供参考:Median of Estimations of Returns on Assets - upcoming year'} +{'id': 61330, 'data_set_name': '可以使用:anl14_median_roa_fy2', 'description': '不可使用,仅供参考:Median of Estimations of Returns on Assets - Upcoming 2 Years'} +{'id': 61331, 'data_set_name': '可以使用:anl14_median_roa_fy3', 'description': '不可使用,仅供参考:Median of Estimations of Returns on Assets - Upcoming 3 Years'} +{'id': 61332, 'data_set_name': '可以使用:anl14_median_roa_fy4', 'description': '不可使用,仅供参考:Median of estimations of returns on assets - upcoming 4 years'} +{'id': 61333, 'data_set_name': '可以使用:anl14_median_roe_fp1', 'description': '不可使用,仅供参考:Median of Estimations of Returns on Equity - upcoming quarter'} +{'id': 61334, 'data_set_name': '可以使用:anl14_median_roe_fp2', 'description': '不可使用,仅供参考:Median of Estimations of Returns on Equity - Upcoming 2 Quarters'} +{'id': 61335, 'data_set_name': '可以使用:anl14_median_roe_fp3', 'description': '不可使用,仅供参考:Median of Estimations of Returns on Equity - upcoming 3 quarters'} +{'id': 61336, 'data_set_name': '可以使用:anl14_median_roe_fp4', 'description': '不可使用,仅供参考:Median of estimations of Returns on Equity - upcoming 4 quarters'} +{'id': 61337, 'data_set_name': '可以使用:anl14_median_roe_fy1', 'description': '不可使用,仅供参考:Median of Estimations of Returns on Equity - Upcoming Year'} +{'id': 61338, 'data_set_name': '可以使用:anl14_median_roe_fy2', 'description': '不可使用,仅供参考:Median of Estimations of Returns on Equity - Upcoming 2 Years'} +{'id': 61339, 'data_set_name': '可以使用:anl14_median_roe_fy3', 'description': '不可使用,仅供参考:Median of Estimations of Returns on Equity - Upcoming 3 Years'} +{'id': 61340, 'data_set_name': '可以使用:anl14_median_roe_fy4', 'description': '不可使用,仅供参考:Median of Estimations of Returns on Equity - upcoming 4 years'} +{'id': 61341, 'data_set_name': '可以使用:anl14_median_roe_fy5', 'description': '不可使用,仅供参考:Median of Estimations of Returns on Equity - upcoming 5 years'} +{'id': 61645, 'data_set_name': '可以使用:rtk_ptg_median', 'description': '不可使用,仅供参考:Median of price target'} +{'id': 61967, 'data_set_name': '可以使用:debt_to_equity_ratio_quarterly_prior_year', 'description': '不可使用,仅供参考:Ratio of total debt to total equity for the same quarter one year ago.'} +{'id': 61973, 'data_set_name': '可以使用:lowest_pe_ratio_quarterly', 'description': '不可使用,仅供参考:Lowest price-to-earnings ratio for the most recent quarter.'} +{'id': 62021, 'data_set_name': '可以使用:quarterly_current_ratio', 'description': '不可使用,仅供参考:Current assets divided by current liabilities for the quarter.'} +{'id': 62022, 'data_set_name': '可以使用:quarterly_current_ratio_2', 'description': '不可使用,仅供参考:Current assets divided by current liabilities for the most recent quarter.'} +{'id': 62023, 'data_set_name': '可以使用:quarterly_current_ratio_3', 'description': '不可使用,仅供参考:Current assets divided by current liabilities for the quarter.'} +{'id': 62024, 'data_set_name': '可以使用:quarterly_current_ratio_alt', 'description': '不可使用,仅供参考:Alternate calculation of current ratio for the most recent quarter.'} +{'id': 62025, 'data_set_name': '可以使用:quarterly_current_ratio_prior', 'description': '不可使用,仅供参考:Current assets divided by current liabilities for the same quarter last year.'} +{'id': 62026, 'data_set_name': '可以使用:quarterly_current_ratio_prior_year', 'description': '不可使用,仅供参考:Current assets divided by current liabilities for the same quarter last year.'} +{'id': 62032, 'data_set_name': '可以使用:quarterly_dividend_payout_ratio', 'description': '不可使用,仅供参考:Percentage of earnings paid out as dividends for the quarter.'} +{'id': 62045, 'data_set_name': '可以使用:quarterly_effective_tax_rate', 'description': '不可使用,仅供参考:Effective tax rate for the most recent quarter.'} +{'id': 62046, 'data_set_name': '可以使用:quarterly_effective_tax_rate_dup', 'description': '不可使用,仅供参考:Effective tax rate for the most recent quarter (duplicate field).'} +{'id': 62066, 'data_set_name': '可以使用:quarterly_interest_coverage', 'description': '不可使用,仅供参考:Ratio of EBIT to interest expense for the quarter.'} +{'id': 62067, 'data_set_name': '可以使用:quarterly_interest_coverage_db', 'description': '不可使用,仅供参考:EBIT divided by interest expense for the quarter (alternate source).'} +{'id': 62068, 'data_set_name': '可以使用:quarterly_interest_coverage_ratio', 'description': '不可使用,仅供参考:Earnings before interest and taxes divided by interest expense for the quarter.'} +{'id': 62076, 'data_set_name': '可以使用:quarterly_inventory_turnover_ratio', 'description': '不可使用,仅供参考:Inventory turnover ratio for the most recent quarter.'} +{'id': 62086, 'data_set_name': '可以使用:quarterly_long_term_debt_to_assets_ratio', 'description': '不可使用,仅供参考:Long-term debt divided by total assets for the most recent quarter.'} +{'id': 62092, 'data_set_name': '可以使用:quarterly_long_term_debt_to_equity_ratio', 'description': '不可使用,仅供参考:Long-term debt divided by equity for the most recent quarter.'} +{'id': 62093, 'data_set_name': '可以使用:quarterly_long_term_debt_to_equity_ratio_prior_year', 'description': '不可使用,仅供参考:Long-term debt divided by equity for the same quarter last year.'} +{'id': 62122, 'data_set_name': '可以使用:quarterly_payout_ratio', 'description': '不可使用,仅供参考:Percentage of earnings paid as dividends for the quarter.'} +{'id': 62123, 'data_set_name': '可以使用:quarterly_payout_ratio_2', 'description': '不可使用,仅供参考:Percentage of earnings paid as dividends for the quarter.'} +{'id': 62124, 'data_set_name': '可以使用:quarterly_pe_ratio_high', 'description': '不可使用,仅供参考:Highest price-to-earnings ratio for the most recent quarter.'} +{'id': 62125, 'data_set_name': '可以使用:quarterly_pe_ratio_high_2', 'description': '不可使用,仅供参考:Highest price-to-earnings ratio for the quarter.'} +{'id': 62126, 'data_set_name': '可以使用:quarterly_pe_ratio_low', 'description': '不可使用,仅供参考:Lowest price-to-earnings ratio for the most recent quarter.'} +{'id': 62127, 'data_set_name': '可以使用:quarterly_pe_ratio_maximum', 'description': '不可使用,仅供参考:Highest price-to-earnings ratio observed in the latest quarter.'} +{'id': 62128, 'data_set_name': '可以使用:quarterly_pe_ratio_minimum', 'description': '不可使用,仅供参考:Lowest price-to-earnings ratio observed in the latest quarter.'} +{'id': 62134, 'data_set_name': '可以使用:quarterly_price_to_sales_ratio', 'description': '不可使用,仅供参考:Price divided by sales per share for the quarter.'} +{'id': 62135, 'data_set_name': '可以使用:quarterly_price_to_sales_ratio_2', 'description': '不可使用,仅供参考:Ratio of price to sales for the most recent quarter.'} +{'id': 62136, 'data_set_name': '可以使用:quarterly_price_to_sales_ratio_3', 'description': '不可使用,仅供参考:Price divided by sales per share for the quarter.'} +{'id': 62137, 'data_set_name': '可以使用:quarterly_quick_ratio', 'description': '不可使用,仅供参考:Ratio of liquid assets to current liabilities for the latest quarter.'} +{'id': 62138, 'data_set_name': '可以使用:quarterly_quick_ratio_2', 'description': '不可使用,仅供参考:Quick ratio for the most recent quarter.'} +{'id': 62139, 'data_set_name': '可以使用:quarterly_quick_ratio_3', 'description': '不可使用,仅供参考:Quick ratio for the most recent quarter.'} +{'id': 62140, 'data_set_name': '可以使用:quarterly_quick_ratio_alt', 'description': '不可使用,仅供参考:Alternate calculation of quick ratio for the most recent quarter.'} +{'id': 62141, 'data_set_name': '可以使用:quarterly_quick_ratio_prior', 'description': '不可使用,仅供参考:Quick ratio for the same quarter last year.'} +{'id': 62142, 'data_set_name': '可以使用:quarterly_quick_ratio_prior_year', 'description': '不可使用,仅供参考:Quick ratio for the same quarter in the previous year.'} +{'id': 62148, 'data_set_name': '可以使用:quarterly_reinvestment_rate', 'description': '不可使用,仅供参考:Percentage of earnings reinvested in the company for the quarter.'} +{'id': 62149, 'data_set_name': '可以使用:quarterly_reinvestment_rate_2', 'description': '不可使用,仅供参考:Percentage of earnings reinvested in the company for the quarter.'} +{'id': 62150, 'data_set_name': '可以使用:quarterly_reinvestment_rate_3', 'description': '不可使用,仅供参考:Percent of earnings reinvested for the quarter.'} +{'id': 62156, 'data_set_name': '可以使用:quarterly_return_on_average_assets', 'description': '不可使用,仅供参考:Annualized net income divided by average assets for the quarter.'} +{'id': 62157, 'data_set_name': '可以使用:quarterly_return_on_average_equity', 'description': '不可使用,仅供参考:Annualized net income divided by average equity for the quarter.'} +{'id': 62172, 'data_set_name': '可以使用:quarterly_sga_to_revenue_ratio', 'description': '不可使用,仅供参考:Selling, general, and administrative expenses as a percentage of revenue for the quarter.'} +{'id': 62173, 'data_set_name': '可以使用:quarterly_sga_to_sales_ratio', 'description': '不可使用,仅供参考:Selling, general, and administrative expenses as a percent of revenue for the quarter.'} +{'id': 62174, 'data_set_name': '可以使用:quarterly_sga_to_sales_ratio_2', 'description': '不可使用,仅供参考:SG&A expenses as a percent of revenue for the quarter.'} +{'id': 62182, 'data_set_name': '可以使用:quarterly_tax_rate_percent', 'description': '不可使用,仅供参考:Effective tax rate for the most recent quarter, as a percentage.'} +{'id': 62195, 'data_set_name': '可以使用:quarterly_total_debt_to_assets_ratio', 'description': '不可使用,仅供参考:Total debt divided by total assets for the most recent quarter.'} +{'id': 62201, 'data_set_name': '可以使用:quarterly_total_debt_to_equity_ratio', 'description': '不可使用,仅供参考:Total debt divided by equity for the most recent quarter.'} +{'id': 62202, 'data_set_name': '可以使用:quarterly_total_debt_to_equity_ratio_prior_year', 'description': '不可使用,仅供参考:Total debt divided by equity for the same quarter last year.'} +{'id': 62203, 'data_set_name': '可以使用:quarterly_total_debt_to_total_capital_ratio', 'description': '不可使用,仅供参考:Total debt divided by total capital for the most recent quarter.'} +{'id': 62215, 'data_set_name': '可以使用:reporting_to_pricing_currency_exchange_rate', 'description': '不可使用,仅供参考:Exchange rate from reporting currency to pricing currency.'} +{'id': 62216, 'data_set_name': '可以使用:reporting_to_pricing_currency_exrate', 'description': '不可使用,仅供参考:Exchange rate between reporting currency and pricing currency.'} +{'id': 62226, 'data_set_name': '可以使用:ttm_return_on_average_equity', 'description': '不可使用,仅供参考:Net income divided by average equity for the trailing twelve months.'} +{'id': 62229, 'data_set_name': '可以使用:usd_to_reporting_currency_exchange_rate', 'description': '不可使用,仅供参考:Exchange rate between US dollar and reporting currency.'} +{'id': 62230, 'data_set_name': '可以使用:usd_to_reporting_currency_exrate', 'description': '不可使用,仅供参考:Exchange rate between USD and the reporting currency.'} +{'id': 62377, 'data_set_name': '可以使用:anl45_bm_exchange_rate', 'description': '不可使用,仅供参考:Exchange Rate by which the Benchmark will be measured'} +{'id': 62389, 'data_set_name': '可以使用:anl45_inv_exchange_rate', 'description': '不可使用,仅供参考:Exchange rate between stock and investment currencies / FX Rate on the period End date'} +{'id': 62410, 'data_set_name': '可以使用:anl45_risk_free_rate', 'description': '不可使用,仅供参考:The unrealised return on an open idea'} +{'id': 62419, 'data_set_name': '可以使用:anl45_treynor_ratio', 'description': '不可使用,仅供参考:A ratio that measures returns earned in excess of that which could have been earned on a riskless investment (such as the risk free rate).??'} +{'id': 62439, 'data_set_name': '可以使用:anl69_best_ebit_median', 'description': '不可使用,仅供参考:EBIT Median'} +{'id': 62448, 'data_set_name': '可以使用:anl69_best_eps_gaap_median', 'description': '不可使用,仅供参考:Earnings Per Share GAAP Median'} +{'id': 62449, 'data_set_name': '可以使用:anl69_best_eps_median', 'description': '不可使用,仅供参考:Earnings per Share Median'} +{'id': 62457, 'data_set_name': '可以使用:anl69_best_ndebt_median', 'description': '不可使用,仅供参考:NDEBT Median'} +{'id': 62471, 'data_set_name': '可以使用:anl69_best_net_gaap_median', 'description': '不可使用,仅供参考:Net GAAP Median'} +{'id': 62477, 'data_set_name': '可以使用:anl69_best_net_median', 'description': '不可使用,仅供参考:Net Inc Median'} +{'id': 62480, 'data_set_name': '可以使用:anl69_best_pe_ratio', 'description': '不可使用,仅供参考:P/E Ratio'} +{'id': 62481, 'data_set_name': '可以使用:anl69_best_px_bps_ratio', 'description': '不可使用,仅供参考:P/Bk'} +{'id': 62482, 'data_set_name': '可以使用:anl69_best_px_cps_ratio', 'description': '不可使用,仅供参考:P/CF'} +{'id': 62490, 'data_set_name': '可以使用:anl69_best_roa_median', 'description': '不可使用,仅供参考:Return on Assets Median'} +{'id': 62500, 'data_set_name': '可以使用:anl69_cps_best_cps_median', 'description': '不可使用,仅供参考:Cash Flow per Share Median'} +{'id': 62522, 'data_set_name': '可以使用:anl69_cpss_best_cps_median', 'description': '不可使用,仅供参考:Cash Flow per Share Median'} +{'id': 62547, 'data_set_name': '可以使用:anl69_dps_best_dps_median', 'description': '不可使用,仅供参考:Dividend per Share Median'} +{'id': 62568, 'data_set_name': '可以使用:anl69_dpss_best_dps_median', 'description': '不可使用,仅供参考:Dividend per Share Median'} +{'id': 62696, 'data_set_name': '可以使用:anl69_roe_best_roe_median', 'description': '不可使用,仅供参考:Return on Equity Median'} +{'id': 62718, 'data_set_name': '可以使用:anl69_roes_best_roe_median', 'description': '不可使用,仅供参考:Return on Equity Median'} +{'id': 136375, 'data_set_name': '可以使用:board_nationality_diversity_ratio_2', 'description': '不可使用,仅供参考:Proportion of directors from different countries on the board (alternate).'} +{'id': 136383, 'data_set_name': '可以使用:director_attrition_ratio_2', 'description': '不可使用,仅供参考:Proportion of directors who have left a role in the preceding period (alternate).'} +{'id': 136384, 'data_set_name': '可以使用:director_attrition_ratio_3yr', 'description': '不可使用,仅供参考:Proportion of directors who have left a role in the preceding three years.'} +{'id': 136386, 'data_set_name': '可以使用:equity_linked_compensation_ratio_2', 'description': '不可使用,仅供参考:Proportion of equity-linked compensation in total director remuneration (alternate).'} +{'id': 136417, 'data_set_name': '可以使用:fnd1_genderratio', 'description': '不可使用,仅供参考:The proportion of male directors whether for the Executive Directors'} +{'id': 136419, 'data_set_name': '可以使用:fnd1_stdevage', 'description': '不可使用,仅供参考:The standard deviation of the population of the ages of Directors for the Executive Directors'} +{'id': 136434, 'data_set_name': '可以使用:ltip_to_total_remuneration_ratio_2', 'description': '不可使用,仅供参考:Ratio of long-term incentive plan value to total remuneration for the period (alternate).'} +{'id': 136456, 'data_set_name': '可以使用:total_remuneration_period', 'description': '不可使用,仅供参考:Total remuneration awarded to the director for the period.'} +{'id': 136569, 'data_set_name': '可以使用:fifty_day_average_price', 'description': '不可使用,仅供参考:The average closing price over the last 50 trading days.'} +{'id': 136570, 'data_set_name': '可以使用:fnd17_1_reptoprcexrate', 'description': '不可使用,仅供参考:Reporting Currency to base currency exchange rate'} +{'id': 136571, 'data_set_name': '可以使用:fnd17_1_usdtorepexrate', 'description': '不可使用,仅供参考:Reporting Currency to USD exchange rate'} +{'id': 136572, 'data_set_name': '可以使用:fnd17_1alldelay1_reptoprcexrate', 'description': '不可使用,仅供参考:Reporting Currency to base currency exchange rate'} +{'id': 136573, 'data_set_name': '可以使用:fnd17_1alldelay1_usdtorepexrate', 'description': '不可使用,仅供参考:Reporting Currency to USD exchange rate'} +{'id': 136574, 'data_set_name': '可以使用:fnd17_2_reptoprcexrate', 'description': '不可使用,仅供参考:Reporting Currency to base currency exchange rate'} +{'id': 136575, 'data_set_name': '可以使用:fnd17_2_usdtorepexrate', 'description': '不可使用,仅供参考:Reporting Currency to USD exchange rate'} +{'id': 136576, 'data_set_name': '可以使用:fnd17_2alldelay1_reptoprcexrate', 'description': '不可使用,仅供参考:Reporting Currency to base currency exchange rate'} +{'id': 136577, 'data_set_name': '可以使用:fnd17_2alldelay1_usdtorepexrate', 'description': '不可使用,仅供参考:Reporting Currency to USD exchange rate'} +{'id': 136585, 'data_set_name': '可以使用:fnd17_3_reptoprcexrate', 'description': '不可使用,仅供参考:Reporting Currency to base currency exchange rate'} +{'id': 136586, 'data_set_name': '可以使用:fnd17_3_usdtorepexrate', 'description': '不可使用,仅供参考:Reporting Currency to USD exchange rate'} +{'id': 136587, 'data_set_name': '可以使用:fnd17_3alldelay1_reptoprcexrate', 'description': '不可使用,仅供参考:Reporting Currency to base currency exchange rate'} +{'id': 136588, 'data_set_name': '可以使用:fnd17_3alldelay1_usdtorepexrate', 'description': '不可使用,仅供参考:Reporting Currency to USD exchange rate'} +{'id': 136589, 'data_set_name': '可以使用:fnd17_4_reptoprcexrate', 'description': '不可使用,仅供参考:Reporting Currency to base currency exchange rate'} +{'id': 136590, 'data_set_name': '可以使用:fnd17_4_usdtorepexrate', 'description': '不可使用,仅供参考:Reporting Currency to USD exchange rate'} +{'id': 136591, 'data_set_name': '可以使用:fnd17_4alldelay1_reptoprcexrate', 'description': '不可使用,仅供参考:Reporting Currency to base currency exchange rate'} +{'id': 136592, 'data_set_name': '可以使用:fnd17_4alldelay1_usdtorepexrate', 'description': '不可使用,仅供参考:Reporting Currency to USD exchange rate'} +{'id': 136593, 'data_set_name': '可以使用:fnd17_5_reptoprcexrate', 'description': '不可使用,仅供参考:Reporting Currency to base currency exchange rate'} +{'id': 136594, 'data_set_name': '可以使用:fnd17_5_usdtorepexrate', 'description': '不可使用,仅供参考:Reporting Currency to USD exchange rate'} +{'id': 136595, 'data_set_name': '可以使用:fnd17_5alldelay1_reptoprcexrate', 'description': '不可使用,仅供参考:Reporting Currency to base currency exchange rate'} +{'id': 136596, 'data_set_name': '可以使用:fnd17_5alldelay1_usdtorepexrate', 'description': '不可使用,仅供参考:Reporting Currency to USD exchange rate'} +{'id': 136597, 'data_set_name': '可以使用:fnd17_6_reptoprcexrate', 'description': '不可使用,仅供参考:Reporting Currency to base currency exchange rate'} +{'id': 136598, 'data_set_name': '可以使用:fnd17_6_usdtorepexrate', 'description': '不可使用,仅供参考:Reporting Currency to USD exchange rate'} +{'id': 136599, 'data_set_name': '可以使用:fnd17_6alldelay1_reptoprcexrate', 'description': '不可使用,仅供参考:Reporting Currency to base currency exchange rate'} +{'id': 136600, 'data_set_name': '可以使用:fnd17_6alldelay1_usdtorepexrate', 'description': '不可使用,仅供参考:Reporting Currency to USD exchange rate'} +{'id': 136601, 'data_set_name': '可以使用:fnd17_7_reptoprcexrate', 'description': '不可使用,仅供参考:Reporting Currency to base currency exchange rate'} +{'id': 136602, 'data_set_name': '可以使用:fnd17_7_usdtorepexrate', 'description': '不可使用,仅供参考:Reporting Currency to USD exchange rate'} +{'id': 136603, 'data_set_name': '可以使用:fnd17_7alldelay1_reptoprcexrate', 'description': '不可使用,仅供参考:Reporting Currency to base currency exchange rate'} +{'id': 136604, 'data_set_name': '可以使用:fnd17_7alldelay1_usdtorepexrate', 'description': '不可使用,仅供参考:Reporting Currency to USD exchange rate'} +{'id': 136605, 'data_set_name': '可以使用:fnd17_8_reptoprcexrate', 'description': '不可使用,仅供参考:Reporting Currency to base currency exchange rate'} +{'id': 136606, 'data_set_name': '可以使用:fnd17_8_usdtorepexrate', 'description': '不可使用,仅供参考:Reporting Currency to USD exchange rate'} +{'id': 136607, 'data_set_name': '可以使用:fnd17_8alldelay1_reptoprcexrate', 'description': '不可使用,仅供参考:Reporting Currency to base currency exchange rate'} +{'id': 136608, 'data_set_name': '可以使用:fnd17_8alldelay1_usdtorepexrate', 'description': '不可使用,仅供参考:Reporting Currency to USD exchange rate'} +{'id': 136609, 'data_set_name': '可以使用:fnd17_9_reptoprcexrate', 'description': '不可使用,仅供参考:Reporting Currency to base currency exchange rate'} +{'id': 136610, 'data_set_name': '可以使用:fnd17_9_usdtorepexrate', 'description': '不可使用,仅供参考:Reporting Currency to USD exchange rate'} +{'id': 136618, 'data_set_name': '可以使用:fnd17_acurratio', 'description': '不可使用,仅供参考:Current ratio - most recent fiscal year'} +{'id': 136653, 'data_set_name': '可以使用:fnd17_alldelay1_acurratio', 'description': '不可使用,仅供参考:Current ratio - most recent fiscal year'} +{'id': 136690, 'data_set_name': '可以使用:fnd17_alldelay1_apayratio', 'description': '不可使用,仅供参考:Payout ratio - most recent fiscal year'} +{'id': 136691, 'data_set_name': '可以使用:fnd17_alldelay1_apayratio2', 'description': '不可使用,仅供参考:Payout ratio - most recent fiscal year -1'} +{'id': 136720, 'data_set_name': '可以使用:fnd17_alldelay1_ataxrate', 'description': '不可使用,仅供参考:Tax rate - most recent fiscal year'} +{'id': 136721, 'data_set_name': '可以使用:fnd17_alldelay1_ataxrate2', 'description': '不可使用,仅供参考:Tax rate - most recent fiscal year -1'} +{'id': 136830, 'data_set_name': '可以使用:fnd17_alldelay1_qcurratio', 'description': '不可使用,仅供参考:Current ratio - most recent quarter'} +{'id': 136831, 'data_set_name': '可以使用:fnd17_alldelay1_qcurratio2', 'description': '不可使用,仅供参考:Current ratio - most recent quarter, 1 year ago'} +{'id': 136843, 'data_set_name': '可以使用:fnd17_alldelay1_qpayratio', 'description': '不可使用,仅供参考:Payout ratio - most recent quarter'} +{'id': 136862, 'data_set_name': '可以使用:fnd17_alldelay1_qtaxrate', 'description': '不可使用,仅供参考:Tax rate - most recent quarter'} +{'id': 136959, 'data_set_name': '可以使用:fnd17_alldelay1_ttmtaxrate', 'description': '不可使用,仅供参考:Tax rate - trailing 12 months'} +{'id': 136987, 'data_set_name': '可以使用:fnd17_apayratio', 'description': '不可使用,仅供参考:Payout ratio - most recent fiscal year'} +{'id': 136988, 'data_set_name': '可以使用:fnd17_apayratio2', 'description': '不可使用,仅供参考:Payout ratio - most recent fiscal year -1'} +{'id': 137017, 'data_set_name': '可以使用:fnd17_ataxrate', 'description': '不可使用,仅供参考:Tax rate - most recent fiscal year'} +{'id': 137018, 'data_set_name': '可以使用:fnd17_ataxrate2', 'description': '不可使用,仅供参考:Tax rate - most recent fiscal year -1'} +{'id': 137136, 'data_set_name': '可以使用:fnd17_qcurratio', 'description': '不可使用,仅供参考:Current ratio - most recent quarter'} +{'id': 137137, 'data_set_name': '可以使用:fnd17_qcurratio2', 'description': '不可使用,仅供参考:Current ratio - most recent quarter, 1 year ago'} +{'id': 137149, 'data_set_name': '可以使用:fnd17_qpayratio', 'description': '不可使用,仅供参考:Payout ratio - most recent quarter'} +{'id': 137168, 'data_set_name': '可以使用:fnd17_qtaxrate', 'description': '不可使用,仅供参考:Tax rate - most recent quarter'} +{'id': 137223, 'data_set_name': '可以使用:fnd17_siratio1', 'description': '不可使用,仅供参考:Short Interest Ratio - most recent'} +{'id': 137224, 'data_set_name': '可以使用:fnd17_siratio2', 'description': '不可使用,仅供参考:Short Interest Ratio - 1 month ago'} +{'id': 137225, 'data_set_name': '可以使用:fnd17_siratio3', 'description': '不可使用,仅供参考:Short Interest ratio - 2 months ago'} +{'id': 137226, 'data_set_name': '可以使用:fnd17_siratio4', 'description': '不可使用,仅供参考:Short Interest ratio - 3 months ago'} +{'id': 137294, 'data_set_name': '可以使用:fnd17_ttmtaxrate', 'description': '不可使用,仅供参考:Tax rate - trailing 12 month'} +{'id': 137307, 'data_set_name': '可以使用:interim_debt_to_capital_ratio', 'description': '不可使用,仅供参考:Ratio of total debt to total capital for the most recent interim period.'} +{'id': 137351, 'data_set_name': '可以使用:quarterly_debt_to_assets_ratio', 'description': '不可使用,仅供参考:Ratio of total debt to total assets for the most recent quarter.'} +{'id': 137352, 'data_set_name': '可以使用:quarterly_debt_to_equity_ratio_2', 'description': '不可使用,仅供参考:Ratio of total debt to total equity for the most recent quarter.'} +{'id': 137376, 'data_set_name': '可以使用:rep_to_pricing_fx_rate_emi1', 'description': '不可使用,仅供参考:Exchange rate used to convert reporting currency to pricing currency for this module.'} +{'id': 137388, 'data_set_name': '可以使用:reporting_to_pricing_fx_rate', 'description': '不可使用,仅供参考:Exchange rate used to convert reporting currency to pricing currency.'} +{'id': 137389, 'data_set_name': '可以使用:reporting_to_pricing_fx_rate_2', 'description': '不可使用,仅供参考:Exchange rate from reporting currency to pricing currency.'} +{'id': 137390, 'data_set_name': '可以使用:reporting_to_pricing_fx_rate_3', 'description': '不可使用,仅供参考:The exchange rate from reporting currency to pricing currency.'} +{'id': 137391, 'data_set_name': '可以使用:return_on_average_assets_quarterly', 'description': '不可使用,仅供参考:Return on average assets for the most recent quarter, annualized.'} +{'id': 137417, 'data_set_name': '可以使用:usd_to_reporting_fx_rate', 'description': '不可使用,仅供参考:Exchange rate used to convert USD to the reporting currency.'} +{'id': 137418, 'data_set_name': '可以使用:usd_to_reporting_fx_rate_3', 'description': '不可使用,仅供参考:The exchange rate from US dollars to the reporting currency.'} +{'id': 137419, 'data_set_name': '可以使用:usd_to_reporting_fx_rate_emi1', 'description': '不可使用,仅供参考:Exchange rate used to convert USD to reporting currency for this module.'} +{'id': 137518, 'data_set_name': '可以使用:earnings_from_residual_operations', 'description': '不可使用,仅供参考:[Quarterly] Restructuring Charge'} +{'id': 137709, 'data_set_name': '可以使用:fnd23_frontpage', 'description': '不可使用,仅供参考:Indicates whether a given development is coming from a front page story'} +{'id': 138896, 'data_set_name': '可以使用:income_from_discontinued_operations', 'description': '不可使用,仅供参考:[Quarterly] Interest Cost - Domestic'} +{'id': 139029, 'data_set_name': '可以使用:return_on_equity_ratio_3', 'description': "不可使用,仅供参考:Return on equity, calculated as net income divided by average shareholders' equity."} +{'id': 139030, 'data_set_name': '可以使用:revenue_from_domestic_operations', 'description': '不可使用,仅供参考:[Quarterly] Expected Rate of Return - Domestic'} +{'id': 139103, 'data_set_name': '可以使用:short_term_portion_total_mortgage', 'description': '不可使用,仅供参考:Short-term portion of total mortgage obligations.'} +{'id': 139115, 'data_set_name': '可以使用:solicitation_liabilities', 'description': '不可使用,仅供参考:[Quarterly] Operating Leases - Interest Cost'} +{'id': 139471, 'data_set_name': '可以使用:fnd28_growthratesa_value_08579a', 'description': '不可使用,仅供参考:value of annual field: Market Capitalization Growth (year ago) Profitability'} +{'id': 139472, 'data_set_name': '可以使用:fnd28_growthratesa_value_08581a', 'description': '不可使用,仅供参考:value of annual field: Market Capitalization - 3 Year Growth'} +{'id': 139473, 'data_set_name': '可以使用:fnd28_growthratesa_value_08601a', 'description': '不可使用,仅供参考:value of annual field: Earnings per Share Growth'} +{'id': 139474, 'data_set_name': '可以使用:fnd28_growthratesa_value_08604a', 'description': '不可使用,仅供参考:value of annual field: Earnings Per Share - 3 Yr Annual Growth Five Year Averages'} +{'id': 139475, 'data_set_name': '可以使用:fnd28_growthratesa_value_08605a', 'description': '不可使用,仅供参考:value of annual field: Earnings per Share Growth'} +{'id': 139476, 'data_set_name': '可以使用:fnd28_growthratesa_value_08606a', 'description': '不可使用,仅供参考:value of annual field: Book Value per Share Growth (year ago)'} +{'id': 139477, 'data_set_name': '可以使用:fnd28_growthratesa_value_08610a', 'description': '不可使用,仅供参考:value of annual field: Book Value Per Share - 5 Yr Annual Growth Profitability Annual Statistics'} +{'id': 139478, 'data_set_name': '可以使用:fnd28_growthratesa_value_08611a', 'description': '不可使用,仅供参考:value of annual field: Dividends per Share Growth'} +{'id': 139479, 'data_set_name': '可以使用:fnd28_growthratesa_value_08615a', 'description': '不可使用,仅供参考:value of annual field: Dividends Per Share - 5 Yr Annual Growth'} +{'id': 139480, 'data_set_name': '可以使用:fnd28_growthratesa_value_08616a', 'description': '不可使用,仅供参考:value of annual field: Equity Growth (year ago)'} +{'id': 139481, 'data_set_name': '可以使用:fnd28_growthratesa_value_08620a', 'description': '不可使用,仅供参考:value of annual field: Equity - 5 Yr Annual Growth'} +{'id': 139482, 'data_set_name': '可以使用:fnd28_growthratesa_value_08621a', 'description': '不可使用,仅供参考:value of annual field: Total Assets Growth (year ago)'} +{'id': 139483, 'data_set_name': '可以使用:fnd28_growthratesa_value_08625a', 'description': '不可使用,仅供参考:value of annual field: Total Assets - 5 Yr Annual Growth'} +{'id': 139484, 'data_set_name': '可以使用:fnd28_growthratesa_value_08626a', 'description': '不可使用,仅供参考:value of annual field: Employees Growth'} +{'id': 139485, 'data_set_name': '可以使用:fnd28_growthratesa_value_08630a', 'description': '不可使用,仅供参考:value of annual field: Employees - 5 Yr Annual Growth'} +{'id': 139486, 'data_set_name': '可以使用:fnd28_growthratesa_value_08631a', 'description': '不可使用,仅供参考:value of annual field: Net Sales/Revenues Growth'} +{'id': 139487, 'data_set_name': '可以使用:fnd28_growthratesa_value_08635a', 'description': '不可使用,仅供参考:value of annual field: Net Sales / Revenues - 5 Yr Annual Growth'} +{'id': 139488, 'data_set_name': '可以使用:fnd28_growthratesa_value_08636a', 'description': '不可使用,仅供参考:value of annual field: Net Income Growth'} +{'id': 139489, 'data_set_name': '可以使用:fnd28_growthratesa_value_08640a', 'description': '不可使用,仅供参考:value of annual field: Net Income Growth'} +{'id': 139490, 'data_set_name': '可以使用:fnd28_growthratesa_value_08646a', 'description': '不可使用,仅供参考:value of annual field: Operating Income Growth'} +{'id': 139491, 'data_set_name': '可以使用:fnd28_growthratesa_value_08650a', 'description': '不可使用,仅供参考:value of annual field: Operating Income - 5 Yr Annual Growth'} +{'id': 139492, 'data_set_name': '可以使用:fnd28_growthratesa_value_08676a', 'description': '不可使用,仅供参考:value of annual field: Net Margin Growth'} +{'id': 139493, 'data_set_name': '可以使用:fnd28_growthratesa_value_08680a', 'description': '不可使用,仅供参考:value of annual field: Net Margin Growth Profitability Annual Statistics'} +{'id': 139494, 'data_set_name': '可以使用:fnd28_growthratesa_value_08816a', 'description': '不可使用,仅供参考:value of annual field: Earnings Per Share - Fiscal - 1 Yr Annual Growth'} +{'id': 139495, 'data_set_name': '可以使用:fnd28_growthratesa_value_08821a', 'description': '不可使用,仅供参考:value of annual field: Book Value Per Share - Fiscal - 1 Yr Growth Rate Three Year Averages'} +{'id': 139776, 'data_set_name': '可以使用:fnd28_ratesq_value_08106q', 'description': '不可使用,仅供参考:value of quarterly field: Current Ratio'} +{'id': 139777, 'data_set_name': '可以使用:fnd28_ratesq_value_08111q', 'description': '不可使用,仅供参考:value of quarterly field: Cash & Equivalents % Total Current Assets Company Specific Accounting Practices'} +{'id': 139778, 'data_set_name': '可以使用:fnd28_ratesq_value_08226q', 'description': '不可使用,仅供参考:value of quarterly field: Long Term Debt % Common Equity'} +{'id': 139779, 'data_set_name': '可以使用:fnd28_ratesq_value_08231q', 'description': '不可使用,仅供参考:value of quarterly field: Total Debt % Common Equity'} +{'id': 139780, 'data_set_name': '可以使用:fnd28_ratesq_value_08236q', 'description': '不可使用,仅供参考:value of quarterly field: Total Debt % Total Assets'} +{'id': 139781, 'data_set_name': '可以使用:fnd28_ratesq_value_08241q', 'description': '不可使用,仅供参考:value of quarterly field: Common Equity % Total Assets'} +{'id': 139782, 'data_set_name': '可以使用:fnd28_ratesq_value_08251q', 'description': '不可使用,仅供参考:value of quarterly field: Fixed Charge Coverage Ratio'} +{'id': 139783, 'data_set_name': '可以使用:fnd28_ratesq_value_08261q', 'description': '不可使用,仅供参考:value of quarterly field: Operating Cash/Fixed Charges'} +{'id': 139784, 'data_set_name': '可以使用:fnd28_ratesq_value_08266q', 'description': '不可使用,仅供参考:value of quarterly field: Fixed Assets % Common Equity Company Specific Accounting Practices'} +{'id': 139785, 'data_set_name': '可以使用:fnd28_ratesq_value_08287q', 'description': '不可使用,仅供参考:value of quarterly field: Total Assets / Common Equity Ratio'} +{'id': 139786, 'data_set_name': '可以使用:fnd28_ratesq_value_08301q', 'description': '不可使用,仅供参考:value of quarterly field: Return on Equity Total (%)'} +{'id': 139787, 'data_set_name': '可以使用:fnd28_ratesq_value_08306q', 'description': '不可使用,仅供参考:value of quarterly field: Gross Profit Margin'} +{'id': 139788, 'data_set_name': '可以使用:fnd28_ratesq_value_08311q', 'description': '不可使用,仅供参考:value of quarterly field: Cash Flow/Sales'} +{'id': 139789, 'data_set_name': '可以使用:fnd28_ratesq_value_08316q', 'description': '不可使用,仅供参考:value of quarterly field: Operating Profit Margin'} +{'id': 139790, 'data_set_name': '可以使用:fnd28_ratesq_value_08321q', 'description': '不可使用,仅供参考:value of quarterly field: Pre-tax Margin'} +{'id': 139791, 'data_set_name': '可以使用:fnd28_ratesq_value_08326q', 'description': '不可使用,仅供参考:value of quarterly field: Return on Assets'} +{'id': 139792, 'data_set_name': '可以使用:fnd28_ratesq_value_08346q', 'description': '不可使用,仅供参考:value of quarterly field: Tax Rate'} +{'id': 139793, 'data_set_name': '可以使用:fnd28_ratesq_value_08366q', 'description': '不可使用,仅供参考:value of quarterly field: Net Margin'} +{'id': 139794, 'data_set_name': '可以使用:fnd28_ratesq_value_08371q', 'description': '不可使用,仅供参考:value of quarterly field: Return on Equity - per Share'} +{'id': 139795, 'data_set_name': '可以使用:fnd28_ratesq_value_08381q', 'description': '不可使用,仅供参考:value of quarterly field: Cash Earnings Return on Equity'} +{'id': 139796, 'data_set_name': '可以使用:fnd28_ratesq_value_08401q', 'description': '不可使用,仅供参考:value of quarterly field: Total Asset Turnover'} +{'id': 139797, 'data_set_name': '可以使用:fnd28_ratesq_value_08416q', 'description': '不可使用,仅供参考:value of quarterly field: Capital Expenditure % Total Assets'} +{'id': 139798, 'data_set_name': '可以使用:fnd28_ratesq_value_08421q', 'description': '不可使用,仅供参考:value of quarterly field: Capital Expenditure % Sales'} +{'id': 139799, 'data_set_name': '可以使用:fnd28_ratesq_value_08579q', 'description': '不可使用,仅供参考:value of quarterly field: Market Capitalization Growth (year ago) Profitability'} +{'id': 139800, 'data_set_name': '可以使用:fnd28_ratesq_value_08601q', 'description': '不可使用,仅供参考:value of quarterly field: Earnings per Share Growth'} +{'id': 139801, 'data_set_name': '可以使用:fnd28_ratesq_value_08606q', 'description': '不可使用,仅供参考:value of quarterly field: Book Value per Share Growth (year ago)'} +{'id': 139802, 'data_set_name': '可以使用:fnd28_ratesq_value_08611q', 'description': '不可使用,仅供参考:value of quarterly field: Dividends per Share Growth'} +{'id': 139803, 'data_set_name': '可以使用:fnd28_ratesq_value_08616q', 'description': '不可使用,仅供参考:value of quarterly field: Equity Growth (year ago)'} +{'id': 139804, 'data_set_name': '可以使用:fnd28_ratesq_value_08621q', 'description': '不可使用,仅供参考:value of quarterly field: Total Assets Growth (year ago)'} +{'id': 139805, 'data_set_name': '可以使用:fnd28_ratesq_value_08631q', 'description': '不可使用,仅供参考:value of quarterly field: Net Sales/Revenues Growth'} +{'id': 139806, 'data_set_name': '可以使用:fnd28_ratesq_value_08636q', 'description': '不可使用,仅供参考:value of quarterly field: Net Income Growth'} +{'id': 139807, 'data_set_name': '可以使用:fnd28_ratesq_value_08651q', 'description': '不可使用,仅供参考:value of quarterly field: Reinvestment Rate - per Share'} +{'id': 139808, 'data_set_name': '可以使用:fnd28_ratesq_value_08676q', 'description': '不可使用,仅供参考:value of quarterly field: Net Margin Growth'} +{'id': 139809, 'data_set_name': '可以使用:fnd28_ratesq_value_08801q', 'description': '不可使用,仅供参考:value of quarterly field: Total Investment Return year ago Growth Rates'} +{'id': 140612, 'data_set_name': '可以使用:fnd72_pit_or_is_a_eff_int_rate', 'description': '不可使用,仅供参考:Percentage of total interest incurred over the average of total debt during the period'} +{'id': 140626, 'data_set_name': '可以使用:fnd72_pit_or_is_a_is_disc_rate_pension_plan_liabs', 'description': '不可使用,仅供参考:The estimated interest rate that is used to compute the present value of benefit obligations'} +{'id': 140656, 'data_set_name': '可以使用:fnd72_pit_or_is_a_is_rate_of_comp_increase_pension', 'description': '不可使用,仅供参考:The expected rate of employee compensation growth expressed as a percentage'} +{'id': 140683, 'data_set_name': '可以使用:fnd72_pit_or_is_q_eff_int_rate', 'description': '不可使用,仅供参考:Percentage of total interest incurred over the average of total debt during the period'} +{'id': 140878, 'data_set_name': '可以使用:fnd72_s_pit_or_is_q_eff_int_rate', 'description': '不可使用,仅供参考:Percentage of total interest incurred over the average of total debt during the period'} +{'id': 140921, 'data_set_name': '可以使用:fnd86_average_score', 'description': '不可使用,仅供参考:Average score'} +{'id': 140934, 'data_set_name': '可以使用:srp_average_score', 'description': '不可使用,仅供参考:Average score'} +{'id': 373203, 'data_set_name': '可以使用:average_spread_slippage', 'description': '不可使用,仅供参考:Average slippage experienced due to spread during trade execution.'} +{'id': 373206, 'data_set_name': '可以使用:global_group_order_slippage', 'description': '不可使用,仅供参考:Slippage experienced for grouped orders across global markets.'} +{'id': 373207, 'data_set_name': '可以使用:group_order_slippage', 'description': '不可使用,仅供参考:Slippage experienced when executing grouped orders.'} +{'id': 373210, 'data_set_name': '可以使用:slippage_commission_2022', 'description': '不可使用,仅供参考:Estimated slippage and commission costs for the year 2022.'} +{'id': 373211, 'data_set_name': '可以使用:slippage_commission_2025', 'description': '不可使用,仅供参考:Estimated slippage and commission costs for the year 2025.'} +{'id': 373214, 'data_set_name': '可以使用:transaction_cost_median', 'description': '不可使用,仅供参考:Median estimated transaction cost for a trade.'} +{'id': 373249, 'data_set_name': '可以使用:pv13_com_page_rank', 'description': '不可使用,仅供参考:the PageRank of competitors'} +{'id': 373262, 'data_set_name': '可以使用:pv13_ompetitor_graphrank_page_rank', 'description': '不可使用,仅供参考:the PageRank score of competitors'} +{'id': 373268, 'data_set_name': '可以使用:pv13_ustomer_graphrank_page_rank', 'description': '不可使用,仅供参考:the PageRank score of customers'} +{'id': 373271, 'data_set_name': '可以使用:pv13_ustomergraphrank_page_rank', 'description': '不可使用,仅供参考:the PageRank of customers'} +{'id': 373323, 'data_set_name': '可以使用:pv173_rawratiosatlas_unit_name', 'description': '不可使用,仅供参考:Atlas unit name'} +{'id': 373324, 'data_set_name': '可以使用:pv173_rawratiosbondreturn20deqwt', 'description': '不可使用,仅供参考:It is defined as the equally weighted single bond return over last 20 days with the filter of the bonds that mature between 3 years and 7 years.'} +{'id': 373325, 'data_set_name': '可以使用:pv173_rawratioseqbddivrgbdrtn120d', 'description': '不可使用,仅供参考:It is defined as the difference of market equity return and estimated equity return based on 120-day linear regression of equity return on aggregated bond return and MSCI ACWIIndex return.'} +{'id': 373326, 'data_set_name': '可以使用:pv173_rawratioseqbddivrgsprd5y120d', 'description': '不可使用,仅供参考:It is defined as the difference of market equity return and estimated equity return based on 120-day linear regression of equity return on 5-year mid z-spread percentage change and MSCI ACWIIndex return.'} +{'id': 373327, 'data_set_name': '可以使用:pv173_rawratioseqbddivrgsprd5y120dsbst', 'description': '不可使用,仅供参考:It is defined as the difference of market equity return and estimated equity return based on 120-day linear regression of equity return on 5-year mid z-spread percentage change and MSCI ACWIIndex return'} +{'id': 373328, 'data_set_name': '可以使用:pv173_rawratiosmt5yzspread', 'description': '不可使用,仅供参考:It is defined as the region relative 5-year mid z-spreadIn the bond z-spread curve.'} +{'id': 373329, 'data_set_name': '可以使用:pv173_rawratiosmt5yzspreadchg60d', 'description': '不可使用,仅供参考:It is defined as the 60-day change of 5-year mid z-spreadIn the bond z-spread curve.'} +{'id': 373330, 'data_set_name': '可以使用:pv173_rawratiosmt5yzspreadchg60dsbst', 'description': '不可使用,仅供参考:It is defined as the 60-day change of 5-year mid z-spreadIn the bond z-spread curve'} +{'id': 373331, 'data_set_name': '可以使用:pv173_rawratiosmt5yzspreadchgstd20d', 'description': '不可使用,仅供参考:It is defined as the 20-day standard deviation of changeIn 5-year mid z-spreadIn the bond z-spread curve.'} +{'id': 373332, 'data_set_name': '可以使用:pv173_rawratiosmt5yzspreadchgstd20dsbst', 'description': '不可使用,仅供参考:It is defined as the 20-day standard deviation of changeIn 5-year mid z-spreadIn the bond z-spread curve'} +{'id': 373333, 'data_set_name': '可以使用:pv173_rawratiosmt5yzspreadindrel', 'description': '不可使用,仅供参考:It is defined as theIndustry relative 5-year mid z-spreadIn the bond z-spread curve.'} +{'id': 373334, 'data_set_name': '可以使用:pv173_rawratiosmt5yzspreadindrelsbst', 'description': '不可使用,仅供参考:It is defined as theIndustry relative 5-year mid z-spreadIn the bond z-spread curve'} +{'id': 373335, 'data_set_name': '可以使用:pv173_rawratiosmt5yzspreadsbst', 'description': '不可使用,仅供参考:It is defined as the region relative 5-year mid z-spreadIn the bond z-spread curve'} +{'id': 373336, 'data_set_name': '可以使用:pv173_rawratiosmt5yzspreadzscore60d', 'description': '不可使用,仅供参考:It is defined as the 60-day z-score of 5-year mid z-spreadIn the bond z-spread curve.'} +{'id': 373337, 'data_set_name': '可以使用:pv173_rawratiosmt5yzspreadzscore60dsbst', 'description': '不可使用,仅供参考:It is defined as the 60-day z-score of 5-year mid z-spreadIn the bond z-spread curve'} +{'id': 373338, 'data_set_name': '可以使用:pv173_rawratiosreltermspreadtransfema120d', 'description': '不可使用,仅供参考:It is defined as the 120-day exponential average of transformed relative term spread.'} +{'id': 373339, 'data_set_name': '可以使用:pv173_rawratiosreltermspreadtransfema120dsbst', 'description': '不可使用,仅供参考:It is defined as the 120-day exponential average of transformed relative term spread'} +{'id': 373340, 'data_set_name': '可以使用:pv173_rawratiosshift60dchg60d', 'description': '不可使用,仅供参考:It is defined as the 60-day change of ShiftIn Bond Z-spread Curve.'} +{'id': 373341, 'data_set_name': '可以使用:pv173_rawratiosshift60dlast', 'description': '不可使用,仅供参考:It is defined as the average factor loading of the first principal component for the z-spread between 6 months and 40 years'} +{'id': 373342, 'data_set_name': '可以使用:pv173_rawratiosshift60dstd60d', 'description': '不可使用,仅供参考:It is defined as the 60-day standard deviation of ShiftIn Bond Z-spread Curve.'} +{'id': 373343, 'data_set_name': '可以使用:pv173_rawratiosshift60dzscore60d', 'description': '不可使用,仅供参考:It is defined as the 60-day z-score of ShiftIn Bond Z-spread Curve.'} +{'id': 373344, 'data_set_name': '可以使用:pv173_rawratiostilt5y1ydiff60dema60d', 'description': '不可使用,仅供参考:It is defined as the 60-day exponential average of the difference of factor loading of the second principal component for the 5-year z-spread and 1-year z-spread'} +{'id': 373345, 'data_set_name': '可以使用:pv173_rawratiostilt60dchg60d', 'description': '不可使用,仅供参考:It is defined as the 60-day change of TiltIn Bond Z-spread Curve.'} +{'id': 373346, 'data_set_name': '可以使用:pv173_rawratiostilt60dema60d', 'description': '不可使用,仅供参考:It is defined as the 60-day exponential average of average factor loading of the second principal component for the z-spread between 6 months and 40 years'} +{'id': 373347, 'data_set_name': '可以使用:pv173_rawratiostilt6m1yavg60dlast', 'description': '不可使用,仅供参考:It is defined as the average factor loading of the second principal component for the 6-month and 1-year z-spread'} +{'id': 373348, 'data_set_name': '可以使用:pv173_rawratiostwist120dlast', 'description': '不可使用,仅供参考:It is defined as the average factor loading of the third principal component for the z-spread between 6 months and 40 years'} +{'id': 373349, 'data_set_name': '可以使用:pv173_rawratiostwist6m1yavg120dema120d', 'description': '不可使用,仅供参考:It is defined as the 120-day exponential average of average factor loading of the third principal component for the 6-month and 1-year z-spread'} +{'id': 373671, 'data_set_name': '可以使用:pv96_eqy_split_ratio', 'description': '不可使用,仅供参考:This number describes the rate at which the company will be dividing their current shares outstanding'} +========================= 数据字段结束 ======================================= + +以上数据字段和操作符, 按照Description说明组合, 但是每一个 alpha 组合的使用的数据字段和操作符不要过于集中, 在符合语法的情况下, 多尝试不同的组合 + +你再检查一下, 如果你使用了 +Operator abs does not support event inputs +Operator ts_mean does not support event inputs +Operator ts_scale does not support event inputs +Operator add does not support event inputs +Operator sign does not support event inputs +Operator greater does not support event inputs +Operator ts_av_diff does not support event inputs +Operator ts_quantile does not support event inputs +Operator ts_arg_min does not support event inputs +Operator divide does not support event inputs +Operator ts_corr does not support event inputs +Operator ts_decay_linear does not support event inputs +Operator ts_sum does not support event inputs +Operator ts_delay does not support event inputs +Operator ts_arg_max does not support event inputs +Operator ts_std_dev does not support event inputs +Operator ts_regression does not support event inputs +Operator ts_backfill does not support event inputs +Operator signed_power does not support event inputs +Operator ts_product does not support event inputs +Operator ts_zscore does not support event inputs +Operator group_rank does not support event inputs +Operator subtract does not support event inputs +Operator ts_delta does not support event inputs +Operator ts_rank does not support event inputs +Operator ts_count_nans does not support event inputs +Operator ts_covariance does not support event inputs +Operator multiply does not support event inputs +Operator if_else does not support event inputs +Operator group_neutralize does not support event inputs +Operator group_zscore does not support event inputs +Operator winsorize does not support event inputs +注意, 以上操作符不能使用事件类型的数据集, 以上操作符禁止使用事件类型的数据集!! + +!! 数据集使用要求尽量分散, 不要重复使用 \ No newline at end of file diff --git a/manual_prompt/2026/01/27/manual_prompt_20260127110240.txt b/manual_prompt/2026/01/27/manual_prompt_20260127110240.txt new file mode 100644 index 0000000..ae4b0ab --- /dev/null +++ b/manual_prompt/2026/01/27/manual_prompt_20260127110240.txt @@ -0,0 +1,817 @@ +Name +Executive-Employee Pay Equity Premium Factor +Hypothesis +Rooted in behavioral finance equity theory, persistent excessive growth in executive compensation relative to ordinary employees signals internal inequity that erodes morale, accelerates talent attrition, and undermines innovation capacity, impairing long-term fundamentals. Yet investors disproportionately weight absolute executive pay or short-term profits while underpricing the governance and human capital risks embedded in pay structure imbalance. This systematic mispricing leads to overvaluation of high-disparity firms (future returns decline) and undervaluation of equitable firms (future returns rebound). A long-short strategy—long low-disparity firms, short high-disparity firms—captures alpha from this behavioral bias-driven mispricing correction. +Implementation +Core metric: Pay Disparity Index = (YoY growth of total executive compensation minus YoY growth of average employee compensation), where average employee compensation = "Cash paid to and on behalf of employees" / "Total employees", executive compensation sourced from cash flow statement notes. Apply cross-sectional Z-score standardization followed by industry neutralization (percentile ranking within CSI一级行业). Impute missing values using rolling 12-month individual stock median. Auxiliary validation variables: quarterly employee turnover rate (recruitment platform data), NLP sentiment score of "employee welfare" sections in annual reports (satisfaction proxy), YoY growth of patent applications. Factor signed negatively (Factor Value = -standardized Disparity Index); high factor value indicates low disparity for long leg. + +*=========================================================================================* +输出格式： +输出必须是且仅是纯文本。 +每一行是一个完整、独立、语法正确的WebSim表达式。 +严禁任何形式的解释、编号、标点包裹（如引号）、Markdown格式或额外文本。 +===================== !!! 重点(输出方式) !!! ===================== +现在，请严格遵守以上所有规则，开始生成可立即在WebSim中运行的复合因子表达式。 +不要自行假设, 你需要用到的操作符 和 数据集, 必须从我提供给你的里面查找, 并严格按照里面的使用方法进行组合 +**输出格式**(一行一个表达式, 每个表达式中间需要添加一个空行, 只要表达式本身, 不需要赋值, 不要解释, 不需要序号, 也不要输出多余的东西)： +表达式 +表达式 +表达式 +... +表达式 +================================================================= +重申：请确保所有表达式都使用WorldQuant WebSim平台函数，不要使用pandas、numpy或其他Python库函数。输出必须是一行有效的WQ表达式。 +以下是我的账号有权限使用的操作符, 请严格按照操作符, 以及我提供的数据集, 进行生成,组合 50 个 alpha： +不要自行假设, 你需要用到的操作符 和 数据集, 必须从我提供给你的里面查找, 并严格按照里面的使用方法进行组合 +!! 数据集使用要求尽量分散, 不要重复使用 +!! 数据集使用要求尽量分散, 不要重复使用 +================================================================= +**操作符汇总 +**算术运算符 (Arithmetic): +abs(x) - 绝对值 +add(x, y, filter=false) - 加法 (x + y) +densify(x) - 分组字段稠密化 +divide(x, y) - 除法 (x / y) +inverse(x) - 倒数 (1/x) +log(x) - 自然对数 +max(x, y, ..) - 最大值 +min(x, y, ..) - 最小值 +multiply(x, y, filter=false) - 乘法 (x * y) +power(x, y) - 幂运算 (x^y) +reverse(x) - 取反 (-x) +sign(x) - 符号函数 +signed_power(x, y) - 保留符号的幂运算 +sqrt(x) - 平方根 +subtract(x, y, filter=false) - 减法 (x - y) +to_nan(x, value=0, reverse=false) - 值与NaN转换 +**逻辑运算符 (Logical): +and(input1, input2) - 逻辑与 +if_else(input1, input2, input3) - 条件判断 +input1 < input2 - 小于比较 +input1 <= input2 - 小于等于 +input1 == input2 - 等于比较 +input1 > input2 - 大于比较 +input1 >= input2 - 大于等于 +input1 != input2 - 不等于 +is_nan(input) - 是否为NaN +not(x) - 逻辑非 +or(input1, input2) - 逻辑或 +**时间序列运算符 (Time Series): +days_from_last_change(x) - 上次变化天数 +hump(x, hump=0.01) - 限制变化幅度 +jump_decay(x, d, sensitivity=0.5, force=0.1) - 跳跃衰减 +kth_element(x, d, k) - 第K个值 +last_diff_value(x, d) - 最后一个不同值 +ts_arg_max(x, d) - 最大值相对索引 +ts_arg_min(x, d) - 最小值相对索引 +ts_av_diff(x, d) - 与均值的差 +ts_backfill(x, lookback=d, k=1, ignore="NAN") - 回填 +ts_corr(x, y, d) - 时间序列相关性 +ts_count_nans(x, d) - NaN计数 +ts_covariance(y, x, d) - 协方差 +ts_decay_linear(x, d, dense=false) - 线性衰减 +ts_delay(x, d) - 延迟值 +ts_delta(x, d) - 差值 (x - 延迟值) +ts_max(x, d) - 时间序列最大值 +ts_mean(x, d) - 时间序列均值 +ts_min(x, d) - 时间序列最小值 +ts_product(x, d) - 时间序列乘积 +ts_quantile(x, d, driver="gaussian") - 分位数 +ts_rank(x, d, constant=0) - 时间序列排名 +ts_regression(y, x, d, lag=0, rettype=0) - 回归分析 +ts_scale(x, d, constant=0) - 时间序列缩放 +ts_std_dev(x, d) - 时间序列标准差 +ts_step(1) - 天数计数器 +ts_sum(x, d) - 时间序列求和 +ts_target_tvr_decay(x, lambda_min=0, lambda_max=1, target_tvr=0.1) - 目标换手率衰减 +ts_target_tvr_delta_limit(x, y, lambda_min=0, lambda_max=1, target_tvr=0.1) - 目标换手率差值限制 +ts_zscore(x, d) - 时间序列Z分数 +**横截面运算符 (Cross Sectional): +normalize(x, useStd=false, limit=0.0) - 标准化 +quantile(x, driver=gaussian, sigma=1.0) - 分位数转换 +rank(x, rate=2) - 排名 +scale(x, scale=1, longscale=1, shortscale=1) - 缩放 +scale_down(x, constant=0) - 按比例缩放 +vector_neut(x, y) - 向量中性化 +winsorize(x, std=4) - 缩尾处理 +zscore(x) - Z分数 +**向量运算符 (Vector): +vec_avg(x) - 向量均值 +vec_max(x) - 向量最大值 +vec_min(x) - 向量最小值 +vec_sum(x) - 向量求和 +**变换运算符 (Transformational): +bucket(rank(x), range="0,1,0.1" or buckets="2,5,6,7,10") - 分桶 +generate_stats(alpha) - 生成统计量 +trade_when(x, y, z) - 条件交易 +**分组运算符 (Group): +combo_a(alpha, nlength=250, mode='algo1') - 组合Alpha +group_backfill(x, group, d, std=4.0) - 分组回填 +group_cartesian_product(g1, g2) - 笛卡尔积分组 +group_max(x, group) - 分组最大值 +group_mean(x, weight, group) - 分组均值 +group_min(x, group) - 分组最小值 +group_neutralize(x, group) - 分组中性化 +group_rank(x, group) - 分组排名 +group_scale(x, group) - 分组缩放 +group_zscore(x, group) - 分组Z分数 +**特殊运算符 (Special): +in - 包含判断 +self_corr(input) - 自相关性 +universe_size - 宇宙大小 +**归约运算符 (Reduce): +reduce_avg(input, threshold=0) - 平均值归约 +reduce_choose(input, nth, ignoreNan=true) - 选择归约 +reduce_count(input, threshold) - 计数归约 +reduce_ir(input) - IR归约 +reduce_kurtosis(input) - 峰度归约 +reduce_max(input) - 最大值归约 +reduce_min(input) - 最小值归约 +reduce_norm(input) - 范数归约 +reduce_percentage(input, percentage=0.5) - 百分比归约 +reduce_powersum(input, constant=2, precise=false) - 幂和归约 +reduce_range(input) - 范围归约 +reduce_skewness(input) - 偏度归约 +reduce_stddev(input, threshold=0) - 标准差归约 +reduce_sum(input) - 求和归约 + +以下是我的账号有权限使用的操作符, 请严格按照操作符, 进行生成,组合因子 + +========================= 操作符开始 ======================================= +注意: Operator: 后面的是操作符(是可以使用的), +Description: 此字段后面的是操作符对应的描述或使用说明(禁止使用, 仅供参考), Description字段后面的内容是使用说明, 不是操作符 +特别注意!!!! 必须按照操作符字段Operator的使用说明生成 alphaOperator: abs(x) +Description: Absolute value of x +Operator: add(x, y, filter = false) +Description: Add all inputs (at least 2 inputs required). If filter = true, filter all input NaN to 0 before adding +Operator: densify(x) +Description: Converts a grouping field of many buckets into lesser number of only available buckets so as to make working with grouping fields computationally efficient +Operator: divide(x, y) +Description: x / y +Operator: inverse(x) +Description: 1 / x +Operator: log(x) +Description: Natural logarithm. For example: Log(high/low) uses natural logarithm of high/low ratio as stock weights. +Operator: max(x, y, ..) +Description: Maximum value of all inputs. At least 2 inputs are required +Operator: min(x, y ..) +Description: Minimum value of all inputs. At least 2 inputs are required +Operator: multiply(x ,y, ... , filter=false) +Description: Multiply all inputs. At least 2 inputs are required. Filter sets the NaN values to 1 +Operator: power(x, y) +Description: x ^ y +Operator: reverse(x) +Description: - x +Operator: sign(x) +Description: if input > 0, return 1; if input < 0, return -1; if input = 0, return 0; if input = NaN, return NaN; +Operator: signed_power(x, y) +Description: x raised to the power of y such that final result preserves sign of x +Operator: sqrt(x) +Description: Square root of x +Operator: subtract(x, y, filter=false) +Description: x-y. If filter = true, filter all input NaN to 0 before subtracting +Operator: and(input1, input2) +Description: Logical AND operator, returns true if both operands are true and returns false otherwise +Operator: if_else(input1, input2, input 3) +Description: If input1 is true then return input2 else return input3. +Operator: input1 < input2 +Description: If input1 < input2 return true, else return false +Operator: input1 <= input2 +Description: Returns true if input1 <= input2, return false otherwise +Operator: input1 == input2 +Description: Returns true if both inputs are same and returns false otherwise +Operator: input1 > input2 +Description: Logic comparison operators to compares two inputs +Operator: input1 >= input2 +Description: Returns true if input1 >= input2, return false otherwise +Operator: input1!= input2 +Description: Returns true if both inputs are NOT the same and returns false otherwise +Operator: is_nan(input) +Description: If (input == NaN) return 1 else return 0 +Operator: not(x) +Description: Returns the logical negation of x. If x is true (1), it returns false (0), and if input is false (0), it returns true (1). +Operator: or(input1, input2) +Description: Logical OR operator returns true if either or both inputs are true and returns false otherwise +Operator: days_from_last_change(x) +Description: Amount of days since last change of x +Operator: hump(x, hump = 0.01) +Description: Limits amount and magnitude of changes in input (thus reducing turnover) +Operator: kth_element(x, d, k) +Description: Returns K-th value of input by looking through lookback days. This operator can be used to backfill missing data if k=1 +Operator: last_diff_value(x, d) +Description: Returns last x value not equal to current x value from last d days +Operator: ts_arg_max(x, d) +Description: Returns the relative index of the max value in the time series for the past d days. If the current day has the max value for the past d days, it returns 0. If previous day has the max value for the past d days, it returns 1 +Operator: ts_arg_min(x, d) +Description: Returns the relative index of the min value in the time series for the past d days; If the current day has the min value for the past d days, it returns 0; If previous day has the min value for the past d days, it returns 1. +Operator: ts_av_diff(x, d) +Description: Returns x - tsmean(x, d), but deals with NaNs carefully. That is NaNs are ignored during mean computation +Operator: ts_backfill(x,lookback = d, k=1, ignore="NAN") +Description: Backfill is the process of replacing the NAN or 0 values by a meaningful value (i.e., a first non-NaN value) +Operator: ts_corr(x, y, d) +Description: Returns correlation of x and y for the past d days +Operator: ts_count_nans(x ,d) +Description: Returns the number of NaN values in x for the past d days +Operator: ts_covariance(y, x, d) +Description: Returns covariance of y and x for the past d days +Operator: ts_decay_linear(x, d, dense = false) +Description: Returns the linear decay on x for the past d days. Dense parameter=false means operator works in sparse mode and we treat NaN as 0. In dense mode we do not. +Operator: ts_delay(x, d) +Description: Returns x value d days ago +Operator: ts_delta(x, d) +Description: Returns x - ts_delay(x, d) +Operator: ts_mean(x, d) +Description: Returns average value of x for the past d days. +Operator: ts_product(x, d) +Description: Returns product of x for the past d days +Operator: ts_quantile(x,d, driver="gaussian" ) +Description: It calculates ts_rank and apply to its value an inverse cumulative density function from driver distribution. Possible values of driver (optional ) are "gaussian", "uniform", "cauchy" distribution where "gaussian" is the default. +Operator: ts_rank(x, d, constant = 0) +Description: Rank the values of x for each instrument over the past d days, then return the rank of the current value + constant. If not specified, by default, constant = 0. +Operator: ts_regression(y, x, d, lag = 0, rettype = 0) +Description: Returns various parameters related to regression function +Operator: ts_scale(x, d, constant = 0) +Description: Returns (x - ts_min(x, d)) / (ts_max(x, d) - ts_min(x, d)) + constant. This operator is similar to scale down operator but acts in time series space +Operator: ts_std_dev(x, d) +Description: Returns standard deviation of x for the past d days +Operator: ts_step(1) +Description: Returns days' counter +Operator: ts_sum(x, d) +Description: Sum values of x for the past d days. +Operator: ts_zscore(x, d) +Description: Z-score is a numerical measurement that describes a value's relationship to the mean of a group of values. Z-score is measured in terms of standard deviations from the mean: (x - tsmean(x,d)) / tsstddev(x,d). This operator may help reduce outliers and drawdown. +Operator: normalize(x, useStd = false, limit = 0.0) +Description: Calculates the mean value of all valid alpha values for a certain date, then subtracts that mean from each element +Operator: quantile(x, driver = gaussian, sigma = 1.0) +Description: Rank the raw vector, shift the ranked Alpha vector, apply distribution (gaussian, cauchy, uniform). If driver is uniform, it simply subtract each Alpha value with the mean of all Alpha values in the Alpha vector +Operator: rank(x, rate=2) +Description: Ranks the input among all the instruments and returns an equally distributed number between 0.0 and 1.0. For precise sort, use the rate as 0 +Operator: scale(x, scale=1, longscale=1, shortscale=1) +Description: Scales input to booksize. We can also scale the long positions and short positions to separate scales by mentioning additional parameters to the operator +Operator: winsorize(x, std=4) +Description: Winsorizes x to make sure that all values in x are between the lower and upper limits, which are specified as multiple of std. +Operator: zscore(x) +Description: Z-score is a numerical measurement that describes a value's relationship to the mean of a group of values. Z-score is measured in terms of standard deviations from the mean +Operator: vec_avg(x) +Description: Taking mean of the vector field x +Operator: vec_sum(x) +Description: Sum of vector field x +Operator: bucket(rank(x), range="0, 1, 0.1" or buckets = "2,5,6,7,10") +Description: Convert float values into indexes for user-specified buckets. Bucket is useful for creating group values, which can be passed to GROUP as input +Operator: trade_when(x, y, z) +Description: Used in order to change Alpha values only under a specified condition and to hold Alpha values in other cases. It also allows to close Alpha positions (assign NaN values) under a specified condition +Operator: group_backfill(x, group, d, std = 4.0) +Description: If a certain value for a certain date and instrument is NaN, from the set of same group instruments, calculate winsorized mean of all non-NaN values over last d days +Operator: group_mean(x, weight, group) +Description: All elements in group equals to the mean +Operator: group_neutralize(x, group) +Description: Neutralizes Alpha against groups. These groups can be subindustry, industry, sector, country or a constant +Operator: group_rank(x, group) +Description: Each elements in a group is assigned the corresponding rank in this group +Operator: group_scale(x, group) +Description: Normalizes the values in a group to be between 0 and 1. (x - groupmin) / (groupmax - groupmin) +Operator: group_zscore(x, group) +Description: Calculates group Z-score - numerical measurement that describes a value's relationship to the mean of a group of values. Z-score is measured in terms of standard deviations from the mean. zscore = (data - mean) / stddev of x for each instrument within its group. +========================= 操作符结束 ======================================= + +========================= 数据字段开始 ======================================= +注意: data_set_name: 后面的是数据字段(可以使用), description: 此字段后面的是数据字段对应的描述或使用说明(不能使用) + +{'id': 60821, 'data_set_name': '可以使用:anl14_high_nav_fy2', 'description': '不可使用,仅供参考:The Highest Estimation of Net Asset Value - Upcoming 2 Years'} +{'id': 373385, 'data_set_name': '可以使用:industry_grouping_level2_top3000_2', 'description': '不可使用,仅供参考:Industry classification group at level 2 for the top 3000 instruments.'} +{'id': 139391, 'data_set_name': '可以使用:fnd28_fsa1_value_02051a', 'description': '不可使用,仅供参考:value of annual field: Receivables (Net)'} +{'id': 60959, 'data_set_name': '可以使用:anl14_low_eps_fy5', 'description': '不可使用,仅供参考:The Lowest Estimation of Earnings Per Share - upcoming 5 years'} +{'id': 140059, 'data_set_name': '可以使用:fnd28_value_19524', 'description': '不可使用,仅供参考:value of field: Product Segment 3 - Capital Expenditures'} +{'id': 139335, 'data_set_name': '可以使用:fnd28_cfsourceusea_value_04001a', 'description': '不可使用,仅供参考:value of annual field: Net Income/Starting Line'} +{'id': 62136, 'data_set_name': '可以使用:quarterly_price_to_sales_ratio_3', 'description': '不可使用,仅供参考:Price divided by sales per share for the quarter.'} +{'id': 60820, 'data_set_name': '可以使用:anl14_high_nav_fy1', 'description': '不可使用,仅供参考:The Highest Estimation of Net Asset Value - upcoming year'} +{'id': 139302, 'data_set_name': '可以使用:fnd28_cfq_value_04053q', 'description': '不可使用,仅供参考:value of quarterly field: Asset Disposal'} +{'id': 140599, 'data_set_name': '可以使用:fnd72_pit_or_cf_q_cf_other_fnc_act', 'description': '不可使用,仅供参考:Other Financing Activities'} +{'id': 139082, 'data_set_name': '可以使用:short_term_loans_advances', 'description': '不可使用,仅供参考:Short-term loans and advances provided by the company.'} +{'id': 62089, 'data_set_name': '可以使用:quarterly_long_term_debt_to_equity_2', 'description': '不可使用,仅供参考:Long-term debt divided by shareholder equity for the quarter.'} +{'id': 139406, 'data_set_name': '可以使用:fnd28_fsa1_value_03054a', 'description': '不可使用,仅供参考:value of annual field: Accrued Payroll'} +{'id': 140858, 'data_set_name': '可以使用:fnd72_s_pit_or_cf_q_cf_form_of_statement', 'description': '不可使用,仅供参考:No longer supported'} +{'id': 61994, 'data_set_name': '可以使用:quarterly_cash_flow_per_share', 'description': '不可使用,仅供参考:Cash flow divided by average shares for the most recent quarter.'} +{'id': 137363, 'data_set_name': '可以使用:quarterly_return_on_equity_percent_3', 'description': '不可使用,仅供参考:Return on average equity for the most recent quarter (annualized).'} +{'id': 139809, 'data_set_name': '可以使用:fnd28_ratesq_value_08801q', 'description': '不可使用,仅供参考:value of quarterly field: Total Investment Return year ago Growth Rates'} +{'id': 60961, 'data_set_name': '可以使用:anl14_low_epsrep_fp2', 'description': '不可使用,仅供参考:The Lowest Estimation of Reported Earnings Per Share - upcoming 2 quarters'} +{'id': 139769, 'data_set_name': '可以使用:fnd28_pftlta_value_08655a', 'description': '不可使用,仅供参考:value of annual field: Reinvestment Rate - Per Share - 5 Yr Avg'} +{'id': 136387, 'data_set_name': '可以使用:estimated_value_exercisable_options', 'description': '不可使用,仅供参考:Estimated value of exercisable options awarded in the period.'} +{'id': 136845, 'data_set_name': '可以使用:fnd17_alldelay1_qpelow', 'description': '不可使用,仅供参考:P/E excluding extraordinary items low, most recent quarter'} +{'id': 62042, 'data_set_name': '可以使用:quarterly_ebitda', 'description': '不可使用,仅供参考:Earnings before interest, taxes, depreciation, and amortization for the quarter.'} +{'id': 62004, 'data_set_name': '可以使用:quarterly_cash_per_share_2', 'description': '不可使用,仅供参考:Cash per share for the most recent quarter.'} +{'id': 61205, 'data_set_name': '可以使用:anl14_median_capex_fp4', 'description': '不可使用,仅供参考:Median of Estimations of Capital Expenditures - upcoming 4 quarters'} +{'id': 137388, 'data_set_name': '可以使用:reporting_to_pricing_fx_rate', 'description': '不可使用,仅供参考:Exchange rate used to convert reporting currency to pricing currency.'} +{'id': 137530, 'data_set_name': '可以使用:extraordinary_gains', 'description': '不可使用,仅供参考:[Quarterly] Actuarial Gains and Losses - Domestic'} +{'id': 61200, 'data_set_name': '可以使用:anl14_median_bvps_fy4', 'description': '不可使用,仅供参考:Median of estimations of book value per share - upcoming 4 years'} +{'id': 139727, 'data_set_name': '可以使用:fnd28_newq_value_08801q', 'description': '不可使用,仅供参考:value of quarterly field: Total Investment Return year ago Growth Rates'} +{'id': 137470, 'data_set_name': '可以使用:annual_short_term_creditors', 'description': '不可使用,仅供参考:Annual amount of short-term creditors at the reporting date.'} +{'id': 139635, 'data_set_name': '可以使用:fnd28_nddq1_value_04302q', 'description': '不可使用,仅供参考:value of quarterly field: Other Proceeds From Sale/Issuance of Stock'} +{'id': 139434, 'data_set_name': '可以使用:fnd28_fsq1_value_01250q', 'description': '不可使用,仅供参考:value of quarterly field: Operating Income'} +{'id': 62255, 'data_set_name': '可以使用:anl44_2_dps_value', 'description': '不可使用,仅供参考:dps value'} +{'id': 139233, 'data_set_name': '可以使用:fnd28_bdeq_value_03245q', 'description': '不可使用,仅供参考:value of quarterly field: Long Term Debt Excluding Capitalized Leases'} +{'id': 139566, 'data_set_name': '可以使用:fnd28_levliqa_value_08236a', 'description': '不可使用,仅供参考:value of annual field: Total Debt % Total Assets'} +{'id': 140063, 'data_set_name': '可以使用:fnd28_value_19533', 'description': '不可使用,仅供参考:value of field: Product Segment 4 - Assets'} +{'id': 137415, 'data_set_name': '可以使用:twelve_month_low_price', 'description': '不可使用,仅供参考:The lowest price at which the security traded in the last twelve months.'} +{'id': 60925, 'data_set_name': '可以使用:anl14_low_div_fy1', 'description': '不可使用,仅供参考:The lowest estimation of dividend - upcoming year'} +{'id': 139052, 'data_set_name': '可以使用:shareholders_other_equity_2', 'description': '不可使用,仅供参考:Other equity attributable to shareholders.'} +{'id': 61284, 'data_set_name': '可以使用:anl14_median_ntp_fp3', 'description': '不可使用,仅供参考:Median of estimations of net profit - upcoming 3 quarters'} +{'id': 61032, 'data_set_name': '可以使用:anl14_low_roa_fy4', 'description': '不可使用,仅供参考:The lowest estimation of Returns on Assets - upcoming 4 years'} +{'id': 137478, 'data_set_name': '可以使用:benefit_expense_total', 'description': '不可使用,仅供参考:Total benefit expense for the period.'} +{'id': 139551, 'data_set_name': '可以使用:fnd28_ishtspmnt_value_18198a', 'description': '不可使用,仅供参考:value of annual field: Earnings before Interest, Taxes, Depreciation & Amortization (EBITDA)'} +{'id': 61221, 'data_set_name': '可以使用:anl14_median_div_fp2', 'description': '不可使用,仅供参考:Median of Estimations of Dividend - upcoming 2 quarters'} +{'id': 60634, 'data_set_name': '可以使用:anl11_esgreg_industryperc', 'description': '不可使用,仅供参考:Industry Percentile on ESG Score with 100% being the best'} +{'id': 137392, 'data_set_name': '可以使用:revenue_per_share_annual', 'description': '不可使用,仅供参考:Total revenue divided by average diluted shares outstanding for the most recent fiscal year.'} +{'id': 139748, 'data_set_name': '可以使用:fnd28_pftlta_value_08340a', 'description': '不可使用,仅供参考:value of annual field: Selling, General & Administrative Exp/Sales - 5 Yr Avg'} +{'id': 60653, 'data_set_name': '可以使用:anl11_g_totalcorregsectrnk', 'description': '不可使用,仅供参考:Sector Rank on G Hybrid Score with 1 being the highest rank'} +{'id': 136597, 'data_set_name': '可以使用:fnd17_6_reptoprcexrate', 'description': '不可使用,仅供参考:Reporting Currency to base currency exchange rate'} +{'id': 140037, 'data_set_name': '可以使用:fnd28_value_18457q', 'description': '不可使用,仅供参考:value of quarterly field: Earnings per Share Included Extra Pre-tax Cr/Chg Basic Revised'} +{'id': 139390, 'data_set_name': '可以使用:fnd28_fsa1_value_02001a', 'description': '不可使用,仅供参考:value of annual field: Cash & Short Term Investments'} +{'id': 62018, 'data_set_name': '可以使用:quarterly_current_liabilities', 'description': '不可使用,仅供参考:Sum of all current liabilities for the most recent quarter.'} +{'id': 62130, 'data_set_name': '可以使用:quarterly_pretax_margin_percent_2', 'description': '不可使用,仅供参考:Pretax margin percentage for the most recent quarter.'} +{'id': 136525, 'data_set_name': '可以使用:annual_book_value_per_share_2', 'description': '不可使用,仅供参考:Book value per share for the most recent fiscal year.'} +{'id': 139660, 'data_set_name': '可以使用:fnd28_nddq1_value_05301q', 'description': '不可使用,仅供参考:value of quarterly field: Common Shares Outstanding'} +{'id': 373414, 'data_set_name': '可以使用:pca_industry_grouping_method4_20', 'description': '不可使用,仅供参考:Industry grouping using fourth method and 20 clusters for top 3000 equities.'} +{'id': 139322, 'data_set_name': '可以使用:fnd28_cfq_value_04795q', 'description': '不可使用,仅供参考:value of quarterly field: Other Uses - Investing'} +{'id': 373203, 'data_set_name': '可以使用:average_spread_slippage', 'description': '不可使用,仅供参考:Average slippage experienced due to spread during trade execution.'} +{'id': 60953, 'data_set_name': '可以使用:anl14_low_eps_fp4', 'description': '不可使用,仅供参考:The lowest estimation of earnings per share - upcoming 4 quarters'} +{'id': 62190, 'data_set_name': '可以使用:quarterly_total_debt', 'description': '不可使用,仅供参考:Total debt reported for the most recent quarter.'} +{'id': 60730, 'data_set_name': '可以使用:anl14_actvalue_epsrep_fp0', 'description': '不可使用,仅供参考:Reported Earnings Per Share - recent last quarter'} +{'id': 139832, 'data_set_name': '可以使用:fnd28_statisticsa_value_07230a', 'description': '不可使用,仅供参考:value of annual field: Total Assets (U.S.$)'} +{'id': 140906, 'data_set_name': '可以使用:fnd72_s_pit_or_is_q_is_sh_for_diluted_eps', 'description': '不可使用,仅供参考:Weighted average number of shares used to calculate Diluted EPS'} +{'id': 139256, 'data_set_name': '可以使用:fnd28_bsasseta_value_02051a', 'description': '不可使用,仅供参考:value of annual field: Receivables (Net)'} +{'id': 60857, 'data_set_name': '可以使用:anl14_high_ptp_fp2', 'description': '不可使用,仅供参考:The highest estimation of pretax profit - upcoming 2 quarters'} +{'id': 136609, 'data_set_name': '可以使用:fnd17_9_reptoprcexrate', 'description': '不可使用,仅供参考:Reporting Currency to base currency exchange rate'} +{'id': 140875, 'data_set_name': '可以使用:fnd72_s_pit_or_is_q_earn_for_common', 'description': '不可使用,仅供参考:Net Income Available To Common Shareholders'} +{'id': 139661, 'data_set_name': '可以使用:fnd28_nddq1_value_05303q', 'description': '不可使用,仅供参考:value of quarterly field: Treasury Shares'} +{'id': 140133, 'data_set_name': '可以使用:fnd31_devnorthamericaadditionalfactor4_slope4qeps5y', 'description': '不可使用,仅供参考:Slope of 5-yr TTM EPS Trend Line. It is defined as the slope coefficient between monthly dates and the corresponding trailing 12-month earnings per share before extra items in the prior 20 quarters.'} +{'id': 139843, 'data_set_name': '可以使用:fnd28_stpera_value_08801a', 'description': '不可使用,仅供参考:value of annual field: Total Investment Return year ago Growth Rates'} +{'id': 139755, 'data_set_name': '可以使用:fnd28_pftlta_value_08356a', 'description': '不可使用,仅供参考:value of annual field: Interest Rate - Estimated Average'} +{'id': 137394, 'data_set_name': '可以使用:revenue_per_share_quarterly_non_annualized', 'description': '不可使用,仅供参考:Revenue per share for the most recent quarter, not annualized.'} +{'id': 373310, 'data_set_name': '可以使用:pv173_ranksmt5yzspreadzscore60dsbst', 'description': '不可使用,仅供参考:It is defined as the 60-day z-score of 5-year mid z-spreadIn the bond z-spread curve'} +{'id': 139358, 'data_set_name': '可以使用:fnd28_cfsourceusea_value_04760a', 'description': '不可使用,仅供参考:value of annual field: Increase in Investments'} +{'id': 62171, 'data_set_name': '可以使用:quarterly_revenue_per_share_nonannualized', 'description': '不可使用,仅供参考:Revenue per share for the quarter, not annualized.'} +{'id': 61241, 'data_set_name': '可以使用:anl14_median_ebitda_fp2', 'description': '不可使用,仅供参考:Median of estimations of earnings before interest, taxes, depreciation & amortization - upcoming 2 quarters'} +{'id': 373214, 'data_set_name': '可以使用:transaction_cost_median', 'description': '不可使用,仅供参考:Median estimated transaction cost for a trade.'} +{'id': 136574, 'data_set_name': '可以使用:fnd17_2_reptoprcexrate', 'description': '不可使用,仅供参考:Reporting Currency to base currency exchange rate'} +{'id': 139709, 'data_set_name': '可以使用:fnd28_newq_value_08326q', 'description': '不可使用,仅供参考:value of quarterly field: Return on Assets'} +{'id': 60640, 'data_set_name': '可以使用:anl11_g1reg_industryrnk', 'description': '不可使用,仅供参考:Industry Rank on G1 1 being the highest rank'} +{'id': 373524, 'data_set_name': '可以使用:top1500_pca_factor2_grouping50', 'description': '不可使用,仅供参考:Second principal component grouping for top 1500 securities with 50 clusters.'} +{'id': 136530, 'data_set_name': '可以使用:annual_debt_to_total_capital', 'description': '不可使用,仅供参考:Ratio of total debt to total capital for the most recent fiscal year.'} +{'id': 140166, 'data_set_name': '可以使用:fnd31_qsg5additionalfactor3_monthly_pbwt', 'description': '不可使用,仅供参考:Time Weighted Book Yield. It is defined as the time-weighted average of book value per share estimates in fiscal year 1 and fiscal year 2 divided by close price.'} +{'id': 62149, 'data_set_name': '可以使用:quarterly_reinvestment_rate_2', 'description': '不可使用,仅供参考:Percentage of earnings reinvested in the company for the quarter.'} +{'id': 62216, 'data_set_name': '可以使用:reporting_to_pricing_currency_exrate', 'description': '不可使用,仅供参考:Exchange rate between reporting currency and pricing currency.'} +{'id': 373535, 'data_set_name': '可以使用:top2000_factor2_group50_score', 'description': '不可使用,仅供参考:Second factor score for top 2000 securities, grouped into 50 clusters.'} +{'id': 62273, 'data_set_name': '可以使用:anl44_2_eps_lastactvalue', 'description': '不可使用,仅供参考:eps lastactvalue'} +{'id': 139775, 'data_set_name': '可以使用:fnd28_pftlta_value_08915a', 'description': '不可使用,仅供参考:value of annual field: Retained Earnings % Equity'} +{'id': 60915, 'data_set_name': '可以使用:anl14_low_cfps_fp4', 'description': '不可使用,仅供参考:The Lowest Estimation of Cash Flow Per Share - upcoming 4 quarters'} +{'id': 140077, 'data_set_name': '可以使用:fnd28_wcannuala_value_08321', 'description': '不可使用,仅供参考:value of field: Pre-tax Margin'} +{'id': 60905, 'data_set_name': '可以使用:anl14_low_capex_fp4', 'description': '不可使用,仅供参考:The lowest estimation of capital expenditures - upcoming 4 quarters'} +{'id': 60754, 'data_set_name': '可以使用:anl14_high_capex_fp3', 'description': '不可使用,仅供参考:The Highest Estimation of Capital Expenditures - upcoming 3 quarters'} +{'id': 62202, 'data_set_name': '可以使用:quarterly_total_debt_to_equity_ratio_prior_year', 'description': '不可使用,仅供参考:Total debt divided by equity for the same quarter last year.'} +{'id': 61971, 'data_set_name': '可以使用:free_cash_flow_quarterly', 'description': '不可使用,仅供参考:Free cash flow for the most recent quarter.'} +{'id': 139712, 'data_set_name': '可以使用:fnd28_newq_value_08371q', 'description': '不可使用,仅供参考:value of quarterly field: Return on Equity - per Share'} +{'id': 139725, 'data_set_name': '可以使用:fnd28_newq_value_08651q', 'description': '不可使用,仅供参考:value of quarterly field: Reinvestment Rate - per Share'} +{'id': 139230, 'data_set_name': '可以使用:fnd28_bdeq_value_03066q', 'description': '不可使用,仅供参考:value of quarterly field: Other Current Liabilities'} +{'id': 373367, 'data_set_name': '可以使用:pv173_zscoresshift60dchg60d', 'description': '不可使用,仅供参考:It is defined as the 60-day change of ShiftIn Bond Z-spread Curve.'} +{'id': 60796, 'data_set_name': '可以使用:anl14_high_ebitda_fy2', 'description': '不可使用,仅供参考:The Highest Estimation of Earnings Before Interest, Taxes, Depreciation & Amortization - upcoming 2 years'} +{'id': 139410, 'data_set_name': '可以使用:fnd28_fsa1_value_03245a', 'description': '不可使用,仅供参考:value of annual field: Long Term Debt Excluding Capitalized Leases'} +{'id': 139825, 'data_set_name': '可以使用:fnd28_statisticsa_value_05502a', 'description': '不可使用,仅供参考:value of annual field: Cash Flow per Share Fiscal Period'} +{'id': 139776, 'data_set_name': '可以使用:fnd28_ratesq_value_08106q', 'description': '不可使用,仅供参考:value of quarterly field: Current Ratio'} +{'id': 373371, 'data_set_name': '可以使用:pv173_zscorestilt5y1ydiff60dema60d', 'description': '不可使用,仅供参考:It is defined as the 60-day exponential average of the difference of factor loading of the second principal component for the 5-year z-spread and 1-year z-spread'} +{'id': 140843, 'data_set_name': '可以使用:fnd72_s_pit_or_bs_q_minority_noncontrolling_interest', 'description': '不可使用,仅供参考:Minority Interest'} +{'id': 136376, 'data_set_name': '可以使用:bonus_proportion_total_comp', 'description': '不可使用,仅供参考:Bonus as a proportion of total direct compensation.'} +{'id': 62173, 'data_set_name': '可以使用:quarterly_sga_to_sales_ratio', 'description': '不可使用,仅供参考:Selling, general, and administrative expenses as a percent of revenue for the quarter.'} +{'id': 139046, 'data_set_name': '可以使用:share_dividend_equity', 'description': '不可使用,仅供参考:Share dividend equity reported for the annual period.'} +{'id': 60918, 'data_set_name': '可以使用:anl14_low_cfps_fy3', 'description': '不可使用,仅供参考:The Lowest Estimation of Cash Flow Per Share - Upcoming 3 Years'} +{'id': 140777, 'data_set_name': '可以使用:fnd72_s_pit_or_bs_q_2_bs_other_cur_asset_less_prepay', 'description': '不可使用,仅供参考:Other assets which are not account and notes receivable, inventory, derivative assets, deferred tax assets, prepaid expenses, assets for held for sale, income taxes receivable, and assets of discontinued operations'} +{'id': 62228, 'data_set_name': '可以使用:ttm_return_on_equity_percent_2', 'description': '不可使用,仅供参考:Net income divided by average equity for trailing twelve months, as a percentage.'} +{'id': 61334, 'data_set_name': '可以使用:anl14_median_roe_fp2', 'description': '不可使用,仅供参考:Median of Estimations of Returns on Equity - Upcoming 2 Quarters'} +{'id': 60581, 'data_set_name': '可以使用:anl11_e_totalcor', 'description': "不可使用,仅供参考:The E Hybrid Score weights the three E scores based on which are strongly rank correlated (to the positive) to financial metrics related to 1yr and 3 yr total returns for that company's peer group."} +{'id': 62085, 'data_set_name': '可以使用:quarterly_long_term_debt_to_assets_2', 'description': '不可使用,仅供参考:Long-term debt divided by total assets for the quarter.'} +{'id': 140794, 'data_set_name': '可以使用:fnd72_s_pit_or_bs_q_bs_accts_rec_excl_notes_rec', 'description': '不可使用,仅供参考:Accounts receivable with maturities of less than one year directly relating to operating activities'} +{'id': 60627, 'data_set_name': '可以使用:anl11_esg_totalcorregsubsecrnk', 'description': '不可使用,仅供参考:SubSector Rank on ESG Hybrid Score with 1 being the highest rank'} +{'id': 60864, 'data_set_name': '可以使用:anl14_high_ptprep_fy1', 'description': '不可使用,仅供参考:The highest estimation of reported pretax profit - upcoming year'} +{'id': 60666, 'data_set_name': '可以使用:anl11_greg_industryrnk', 'description': '不可使用,仅供参考:Industry Rank on G Score with 1 being the highest rank'} +{'id': 61240, 'data_set_name': '可以使用:anl14_median_ebitda_fp1', 'description': '不可使用,仅供参考:Median of Estimations of Earnings Before Interest, Taxes, Depreciation & Amortization - upcoming quarter'} +{'id': 60771, 'data_set_name': '可以使用:anl14_high_div_fp2', 'description': '不可使用,仅供参考:The highest estimation of dividend - upcoming 2 quarters'} +{'id': 373437, 'data_set_name': '可以使用:resampled_pca_factor_7', 'description': '不可使用,仅供参考:Seventh principal component factor derived from robust resampling of returns for top 2000 securities.'} +{'id': 139707, 'data_set_name': '可以使用:fnd28_newq_value_08316q', 'description': '不可使用,仅供参考:value of quarterly field: Operating Profit Margin'} +{'id': 136601, 'data_set_name': '可以使用:fnd17_7_reptoprcexrate', 'description': '不可使用,仅供参考:Reporting Currency to base currency exchange rate'} +{'id': 62186, 'data_set_name': '可以使用:quarterly_total_assets_change_percent', 'description': '不可使用,仅供参考:Percent change in total assets compared to the same quarter last year.'} +{'id': 61017, 'data_set_name': '可以使用:anl14_low_ptprep_fy4', 'description': '不可使用,仅供参考:The lowest estimation of reported pretax profit - upcoming 4 years'} +{'id': 61291, 'data_set_name': '可以使用:anl14_median_ntp_fy5', 'description': '不可使用,仅供参考:Median of Estimations of Net Profit - upcoming 5 years'} +{'id': 139223, 'data_set_name': '可以使用:fnd28_bdea_value_03506a', 'description': '不可使用,仅供参考:value of annual field: Fully Diluted Equity'} +{'id': 60977, 'data_set_name': '可以使用:anl14_low_ndebt_fy1', 'description': '不可使用,仅供参考:The lowest estimation of net debt - upcoming year'} +{'id': 62439, 'data_set_name': '可以使用:anl69_best_ebit_median', 'description': '不可使用,仅供参考:EBIT Median'} +{'id': 62052, 'data_set_name': '可以使用:quarterly_free_cash_flow', 'description': '不可使用,仅供参考:Free cash flow for the most recent quarter.'} +{'id': 140802, 'data_set_name': '可以使用:fnd72_s_pit_or_bs_q_bs_common_stock', 'description': '不可使用,仅供参考:Common Stocks'} +{'id': 139064, 'data_set_name': '可以使用:short_term_contract_expenses', 'description': '不可使用,仅供参考:Expenses related to short-term contracts.'} +{'id': 139631, 'data_set_name': '可以使用:fnd28_nddq1_value_04148q', 'description': '不可使用,仅供参考:value of quarterly field: Interest Paid (Cash Flow)'} +{'id': 60885, 'data_set_name': '可以使用:anl14_high_roe_fp3', 'description': '不可使用,仅供参考:The highest estimation of returns on equity - upcoming 3 quarters'} +{'id': 61777, 'data_set_name': '可以使用:anl15_ind_cal_fy0_total', 'description': '不可使用,仅供参考:The total of earnings estimation for the current calendarized fiscal year aggregated within GICS industry grouping.'} +{'id': 139592, 'data_set_name': '可以使用:fnd28_nddq1_value_02001q', 'description': '不可使用,仅供参考:value of quarterly field: Cash & Short Term Investments'} +{'id': 139069, 'data_set_name': '可以使用:short_term_debt_bonds_face_value', 'description': '不可使用,仅供参考:Face value of short-term debt bonds outstanding.'} +{'id': 373521, 'data_set_name': '可以使用:top1500_pca_factor2_grouping2', 'description': '不可使用,仅供参考:Second principal component grouping for top 1500 securities with 2 clusters.'} +{'id': 373643, 'data_set_name': '可以使用:pv72_ibeseps_score_float', 'description': '不可使用,仅供参考:pv_score'} +{'id': 140767, 'data_set_name': '可以使用:fnd72_s_pit_or_bs_q_2_bs_accts_rec_excl_notes_rec', 'description': '不可使用,仅供参考:Accounts receivable with maturities of less than 1 year directly relating to operating activities'} +{'id': 136508, 'data_set_name': '可以使用:fnd13_statementq_agss', 'description': '不可使用,仅供参考:Selling/General/Admin. Expenses, Total'} +{'id': 140512, 'data_set_name': '可以使用:fnd72_pit_or_bs_q_bs_other_st_liab', 'description': '不可使用,仅供参考:Other ST Liabilities'} +{'id': 140124, 'data_set_name': '可以使用:fnd31_devnorthamericaadditionalfactor4_mpgghcy3', 'description': '不可使用,仅供参考:3-Year Change in Gross Profit Margin. It is defined as the 3-Year on Year Change in Gross Profit Margin.'} +{'id': 139847, 'data_set_name': '可以使用:fnd28_stpera_value_09111a', 'description': '不可使用,仅供参考:value of annual field: Price/Earnings Ratio - 5 Yr Avg High'} +{'id': 373369, 'data_set_name': '可以使用:pv173_zscoresshift60dstd60d', 'description': '不可使用,仅供参考:It is defined as the 60-day standard deviation of ShiftIn Bond Z-spread Curve.'} +{'id': 139657, 'data_set_name': '可以使用:fnd28_nddq1_value_04851q', 'description': '不可使用,仅供参考:value of quarterly field: Increase/Decrease in Cash & Short Term Investments'} +{'id': 60806, 'data_set_name': '可以使用:anl14_high_eps_fy2', 'description': '不可使用,仅供参考:The Highest Estimation of Earnings Per Share - upcoming 2 years'} +{'id': 62144, 'data_set_name': '可以使用:quarterly_receivables_2', 'description': '不可使用,仅供参考:Total receivables reported for the most recent quarter.'} +{'id': 62280, 'data_set_name': '可以使用:anl44_2_epsr_value', 'description': '不可使用,仅供参考:epsr value'} +{'id': 60842, 'data_set_name': '可以使用:anl14_high_ntprep_fp1', 'description': '不可使用,仅供参考:The Highest Estimation of Reported Net Profit - upcoming quarter'} +{'id': 139420, 'data_set_name': '可以使用:fnd28_fsa1_value_04601a', 'description': '不可使用,仅供参考:value of annual field: Capital Expenditures (Additions to Fixed Assets)'} +{'id': 139687, 'data_set_name': '可以使用:fnd28_newq_value_03999q', 'description': "不可使用,仅供参考:value of quarterly field: Total Liabilities & Shareholders' Equity"} +{'id': 138938, 'data_set_name': '可以使用:long_term_liabilities_total_4', 'description': '不可使用,仅供参考:Total long-term liabilities reported in interim financials.'} +{'id': 60807, 'data_set_name': '可以使用:anl14_high_eps_fy3', 'description': '不可使用,仅供参考:The highest estimation of Earnings Per Share - upcoming 3 years'} +{'id': 60620, 'data_set_name': '可以使用:anl11_ereg_industryrnk', 'description': '不可使用,仅供参考:Industry Rank on E Score with 1 being the highest rank'} +{'id': 60658, 'data_set_name': '可以使用:anl11_global_industrycount', 'description': '不可使用,仅供参考:Global Industry Count'} +{'id': 61662, 'data_set_name': '可以使用:anl15_gr_12_m_total', 'description': '不可使用,仅供参考:The total of 12-month forward earnings estimation aggregated within GICS group grouping.'} +{'id': 138983, 'data_set_name': '可以使用:other_interest_liabilities_2', 'description': '不可使用,仅供参考:represents changes in inventories during the period. An increase in inventories has a negative impact on cash flow from operating activities, and vice versa.'} +{'id': 61220, 'data_set_name': '可以使用:anl14_median_div_fp1', 'description': '不可使用,仅供参考:Median of Estimations of Dividend - upcoming quarter'} +{'id': 140132, 'data_set_name': '可以使用:fnd31_devnorthamericaadditionalfactor4_rev6fy2', 'description': "不可使用,仅供参考:Averaged Last 6-M EPS Revisions for FY2. It is defined as the average of prior 6-month monthly changes in a stock's consensus analysts' earnings forecasts for fiscal year 2, scaled by previous month-end trading price."} +{'id': 61001, 'data_set_name': '可以使用:anl14_low_ntprep_fy5', 'description': '不可使用,仅供参考:The lowest estimation of reported net profit - upcoming 5 years'} +{'id': 140421, 'data_set_name': '可以使用:fnd72_pit_or_bs_a_bs_add_paid_in_cap', 'description': '不可使用,仅供参考:Additional Paid in Capital'} +{'id': 60662, 'data_set_name': '可以使用:anl11_gposcorreg_industryrnk', 'description': '不可使用,仅供参考:Industry Rank on G Max Correlation Score with 1 being the highest rank'} +{'id': 373568, 'data_set_name': '可以使用:pv37_high_14', 'description': '不可使用,仅供参考:14-Day Highest Price'} +{'id': 60720, 'data_set_name': '可以使用:social_totalcorr_sector_position', 'description': '不可使用,仅供参考:Company’s position within sector peer group for social score weighted by total correlation to financial returns.'} +{'id': 140851, 'data_set_name': '可以使用:fnd72_s_pit_or_cf_q_cf_cash_paid_for_tax', 'description': '不可使用,仅供参考:Cash Paid for Taxes'} +{'id': 62133, 'data_set_name': '可以使用:quarterly_price_to_cash_flow_per_share_2', 'description': '不可使用,仅供参考:Ratio of price to cash flow per share for the quarter.'} +{'id': 139258, 'data_set_name': '可以使用:fnd28_bsasseta_value_02098a', 'description': '不可使用,仅供参考:value of annual field: Work in Process'} +{'id': 139057, 'data_set_name': '可以使用:short_term_bank_debt_amount', 'description': '不可使用,仅供参考:Amount of short-term bank debt outstanding.'} +{'id': 139342, 'data_set_name': '可以使用:fnd28_cfsourceusea_value_04201a', 'description': '不可使用,仅供参考:value of annual field: Funds from Operations'} +{'id': 139066, 'data_set_name': '可以使用:short_term_creditors', 'description': '不可使用,仅供参考:Amount of short-term creditors at the reporting date.'} +{'id': 140569, 'data_set_name': '可以使用:fnd72_pit_or_cf_a_cf_other_inv_act', 'description': '不可使用,仅供参考:Other Investing Activities'} +{'id': 140136, 'data_set_name': '可以使用:fnd31_devnorthamericaadditionalfactor4_y5speq4rqsr', 'description': '不可使用,仅供参考:R Sqr of 5-yr TTM EPS Trend Line. It is defined as the slope coefficient between monthly dates and the corresponding trailing 12-month earnings per share before extra items in the prior 20 quarters.'} +{'id': 62049, 'data_set_name': '可以使用:quarterly_eps_incl_extraordinary', 'description': '不可使用,仅供参考:Earnings per share for the quarter, including extraordinary items.'} +{'id': 60808, 'data_set_name': '可以使用:anl14_high_eps_fy4', 'description': '不可使用,仅供参考:The Highest Estimation of Earnings Per Share - upcoming 4 years'} +{'id': 139681, 'data_set_name': '可以使用:fnd28_newq_value_02652q', 'description': '不可使用,仅供参考:value of quarterly field: Other Assets'} +{'id': 139396, 'data_set_name': '可以使用:fnd28_fsa1_value_02256a', 'description': '不可使用,仅供参考:value of annual field: Investments In Associated Companies'} +{'id': 139368, 'data_set_name': '可以使用:fnd28_cfsourceusea_value_04830a', 'description': '不可使用,仅供参考:value of annual field: Decrease/Increase In Other Assets/Liabilities'} +{'id': 138980, 'data_set_name': '可以使用:other_current_liabilities_3', 'description': '不可使用,仅供参考:Other liabilities classified as current.'} +{'id': 138995, 'data_set_name': '可以使用:other_off_balance_liabilities', 'description': '不可使用,仅供参考:Other off-balance sheet liabilities for the annual period.'} +{'id': 60911, 'data_set_name': '可以使用:anl14_low_capex_fy5', 'description': '不可使用,仅供参考:The Lowest Estimation of Capital Expenditures - upcoming 5 years'} +{'id': 61233, 'data_set_name': '可以使用:anl14_median_ebit_fp4', 'description': '不可使用,仅供参考:Median of estimations of earnings before interest & taxes - upcoming 4 quarters'} +{'id': 62119, 'data_set_name': '可以使用:quarterly_operating_margin_percent', 'description': '不可使用,仅供参考:Operating income divided by revenue for the quarter, expressed as a percentage.'} +{'id': 137365, 'data_set_name': '可以使用:quarterly_revenue_per_share_nonannualized_2', 'description': '不可使用,仅供参考:Revenue per share for the most recent quarter (not annualized).'} +{'id': 60907, 'data_set_name': '可以使用:anl14_low_capex_fy1', 'description': '不可使用,仅供参考:The lowest estimation of capital expenditures - upcoming year'} +{'id': 373640, 'data_set_name': '可以使用:pv72_ibeseps2_score_float', 'description': '不可使用,仅供参考:pv_score'} +{'id': 137314, 'data_set_name': '可以使用:net_debt_annual', 'description': '不可使用,仅供参考:Net debt for the most recent fiscal year, calculated as total debt minus cash and equivalents.'} +{'id': 60600, 'data_set_name': '可以使用:anl11_emp_totalcor', 'description': "不可使用,仅供参考:The EMP Hybrid Score weights the three EMP scores based on which are strongly rank correlated (to the positive) to financial metrics related to 1yr and 3 yr total returns for that company's peer group."} +{'id': 139803, 'data_set_name': '可以使用:fnd28_ratesq_value_08616q', 'description': '不可使用,仅供参考:value of quarterly field: Equity Growth (year ago)'} +{'id': 139879, 'data_set_name': '可以使用:fnd28_value_04225q', 'description': '不可使用,仅供参考:value of quarterly field: Extraordinary Items'} +{'id': 137532, 'data_set_name': '可以使用:extraordinary_items_other', 'description': '不可使用,仅供参考:Other extraordinary items reported in the financials.'} +{'id': 137427, 'data_set_name': '可以使用:accounts_payable_current_2', 'description': '不可使用,仅供参考:[Quarterly] Construction in Progress - Gross'} +{'id': 139489, 'data_set_name': '可以使用:fnd28_growthratesa_value_08640a', 'description': '不可使用,仅供参考:value of annual field: Net Income Growth'} +{'id': 60742, 'data_set_name': '可以使用:anl14_high_bvps_fp1', 'description': '不可使用,仅供参考:The Highest Estimation of Book Value Per Share - Upcoming Quarter'} +{'id': 60888, 'data_set_name': '可以使用:anl14_high_roe_fy2', 'description': '不可使用,仅供参考:The highest estimation of returns on equity - upcoming 2 years'} +{'id': 60952, 'data_set_name': '可以使用:anl14_low_eps_fp3', 'description': '不可使用,仅供参考:The Lowest Estimation of Earnings Per Share - upcoming 3 quarters'} +{'id': 139320, 'data_set_name': '可以使用:fnd28_cfq_value_04751q', 'description': '不可使用,仅供参考:value of quarterly field: Common/Preferred Redeemed, Retired, Converted, Etc.'} +{'id': 373382, 'data_set_name': '可以使用:industry_grouping_level20_top3000_2', 'description': '不可使用,仅供参考:Industry classification group at level 20 for the top 3000 instruments.'} +{'id': 139935, 'data_set_name': '可以使用:fnd28_value_08601', 'description': '不可使用,仅供参考:value of field: Earnings per Share Growth'} +{'id': 60798, 'data_set_name': '可以使用:anl14_high_ebitda_fy4', 'description': '不可使用,仅供参考:The highest estimation of Earnings Before Interest, Taxes, Depreciation & Amortization - upcoming 4 years'} +{'id': 138942, 'data_set_name': '可以使用:long_term_loans_total_debt', 'description': '不可使用,仅供参考:Total long-term loans and debt reported for the interim period.'} +{'id': 139650, 'data_set_name': '可以使用:fnd28_nddq1_value_04826q', 'description': '不可使用,仅供参考:value of quarterly field: Decrease/Increase In Inventories'} +{'id': 140122, 'data_set_name': '可以使用:fnd31_devnorthamericaadditionalfactor4_fwdebitdaev', 'description': '不可使用,仅供参考:Leading 12 Month EBITDA/EV. It is defined as the FY1 estimate of EBITDA/Enterprise Value.'} +{'id': 140118, 'data_set_name': '可以使用:fnd31_devnorthamericaadditionalfactor4_apgghcy1', 'description': '不可使用,仅供参考:1-Year Change in Gross Profit to Assets. It is defined as the Year on Year Change in Gross Profit to Assets.'} +{'id': 140748, 'data_set_name': '可以使用:fnd72_s_pit_or_bs_q_1_bs_other_lt_liabilities', 'description': '不可使用,仅供参考:Other LT Liabilities'} +{'id': 139307, 'data_set_name': '可以使用:fnd28_cfq_value_04199q', 'description': '不可使用,仅供参考:value of quarterly field: Deferred Income Taxes (Cash Flow)'} +{'id': 140057, 'data_set_name': '可以使用:fnd28_value_19522', 'description': '不可使用,仅供参考:value of field: Product Segment 3 - Operating Income'} +{'id': 62160, 'data_set_name': '可以使用:quarterly_return_on_investment', 'description': '不可使用,仅供参考:Annualized net income divided by average investment for the quarter.'} +{'id': 62223, 'data_set_name': '可以使用:ttm_price_to_free_cash_flow_per_share', 'description': '不可使用,仅供参考:Price divided by free cash flow per share for trailing twelve months.'} +{'id': 139048, 'data_set_name': '可以使用:share_premium_reserve', 'description': '不可使用,仅供参考:Preferred Stock – Non-Redeemable, Total.'} +{'id': 60757, 'data_set_name': '可以使用:anl14_high_capex_fy1', 'description': '不可使用,仅供参考:The highest estimation of Capital Expenditures - upcoming year'} +{'id': 139543, 'data_set_name': '可以使用:fnd28_ishtspmnt_value_01306a', 'description': '不可使用,仅供参考:value of annual field: Gain/Loss On Disposal of Assets'} +{'id': 373436, 'data_set_name': '可以使用:resampled_pca_factor_6', 'description': '不可使用,仅供参考:Sixth principal component factor derived from robust resampling of returns for top 2000 securities.'} +{'id': 138883, 'data_set_name': '可以使用:fully_paid_share_capital', 'description': '不可使用,仅供参考:Total value of fully paid share capital issued by the company.'} +{'id': 140460, 'data_set_name': '可以使用:fnd72_pit_or_bs_a_bs_sh_cap_and_apic', 'description': '不可使用,仅供参考:Share Capital & APIC'} +{'id': 139571, 'data_set_name': '可以使用:fnd28_levliqa_value_08261a', 'description': '不可使用,仅供参考:value of annual field: Operating Cash/Fixed Charges'} +{'id': 61192, 'data_set_name': '可以使用:anl14_median_bvps_fp1', 'description': '不可使用,仅供参考:Median of Estimations of Book Value Per Share - upcoming quarter'} +{'id': 60737, 'data_set_name': '可以使用:anl14_actvalue_revenue_fp0', 'description': '不可使用,仅供参考:Revenue - recent last quarter'} +{'id': 60805, 'data_set_name': '可以使用:anl14_high_eps_fy1', 'description': '不可使用,仅供参考:The Highest Estimation of Earnings Per Share - Upcoming Year'} +{'id': 61281, 'data_set_name': '可以使用:anl14_median_ndebt_fy5', 'description': '不可使用,仅供参考:Median of Estimations of Net Debt - upcoming 5 years'} +{'id': 60896, 'data_set_name': '可以使用:anl14_low_bvps_fp5', 'description': '不可使用,仅供参考:The lowest estimation of Book Value Per Share - upcoming 5 quarters'} +{'id': 139909, 'data_set_name': '可以使用:fnd28_value_07260q', 'description': '不可使用,仅供参考:value of quarterly field: Trailing Twelve Months Net Profit (U.S. $)'} +{'id': 373374, 'data_set_name': '可以使用:pv173_zscorestilt6m1yavg60dlast', 'description': '不可使用,仅供参考:It is defined as the average factor loading of the second principal component for the 6-month and 1-year z-spread'} +{'id': 139705, 'data_set_name': '可以使用:fnd28_newq_value_08306q', 'description': '不可使用,仅供参考:value of quarterly field: Gross Profit Margin'} +{'id': 62074, 'data_set_name': '可以使用:quarterly_inventory_turnover', 'description': '不可使用,仅供参考:Annualized ratio of cost of goods sold to average inventory for the quarter.'} +{'id': 139898, 'data_set_name': '可以使用:fnd28_value_05302', 'description': '不可使用,仅供参考:value of field: Common Shares Outstanding - Current'} +{'id': 139101, 'data_set_name': '可以使用:short_term_other_operating_expenses', 'description': '不可使用,仅供参考:Short-term other operating expenses reported in interim financials.'} +{'id': 139244, 'data_set_name': '可以使用:fnd28_bdeq_value_03498q', 'description': '不可使用,仅供参考:value of quarterly field: Unrealized Gain/Loss On Marketable Securities'} +{'id': 139290, 'data_set_name': '可以使用:fnd28_bsassetq_value_02652q', 'description': '不可使用,仅供参考:value of quarterly field: Other Assets'} +{'id': 60776, 'data_set_name': '可以使用:anl14_high_div_fy2', 'description': '不可使用,仅供参考:The Highest Estimation of Dividend - Upcoming 2 Years'} +{'id': 139389, 'data_set_name': '可以使用:fnd28_fsa1_value_01706a', 'description': '不可使用,仅供参考:value of annual field: Net Income after Preferred Dividends (Basic EPS)'} +{'id': 139940, 'data_set_name': '可以使用:fnd28_value_08621', 'description': '不可使用,仅供参考:value of field: Total Assets Growth (year ago)'} +{'id': 140171, 'data_set_name': '可以使用:fnd31_qsg5additionalfactor3_monthly_rev6fy2', 'description': "不可使用,仅供参考:Averaged Last 6-M EPS Revisions for FY2. It is defined as the average of prior 6-month monthly changes in a stock's consensus analysts earnings forecasts for fiscal year 2, scaled by previous month-end trading price."} +{'id': 61282, 'data_set_name': '可以使用:anl14_median_ntp_fp1', 'description': '不可使用,仅供参考:Median of estimations of net profit - upcoming quarter'} +{'id': 136607, 'data_set_name': '可以使用:fnd17_8alldelay1_reptoprcexrate', 'description': '不可使用,仅供参考:Reporting Currency to base currency exchange rate'} +{'id': 140796, 'data_set_name': '可以使用:fnd72_s_pit_or_bs_q_bs_add_paid_in_cap', 'description': '不可使用,仅供参考:Additional Paid in Capital'} +{'id': 140469, 'data_set_name': '可以使用:fnd72_pit_or_bs_a_bs_total_capital_leases', 'description': '不可使用,仅供参考:Total of short- and long-term amounts payable by a lessee under capital leases'} +{'id': 60803, 'data_set_name': '可以使用:anl14_high_eps_fp4', 'description': '不可使用,仅供参考:The Highest Estimation of Earnings Per Share - upcoming 4 quarters'} +{'id': 139177, 'data_set_name': '可以使用:fnd25_statementtypecode', 'description': '不可使用,仅供参考:Statement type code: 1 for Profit & Loss, 3 for Balance Sheet statements'} +{'id': 140859, 'data_set_name': '可以使用:fnd72_s_pit_or_cf_q_cf_incr_cap_stock', 'description': '不可使用,仅供参考:Increase in Capital Stocks'} +{'id': 139198, 'data_set_name': '可以使用:fnd28_bdea_value_03061a', 'description': '不可使用,仅供参考:value of annual field: Dividends Payable'} +{'id': 61678, 'data_set_name': '可以使用:anl15_gr_18_m_total', 'description': '不可使用,仅供参考:The total of 18-month forward earnings estimation aggregated within GICS group grouping.'} +{'id': 62201, 'data_set_name': '可以使用:quarterly_total_debt_to_equity_ratio', 'description': '不可使用,仅供参考:Total debt divided by equity for the most recent quarter.'} +{'id': 61715, 'data_set_name': '可以使用:anl15_gr_cal_fy3_total', 'description': '不可使用,仅供参考:The total of earnings estimation for the calendarized 3 fiscal year aggregated within GICS group grouping.'} +{'id': 62426, 'data_set_name': '可以使用:anl46_alphadecay', 'description': '不可使用,仅供参考:Contribution of idea age'} +{'id': 62188, 'data_set_name': '可以使用:quarterly_total_assets_change_yoy', 'description': '不可使用,仅供参考:Year-over-year percent change in total assets for the quarter.'} +{'id': 60900, 'data_set_name': '可以使用:anl14_low_bvps_fy4', 'description': '不可使用,仅供参考:The Lowest Estimation of Book Value Per Share - Upcoming 4 Years'} +{'id': 138962, 'data_set_name': '可以使用:net_income_value_3', 'description': '不可使用,仅供参考:Total profit after all expenses, taxes, and costs for the period.'} +{'id': 373657, 'data_set_name': '可以使用:pv72_pv_score', 'description': '不可使用,仅供参考:pv_score'} +{'id': 140853, 'data_set_name': '可以使用:fnd72_s_pit_or_cf_q_cf_chng_non_cash_work_cap', 'description': '不可使用,仅供参考:Changes in Non-Cash Working Capital'} +{'id': 61279, 'data_set_name': '可以使用:anl14_median_ndebt_fy3', 'description': '不可使用,仅供参考:Median of estimations of net debt - upcoming 3 years'} +{'id': 139093, 'data_set_name': '可以使用:short_term_non_current_investments', 'description': '不可使用,仅供参考:Short-term investments that are classified as non-current.'} +{'id': 140626, 'data_set_name': '可以使用:fnd72_pit_or_is_a_is_disc_rate_pension_plan_liabs', 'description': '不可使用,仅供参考:The estimated interest rate that is used to compute the present value of benefit obligations'} +{'id': 373439, 'data_set_name': '可以使用:resampled_pca_factor_9', 'description': '不可使用,仅供参考:Ninth principal component factor derived from robust resampling of returns for top 2000 securities.'} +{'id': 139447, 'data_set_name': '可以使用:fnd28_fsq1_value_02301q', 'description': '不可使用,仅供参考:value of quarterly field: Property, Plant and Equipment - Gross'} +{'id': 62347, 'data_set_name': '可以使用:cash_eps_currency_code_2', 'description': '不可使用,仅供参考:Currency code in which the cash earnings per share value is reported.'} +{'id': 139499, 'data_set_name': '可以使用:fnd28_ishta_value_01101a', 'description': '不可使用,仅供参考:value of annual field: Selling, General & Administrative Expenses'} +{'id': 140485, 'data_set_name': '可以使用:fnd72_pit_or_bs_q_bs_acct_payable', 'description': '不可使用,仅供参考:Accounts Payable'} +{'id': 140921, 'data_set_name': '可以使用:fnd86_average_score', 'description': '不可使用,仅供参考:Average score'} +{'id': 136528, 'data_set_name': '可以使用:annual_common_equity', 'description': '不可使用,仅供参考:Book value of common equity for the most recent fiscal year.'} +{'id': 62183, 'data_set_name': '可以使用:quarterly_taxes_paid', 'description': '不可使用,仅供参考:Income taxes paid for the most recent quarter.'} +{'id': 136518, 'data_set_name': '可以使用:fnd13_statementq_tait', 'description': '不可使用,仅供参考:Net Income After Taxes'} +{'id': 139003, 'data_set_name': '可以使用:preferred_stock_dividends', 'description': '不可使用,仅供参考:[Quarterly] Total Plan Service Cost'} +{'id': 60612, 'data_set_name': '可以使用:anl11_empreg_industryrnk', 'description': '不可使用,仅供参考:Industry Rank on EMP Score with 1 being the highest rank'} +{'id': 136544, 'data_set_name': '可以使用:annual_total_liabilities_value', 'description': '不可使用,仅供参考:Total liabilities for the most recent fiscal year.'} +{'id': 60775, 'data_set_name': '可以使用:anl14_high_div_fy1', 'description': '不可使用,仅供参考:The Highest Estimation of Dividend - Upcoming Year'} +{'id': 373530, 'data_set_name': '可以使用:top2000_factor1_group50_score', 'description': '不可使用,仅供参考:First factor score for top 2000 securities, grouped into 50 clusters.'} +{'id': 139760, 'data_set_name': '可以使用:fnd28_pftlta_value_08370a', 'description': '不可使用,仅供参考:value of annual field: Net Margin - 5 Yr Avg Asset Utilization Annual Statistics'} +{'id': 60779, 'data_set_name': '可以使用:anl14_high_div_fy5', 'description': '不可使用,仅供参考:The Highest Estimation of Dividend - upcoming 5 years'} +{'id': 140028, 'data_set_name': '可以使用:fnd28_value_18379a', 'description': '不可使用,仅供参考:value of annual field: Property, Plant & Equipment - Other'} +{'id': 60892, 'data_set_name': '可以使用:anl14_low_bvps_fp1', 'description': '不可使用,仅供参考:The lowest estimation of book value per share - upcoming quarter'} +{'id': 140489, 'data_set_name': '可以使用:fnd72_pit_or_bs_q_bs_allow_doubtful_acc_rec', 'description': '不可使用,仅供参考:A contra asset account that is subtracted from trade and notes receivables on the balance sheet'} +{'id': 60993, 'data_set_name': '可以使用:anl14_low_ntprep_fp2', 'description': '不可使用,仅供参考:The Lowest Estimation of Reported Net Profit - upcoming 2 quarters'} +{'id': 140422, 'data_set_name': '可以使用:fnd72_pit_or_bs_a_bs_allow_doubtful_acc_rec', 'description': '不可使用,仅供参考:A contra asset account that is subtracted from trade and notes receivables on the balance sheet'} +{'id': 139722, 'data_set_name': '可以使用:fnd28_newq_value_08621q', 'description': '不可使用,仅供参考:value of quarterly field: Total Assets Growth (year ago)'} +{'id': 60823, 'data_set_name': '可以使用:anl14_high_ndebt_fp1', 'description': '不可使用,仅供参考:The Highest Estimation of Net Debt - upcoming quarter'} +{'id': 140635, 'data_set_name': '可以使用:fnd72_pit_or_is_a_is_foreign_crncy_trans_adj', 'description': '不可使用,仅供参考:Foreign Currency Translation Adjustment'} +{'id': 61870, 'data_set_name': '可以使用:anl15_s_18_m_total', 'description': '不可使用,仅供参考:The total of 18-month forward earnings estimation aggregated within GICS sector grouping.'} +{'id': 62210, 'data_set_name': '可以使用:quarterly_total_shareholder_equity_2', 'description': '不可使用,仅供参考:Sum of all equity line items for the most recent quarter.'} +{'id': 139196, 'data_set_name': '可以使用:fnd28_bdea_value_03051a', 'description': '不可使用,仅供参考:value of annual field: Short Term Debt & Current Portion of Long Term Debt'} +{'id': 139269, 'data_set_name': '可以使用:fnd28_bsasseta_value_02401a', 'description': '不可使用,仅供参考:value of annual field: Accumulated Depreciation'} +{'id': 136592, 'data_set_name': '可以使用:fnd17_4alldelay1_usdtorepexrate', 'description': '不可使用,仅供参考:Reporting Currency to USD exchange rate'} +{'id': 140744, 'data_set_name': '可以使用:fnd72_s_pit_or_bs_q_1_bs_mkt_sec_other_st_invest', 'description': '不可使用,仅供参考:Marketable Securities & Other ST Investments'} +{'id': 139667, 'data_set_name': '可以使用:fnd28_nddq2_value_18208q', 'description': '不可使用,仅供参考:value of quarterly field: Earnings per Share Continuing Operations'} +{'id': 140926, 'data_set_name': '可以使用:fnd86_fundamental_score', 'description': '不可使用,仅供参考:Fundamental score'} +{'id': 61009, 'data_set_name': '可以使用:anl14_low_ptp_fy1', 'description': '不可使用,仅供参考:The lowest estimation of pretax profit - upcoming year'} +{'id': 139688, 'data_set_name': '可以使用:fnd28_newq_value_04051q', 'description': '不可使用,仅供参考:value of quarterly field: Depreciation, Depletion & Amortization'} +{'id': 139690, 'data_set_name': '可以使用:fnd28_newq_value_04251q', 'description': '不可使用,仅供参考:value of quarterly field: Net Proceeds from Sale/Issuance of Common & Preferred'} +{'id': 60921, 'data_set_name': '可以使用:anl14_low_div_fp2', 'description': '不可使用,仅供参考:The Lowest Estimation of Dividend - Upcoming 2 Quarters'} +{'id': 136566, 'data_set_name': '可以使用:ebitda_quarterly', 'description': '不可使用,仅供参考:Earnings before interest, taxes, depreciation, and amortization for the most recent quarter.'} +{'id': 137356, 'data_set_name': '可以使用:quarterly_ebit_value', 'description': '不可使用,仅供参考:Earnings before interest and taxes for the most recent quarter.'} +{'id': 61004, 'data_set_name': '可以使用:anl14_low_opp_fy2', 'description': '不可使用,仅供参考:The Lowest Estimation of Operating Profit - upcoming 2 years'} +{'id': 139450, 'data_set_name': '可以使用:fnd28_fsq1_value_03040q', 'description': '不可使用,仅供参考:value of quarterly field: Accounts Payable'} +{'id': 60592, 'data_set_name': '可以使用:anl11_emp2reg_industryperc', 'description': '不可使用,仅供参考:Industry Percentile on EMP2 100% being the best'} +{'id': 140018, 'data_set_name': '可以使用:fnd28_value_18310q', 'description': '不可使用,仅供参考:value of quarterly field: Trailing Twelve Months Net Cash Flow Operating Activities'} +{'id': 140052, 'data_set_name': '可以使用:fnd28_value_19512', 'description': '不可使用,仅供参考:value of field: Product Segment 2 - Operating Income'} +{'id': 139065, 'data_set_name': '可以使用:short_term_contracts_executed', 'description': '不可使用,仅供参考:capital expenditures.'} +{'id': 62128, 'data_set_name': '可以使用:quarterly_pe_ratio_minimum', 'description': '不可使用,仅供参考:Lowest price-to-earnings ratio observed in the latest quarter.'} +{'id': 140098, 'data_set_name': '可以使用:fnd28_wsannualstats_value_08335', 'description': '不可使用,仅供参考:value of field: Cost Of Goods Sold/Sales - 5 Yr Avg'} +{'id': 140039, 'data_set_name': '可以使用:fnd28_value_18469q', 'description': '不可使用,仅供参考:value of quarterly field: Earnings per Share Fiscal Period Basic Revised'} +{'id': 137410, 'data_set_name': '可以使用:ttm_net_income_value', 'description': '不可使用,仅供参考:Net income for the trailing twelve months.'} +{'id': 62248, 'data_set_name': '可以使用:anl44_2_csh_lastactvalue', 'description': '不可使用,仅供参考:csh lastactvalue'} +{'id': 60836, 'data_set_name': '可以使用:anl14_high_ntp_fp5', 'description': '不可使用,仅供参考:The highest estimation of Net Profit - upcoming 5 quarters'} +{'id': 139948, 'data_set_name': '可以使用:fnd28_value_08735', 'description': '不可使用,仅供参考:value of field: Foreign Sales % Total Sales'} +{'id': 137282, 'data_set_name': '可以使用:fnd17_ttmpelow', 'description': '不可使用,仅供参考:P/E excluding extraordinary items low, trailing 12 months'} +{'id': 139595, 'data_set_name': '可以使用:fnd28_nddq1_value_02101q', 'description': '不可使用,仅供参考:value of quarterly field: Inventories Total'} +{'id': 139277, 'data_set_name': '可以使用:fnd28_bsassetq_value_02003q', 'description': '不可使用,仅供参考:value of quarterly field: Cash'} +{'id': 139724, 'data_set_name': '可以使用:fnd28_newq_value_08636q', 'description': '不可使用,仅供参考:value of quarterly field: Net Income Growth'} +{'id': 137393, 'data_set_name': '可以使用:revenue_per_share_quarterly', 'description': '不可使用,仅供参考:Revenue per share for the most recent quarter, annualized.'} +{'id': 139442, 'data_set_name': '可以使用:fnd28_fsq1_value_02051q', 'description': '不可使用,仅供参考:value of quarterly field: Receivables (Net)'} +{'id': 137529, 'data_set_name': '可以使用:extraordinary_adjustments', 'description': '不可使用,仅供参考:Adjustments related to extraordinary items.'} +{'id': 62024, 'data_set_name': '可以使用:quarterly_current_ratio_alt', 'description': '不可使用,仅供参考:Alternate calculation of current ratio for the most recent quarter.'} +{'id': 373381, 'data_set_name': '可以使用:industry_grouping_level20_top2000', 'description': '不可使用,仅供参考:Industry classification group at level 20 for the top 2000 instruments.'} +{'id': 139601, 'data_set_name': '可以使用:fnd28_nddq1_value_02401q', 'description': '不可使用,仅供参考:value of quarterly field: Accumulated Depreciation'} +{'id': 60938, 'data_set_name': '可以使用:anl14_low_ebit_fy4', 'description': '不可使用,仅供参考:The lowest estimation of earnings before interest & taxes - upcoming 4 years'} +{'id': 140547, 'data_set_name': '可以使用:fnd72_pit_or_cf_a_cf_cash_from_fnc_act', 'description': '不可使用,仅供参考:Cash from Financing Activities'} +{'id': 138970, 'data_set_name': '可以使用:operating_revenue_other', 'description': '不可使用,仅供参考:[Quarterly] Other Revenue, Total'} +{'id': 138973, 'data_set_name': '可以使用:other_appropriations_balance', 'description': '不可使用,仅供参考:Other appropriations balance reported for the annual period.'} +{'id': 137527, 'data_set_name': '可以使用:equity_to_equityholders', 'description': '不可使用,仅供参考:Equity attributable to equity holders of the parent.'} +{'id': 61242, 'data_set_name': '可以使用:anl14_median_ebitda_fp3', 'description': '不可使用,仅供参考:Median of Estimations of Earnings Before Interest, Taxes, Depreciation & Amortization - upcoming 3 quarters'} +{'id': 139465, 'data_set_name': '可以使用:fnd28_fsq1_value_04751q', 'description': '不可使用,仅供参考:value of quarterly field: Common/Preferred Redeemed, Retired, Converted, Etc.'} +{'id': 62313, 'data_set_name': '可以使用:anl44_2_operatingprofit_lastactvalue', 'description': '不可使用,仅供参考:operatingprofit lastactvalue'} +{'id': 62696, 'data_set_name': '可以使用:anl69_roe_best_roe_median', 'description': '不可使用,仅供参考:Return on Equity Median'} +{'id': 60963, 'data_set_name': '可以使用:anl14_low_epsrep_fp4', 'description': '不可使用,仅供参考:The Lowest Estimation of Reported Earnings Per Share - upcoming 4 quarters'} +{'id': 136599, 'data_set_name': '可以使用:fnd17_6alldelay1_reptoprcexrate', 'description': '不可使用,仅供参考:Reporting Currency to base currency exchange rate'} +{'id': 136428, 'data_set_name': '可以使用:intrinsic_value_options_awarded_2', 'description': '不可使用,仅供参考:Intrinsic value of options awarded to the director during the period (alternate).'} +{'id': 139520, 'data_set_name': '可以使用:fnd28_ishtq_value_01101q', 'description': '不可使用,仅供参考:value of quarterly field: Selling, General & Administrative Expenses'} +{'id': 60651, 'data_set_name': '可以使用:anl11_g_totalcor', 'description': "不可使用,仅供参考:The G Hybrid Score weights the three G scores based on which are strongly rank correlated (to the positive) to financial metrics related to 1yr and 3 yr total returns for that company's peer group."} +{'id': 61290, 'data_set_name': '可以使用:anl14_median_ntp_fy4', 'description': '不可使用,仅供参考:Median of estimations of net profit - upcoming 4 years'} +{'id': 139881, 'data_set_name': '可以使用:fnd28_value_04651q', 'description': '不可使用,仅供参考:value of quarterly field: Additions to Other Assets'} +{'id': 137484, 'data_set_name': '可以使用:cash_proceeds_received', 'description': '不可使用,仅供参考:Cash proceeds received during the period.'} +{'id': 61873, 'data_set_name': '可以使用:anl15_s_cal_fy0_total', 'description': '不可使用,仅供参考:The total of earnings estimation for the current calendarized fiscal year aggregated within GICS sector grouping.'} +{'id': 60785, 'data_set_name': '可以使用:anl14_high_ebit_fy1', 'description': '不可使用,仅供参考:The Highest Estimation of Earnings Before Interest & Taxes - Upcoming Year'} +{'id': 60586, 'data_set_name': '可以使用:anl11_e_ttcrg_industryperc', 'description': '不可使用,仅供参考:Industry Percentile on E Hybrid Score with 100% being the best'} +{'id': 139007, 'data_set_name': '可以使用:quarterly_cash_market_securities', 'description': '不可使用,仅供参考:Cash and marketable securities at quarter end.'} +{'id': 138978, 'data_set_name': '可以使用:other_borrowings_total', 'description': '不可使用,仅供参考:Total other borrowings reported for the annual period.'} +{'id': 140816, 'data_set_name': '可以使用:fnd72_s_pit_or_bs_q_bs_other_asset_def_chng_other', 'description': '不可使用,仅供参考:Other Assets/Def Change'} +{'id': 138971, 'data_set_name': '可以使用:other_accumulated_reserves', 'description': '不可使用,仅供参考:Other accumulated reserves reported for the annual period.'} +{'id': 139812, 'data_set_name': '可以使用:fnd28_statisticsa_value_05201a', 'description': '不可使用,仅供参考:Value of annual field: Earnings per Share (Supported for Non-U.S. only)'} +{'id': 136519, 'data_set_name': '可以使用:fnd13_statementq_tbie', 'description': '不可使用,仅供参考:Net Income Before Taxes'} +{'id': 139221, 'data_set_name': '可以使用:fnd28_bdea_value_03499a', 'description': '不可使用,仅供参考:value of annual field: Treasury Stock'} +{'id': 139091, 'data_set_name': '可以使用:short_term_net_cash_inflow', 'description': '不可使用,仅供参考:non-cash items.'} +{'id': 373589, 'data_set_name': '可以使用:pv37_low_global3h', 'description': '不可使用,仅供参考:Global Lowest Price 3H'} +{'id': 140163, 'data_set_name': '可以使用:fnd31_qsg5additionalfactor3_monthly_mpgghcy3', 'description': '不可使用,仅供参考:3 Year Change in Gross Profit Margin. It is defined as the 3-year-on-year change in gross profit margin.'} +{'id': 139024, 'data_set_name': '可以使用:research_and_development_expense', 'description': '不可使用,仅供参考:[Quarterly] Discount Rate - Domestic'} +{'id': 60607, 'data_set_name': '可以使用:anl11_empposcorreg_industryperc', 'description': '不可使用,仅供参考:Industry Percentile on EMP Max Correlation Score with 100% being the best'} +{'id': 139573, 'data_set_name': '可以使用:fnd28_levliqa_value_08271a', 'description': '不可使用,仅供参考:value of annual field: Working Capital % Total Capital Five Year Averages'} +{'id': 139272, 'data_set_name': '可以使用:fnd28_bsasseta_value_02648a', 'description': '不可使用,仅供参考:value of annual field: Tangible Other Assets'} +{'id': 62067, 'data_set_name': '可以使用:quarterly_interest_coverage_db', 'description': '不可使用,仅供参考:EBIT divided by interest expense for the quarter (alternate source).'} +{'id': 140165, 'data_set_name': '可以使用:fnd31_qsg5additionalfactor3_monthly_pbdwf', 'description': '不可使用,仅供参考:Leading 12-Month Book Yield. It is defined as the average quarterly forward-looking book value per share divided by close price.'} +{'id': 61313, 'data_set_name': '可以使用:anl14_median_ptp_fy5', 'description': '不可使用,仅供参考:Median of estimations of pretax profit - upcoming 5 years'} +{'id': 138923, 'data_set_name': '可以使用:long_term_borrowings_amount', 'description': '不可使用,仅供参考:Amount of long-term borrowings outstanding.'} +{'id': 136388, 'data_set_name': '可以使用:estimated_value_exercisable_options_ltip', 'description': '不可使用,仅供参考:Estimated value of exercisable options awarded in the period for LTIP compensation.'} +{'id': 136429, 'data_set_name': '可以使用:intrinsic_value_options_held_2', 'description': '不可使用,仅供参考:Intrinsic value of options held by the director at period end.'} +{'id': 139151, 'data_set_name': '可以使用:valuation_investment_assets_total', 'description': '不可使用,仅供参考:Total valuation allowance for investment assets.'} +{'id': 61208, 'data_set_name': '可以使用:anl14_median_capex_fy2', 'description': '不可使用,仅供参考:Median of Estimations of Capital Expenditures - upcoming 2 years'} +{'id': 139718, 'data_set_name': '可以使用:fnd28_newq_value_08601q', 'description': '不可使用,仅供参考:value of quarterly field: Earnings per Share Growth'} +{'id': 139019, 'data_set_name': '可以使用:quasi_equity_liabilities', 'description': '不可使用,仅供参考:Quasi-equity liabilities reported for the annual period.'} +{'id': 137535, 'data_set_name': '可以使用:extraordinary_losses', 'description': '不可使用,仅供参考:[Quarterly] Expected Return on Assets - Domestic'} +{'id': 62015, 'data_set_name': '可以使用:quarterly_current_assets', 'description': '不可使用,仅供参考:Sum of all current assets for the most recent quarter.'} +{'id': 60817, 'data_set_name': '可以使用:anl14_high_epsrep_fy3', 'description': '不可使用,仅供参考:The Highest Estimation of Reported Earnings Per Share - upcoming 3 years'} +{'id': 139801, 'data_set_name': '可以使用:fnd28_ratesq_value_08606q', 'description': '不可使用,仅供参考:value of quarterly field: Book Value per Share Growth (year ago)'} +{'id': 60661, 'data_set_name': '可以使用:anl11_gposcorreg_industryperc', 'description': '不可使用,仅供参考:Industry Percentile on G Max Correlation Score with 100% being the best'} +{'id': 60812, 'data_set_name': '可以使用:anl14_high_epsrep_fp3', 'description': '不可使用,仅供参考:The Highest Estimation of Reported Earnings Per Share - upcoming 3 quarters'} +{'id': 139505, 'data_set_name': '可以使用:fnd28_ishta_value_01250a', 'description': '不可使用,仅供参考:value of annual field: Operating Income'} +{'id': 139553, 'data_set_name': '可以使用:fnd28_levliqa_value_08101a', 'description': '不可使用,仅供参考:value of annual field: Quick Ratio'} +{'id': 140002, 'data_set_name': '可以使用:fnd28_value_18267q', 'description': '不可使用,仅供参考:value of quarterly field: Trailing Twelve Months Gross Margin'} +{'id': 62092, 'data_set_name': '可以使用:quarterly_long_term_debt_to_equity_ratio', 'description': '不可使用,仅供参考:Long-term debt divided by equity for the most recent quarter.'} +{'id': 140003, 'data_set_name': '可以使用:fnd28_value_18268q', 'description': '不可使用,仅供参考:value of quarterly field: Trailing Twelve Months Net Margin Asset Utilization'} +{'id': 62032, 'data_set_name': '可以使用:quarterly_dividend_payout_ratio', 'description': '不可使用,仅供参考:Percentage of earnings paid out as dividends for the quarter.'} +{'id': 62329, 'data_set_name': '可以使用:anl44_2_roa_prevalue', 'description': '不可使用,仅供参考:roa prevalue'} +{'id': 62290, 'data_set_name': '可以使用:anl44_2_grossmargin_value', 'description': '不可使用,仅供参考:grossmargin value'} +{'id': 140004, 'data_set_name': '可以使用:fnd28_value_18269q', 'description': '不可使用,仅供参考:Value of quarterly field: Trailing 12 Months Interest Expense'} +{'id': 140080, 'data_set_name': '可以使用:fnd28_wcbshtassetq_value_02999q', 'description': '不可使用,仅供参考:value of quarterly field: Total Assets'} +{'id': 138950, 'data_set_name': '可以使用:long_term_tax_payable_4', 'description': '不可使用,仅供参考:Long-term tax payable reported in interim financials.'} +{'id': 62045, 'data_set_name': '可以使用:quarterly_effective_tax_rate', 'description': '不可使用,仅供参考:Effective tax rate for the most recent quarter.'} +{'id': 140813, 'data_set_name': '可以使用:fnd72_s_pit_or_bs_q_bs_mkt_sec_other_st_invest', 'description': '不可使用,仅供参考:Marketable Securities & Other ST Investments'} +{'id': 139953, 'data_set_name': '可以使用:fnd28_value_09104q', 'description': '不可使用,仅供参考:value of quarterly field: Price/Earnings Ratio Close'} +{'id': 139787, 'data_set_name': '可以使用:fnd28_ratesq_value_08306q', 'description': '不可使用,仅供参考:value of quarterly field: Gross Profit Margin'} +{'id': 139613, 'data_set_name': '可以使用:fnd28_nddq1_value_03263q', 'description': '不可使用,仅供参考:value of quarterly field: Deferred Taxes'} +{'id': 62235, 'data_set_name': '可以使用:anl44_2_bps_value', 'description': '不可使用,仅供参考:bps value'} +{'id': 137417, 'data_set_name': '可以使用:usd_to_reporting_fx_rate', 'description': '不可使用,仅供参考:Exchange rate used to convert USD to the reporting currency.'} +{'id': 139000, 'data_set_name': '可以使用:other_unusual_income_expense_2', 'description': '不可使用,仅供参考:is applicable only if Net Income/Starting Line [ONET] is income after gains/losses from unusual activities. Because the operating section of the cash flow pursues changes in cash flow from operating activities, any unusual gain/loss included in net income computation needs to be reversed.'} +{'id': 139790, 'data_set_name': '可以使用:fnd28_ratesq_value_08321q', 'description': '不可使用,仅供参考:value of quarterly field: Pre-tax Margin'} +{'id': 62069, 'data_set_name': '可以使用:quarterly_interest_expense', 'description': '不可使用,仅供参考:Interest expense for the most recent quarter.'} +{'id': 140480, 'data_set_name': '可以使用:fnd72_pit_or_bs_a_st_capital_lease_obligations', 'description': '不可使用,仅供参考:Amount payable within 1 year by a lessee under capital leases'} +{'id': 139995, 'data_set_name': '可以使用:fnd28_value_18260q', 'description': '不可使用,仅供参考:value of quarterly field: Excludes Pre-tax Extraordinary Credit/Charge per Share - Fiscal'} +{'id': 62386, 'data_set_name': '可以使用:anl45_index_period_start_prc', 'description': '不可使用,仅供参考:Benchmark/Index start price based on the performance period you chose'} +{'id': 62269, 'data_set_name': '可以使用:anl44_2_ebitdaps_prevalue', 'description': '不可使用,仅供参考:ebitdaps prevalue'} +{'id': 139608, 'data_set_name': '可以使用:fnd28_nddq1_value_03063q', 'description': '不可使用,仅供参考:value of quarterly field: Income Taxes Payable'} +{'id': 60787, 'data_set_name': '可以使用:anl14_high_ebit_fy3', 'description': '不可使用,仅供参考:The Highest Estimation of Earnings Before Interest & Taxes - upcoming 3 years'} +{'id': 61022, 'data_set_name': '可以使用:anl14_low_revenue_fp4', 'description': '不可使用,仅供参考:The Lowest Estimation of Revenue - upcoming 4 quarters'} +{'id': 60717, 'data_set_name': '可以使用:social_totalcorr_industry_percentile', 'description': '不可使用,仅供参考:Percentile ranking within industry peer group for social score weighted by total correlation to financial returns.'} +{'id': 137452, 'data_set_name': '可以使用:accumulated_other_comprehensive_assets', 'description': '不可使用,仅供参考:Accumulated other comprehensive assets at period end.'} +{'id': 60843, 'data_set_name': '可以使用:anl14_high_ntprep_fp2', 'description': '不可使用,仅供参考:The Highest Estimation of Reported Net Profit - Upcoming 2 Quarters'} +{'id': 139310, 'data_set_name': '可以使用:fnd28_cfq_value_04301q', 'description': '不可使用,仅供参考:value of quarterly field: Proceeds From Stock Options'} +{'id': 62134, 'data_set_name': '可以使用:quarterly_price_to_sales_ratio', 'description': '不可使用,仅供参考:Price divided by sales per share for the quarter.'} +{'id': 61992, 'data_set_name': '可以使用:quarterly_cash_and_equivalents', 'description': '不可使用,仅供参考:Cash and short-term investments for the most recent quarter.'} +{'id': 139171, 'data_set_name': '可以使用:voting_rights_total', 'description': '不可使用,仅供参考:Total number of voting rights outstanding.'} +{'id': 139157, 'data_set_name': '可以使用:value_debt_equity_securities', 'description': '不可使用,仅供参考:Value of debt and equity securities held.'} +{'id': 139875, 'data_set_name': '可以使用:fnd28_value_04054q', 'description': '不可使用,仅供参考:value of quarterly field: Discontinued Operations'} +{'id': 62288, 'data_set_name': '可以使用:anl44_2_grossmargin_lastactvalue', 'description': '不可使用,仅供参考:grossmargin lastactvalue'} +{'id': 60936, 'data_set_name': '可以使用:anl14_low_ebit_fy2', 'description': '不可使用,仅供参考:The lowest estimation of earnings before interest & taxes - upcoming 2 years'} +{'id': 139540, 'data_set_name': '可以使用:fnd28_ishtspmnt_value_01084a', 'description': '不可使用,仅供参考:value of annual field: Staff Costs'} +{'id': 136514, 'data_set_name': '可以使用:fnd13_statementq_rltr', 'description': '不可使用,仅供参考:Total Revenue'} +{'id': 139278, 'data_set_name': '可以使用:fnd28_bsassetq_value_02005q', 'description': '不可使用,仅供参考:value of quarterly field: Cash & Equivalents Generic'} +{'id': 61977, 'data_set_name': '可以使用:quarterly_accumulated_depreciation_2', 'description': '不可使用,仅供参考:Total depreciation accumulated for the quarter.'} +{'id': 140066, 'data_set_name': '可以使用:fnd28_value_19541', 'description': '不可使用,仅供参考:value of field: Product Segment 5 - Sales'} +{'id': 137307, 'data_set_name': '可以使用:interim_debt_to_capital_ratio', 'description': '不可使用,仅供参考:Ratio of total debt to total capital for the most recent interim period.'} +{'id': 138931, 'data_set_name': '可以使用:long_term_debt', 'description': '不可使用,仅供参考:Total value of long-term debt obligations outstanding.'} +{'id': 61271, 'data_set_name': '可以使用:anl14_median_nav_fy2', 'description': '不可使用,仅供参考:Median of estimations of net asset value - upcoming 2 years'} +{'id': 140566, 'data_set_name': '可以使用:fnd72_pit_or_cf_a_cf_net_chng_cash', 'description': '不可使用,仅供参考:Net Changes in Cash'} +{'id': 60777, 'data_set_name': '可以使用:anl14_high_div_fy3', 'description': '不可使用,仅供参考:The Highest Estimation of Dividend - Upcoming 3 Years'} +{'id': 140763, 'data_set_name': '可以使用:fnd72_s_pit_or_bs_q_1_minority_noncontrolling_interest', 'description': '不可使用,仅供参考:Minority Interest'} +{'id': 62263, 'data_set_name': '可以使用:anl44_2_ebitda_lastactvalue', 'description': '不可使用,仅供参考:ebitda lastactvalue'} +{'id': 140053, 'data_set_name': '可以使用:fnd28_value_19513', 'description': '不可使用,仅供参考:value of field: Product Segment 2 - Assets'} +{'id': 139238, 'data_set_name': '可以使用:fnd28_bdeq_value_03351q', 'description': '不可使用,仅供参考:value of quarterly field: Total Liabilities'} +{'id': 140068, 'data_set_name': '可以使用:fnd28_value_19543', 'description': '不可使用,仅供参考:value of field: Product Segment 5 - Assets'} +{'id': 137438, 'data_set_name': '可以使用:accounts_total_current_assets_2', 'description': '不可使用,仅供参考:Total current assets as reported in accounts.'} +{'id': 373519, 'data_set_name': '可以使用:top1500_pca_factor1_grouping50', 'description': '不可使用,仅供参考:First principal component grouping for top 1500 securities with 50 clusters.'} +{'id': 60734, 'data_set_name': '可以使用:anl14_actvalue_opp_fp0', 'description': '不可使用,仅供参考:Operating profit - recent last quarter'} +{'id': 62146, 'data_set_name': '可以使用:quarterly_receivables_turnover', 'description': '不可使用,仅供参考:Annualized ratio of sales to average receivables for the quarter.'} +{'id': 139626, 'data_set_name': '可以使用:fnd28_nddq1_value_04001q', 'description': '不可使用,仅供参考:value of quarterly field: Net Income/Starting Line'} +{'id': 136451, 'data_set_name': '可以使用:share_award_value_ltip', 'description': '不可使用,仅供参考:Value of shares awarded in a given incentive plan for LTIP compensation.'} +{'id': 139611, 'data_set_name': '可以使用:fnd28_nddq1_value_03245q', 'description': '不可使用,仅供参考:value of quarterly field: Long Term Debt Excluding Capitalized Leases'} +{'id': 60573, 'data_set_name': '可以使用:anl11_e2reg_industryperc', 'description': '不可使用,仅供参考:Industry Percentile on E2 100% being the best'} +{'id': 139947, 'data_set_name': '可以使用:fnd28_value_08721', 'description': '不可使用,仅供参考:value of field: Foreign Sales Growth'} +{'id': 373189, 'data_set_name': '可以使用:industry', 'description': '不可使用,仅供参考:Industry grouping'} +{'id': 61789, 'data_set_name': '可以使用:anl15_ind_cal_fy1_total', 'description': '不可使用,仅供参考:The total of earnings estimation for the calendarized 1 fiscal year aggregated within GICS industry grouping.'} +{'id': 139788, 'data_set_name': '可以使用:fnd28_ratesq_value_08311q', 'description': '不可使用,仅供参考:value of quarterly field: Cash Flow/Sales'} +{'id': 139912, 'data_set_name': '可以使用:fnd28_value_08003', 'description': '不可使用,仅供参考:value of field: Market Capitalization (Public) - Current'} +{'id': 140137, 'data_set_name': '可以使用:fnd31_devnorthamericaadditionalfactor4_y5speq4vc', 'description': '不可使用,仅供参考:Stability of 5-yr TTM Earnings per Share. It is defined as the standard deviation of the last 20-Month trailing 12-month earnings per share (EPS) divided by the mean of these EPSs.'} +{'id': 139894, 'data_set_name': '可以使用:fnd28_value_05194q', 'description': '不可使用,仅供参考:value of quarterly field: Common Shares Used to Calculate Fully Diluted EPS Balance Sheet Assets'} +{'id': 136548, 'data_set_name': '可以使用:book_value_per_share_annual', 'description': '不可使用,仅供参考:Book value per share for the most recent fiscal year.'} +{'id': 373337, 'data_set_name': '可以使用:pv173_rawratiosmt5yzspreadzscore60dsbst', 'description': '不可使用,仅供参考:It is defined as the 60-day z-score of 5-year mid z-spreadIn the bond z-spread curve'} +{'id': 139009, 'data_set_name': '可以使用:quarterly_item_value', 'description': '不可使用,仅供参考:Value of the item for the specific quarter.'} +{'id': 62147, 'data_set_name': '可以使用:quarterly_receivables_turnover_2', 'description': '不可使用,仅供参考:Receivables turnover ratio for the most recent quarter.'} +{'id': 139480, 'data_set_name': '可以使用:fnd28_growthratesa_value_08616a', 'description': '不可使用,仅供参考:value of annual field: Equity Growth (year ago)'} +{'id': 139214, 'data_set_name': '可以使用:fnd28_bdea_value_03426a', 'description': '不可使用,仅供参考:value of annual field: Minority Interest'} +{'id': 137421, 'data_set_name': '可以使用:fnd22_a5_value', 'description': '不可使用,仅供参考:Value of the field'} +{'id': 60751, 'data_set_name': '可以使用:anl14_high_bvps_fy5', 'description': '不可使用,仅供参考:The Highest Estimation of Book Value Per Share - upcoming 5 years'} +{'id': 136426, 'data_set_name': '可以使用:intrinsic_value_exercisable_options', 'description': '不可使用,仅供参考:Actual value of exercisable options awarded during the period.'} +{'id': 373567, 'data_set_name': '可以使用:pv37_high_13', 'description': '不可使用,仅供参考:High price'} +{'id': 140478, 'data_set_name': '可以使用:fnd72_pit_or_bs_a_lt_capital_lease_obligations', 'description': '不可使用,仅供参考:Noncurrent amount payable by a lessee under capital leases'} +{'id': 139914, 'data_set_name': '可以使用:fnd28_value_08106', 'description': '不可使用,仅供参考:value of field: Current Ratio'} +{'id': 139138, 'data_set_name': '可以使用:treasury_stock_current', 'description': '不可使用,仅供参考:[Quarterly] Treasury Stock - Common'} +{'id': 136602, 'data_set_name': '可以使用:fnd17_7_usdtorepexrate', 'description': '不可使用,仅供参考:Reporting Currency to USD exchange rate'} +{'id': 139392, 'data_set_name': '可以使用:fnd28_fsa1_value_02101a', 'description': '不可使用,仅供参考:value of annual field: Inventories Total'} +{'id': 139319, 'data_set_name': '可以使用:fnd28_cfq_value_04701q', 'description': '不可使用,仅供参考:value of quarterly field: Reduction In Long Term Debt'} +{'id': 140938, 'data_set_name': '可以使用:srp_price_momentum_score', 'description': '不可使用,仅供参考:Price momentum score'} +{'id': 140089, 'data_set_name': '可以使用:fnd28_wsannualstats_value_05507', 'description': '不可使用,仅供参考:value of field: Free Cash Flow per Share'} +{'id': 139360, 'data_set_name': '可以使用:fnd28_cfsourceusea_value_04796a', 'description': '不可使用,仅供参考:value of annual field: Other Sources - Investing'} +{'id': 62319, 'data_set_name': '可以使用:anl44_2_pretaxprofit_prevalue', 'description': '不可使用,仅供参考:pretaxprofit prevalue'} +{'id': 136572, 'data_set_name': '可以使用:fnd17_1alldelay1_reptoprcexrate', 'description': '不可使用,仅供参考:Reporting Currency to base currency exchange rate'} +{'id': 139633, 'data_set_name': '可以使用:fnd28_nddq1_value_04151q', 'description': '不可使用,仅供参考:value of quarterly field: Other Cash Flow - Total'} +{'id': 60960, 'data_set_name': '可以使用:anl14_low_epsrep_fp1', 'description': '不可使用,仅供参考:The Lowest Estimation of Reported Earnings Per Share - Upcoming Quarter'} +{'id': 140552, 'data_set_name': '可以使用:fnd72_pit_or_cf_a_cf_chng_non_cash_work_cap', 'description': '不可使用,仅供参考:Changes in Non-Cash Working Capital'} +{'id': 139107, 'data_set_name': '可以使用:short_term_securities_gain_adjustment', 'description': '不可使用,仅供参考:Adjustment for gains on short-term securities.'} +{'id': 62002, 'data_set_name': '可以使用:quarterly_cash_flow_per_share_nonannualized_prior', 'description': '不可使用,仅供参考:Cash flow per share for the quarter, not annualized, for the same quarter last year.'} +{'id': 139683, 'data_set_name': '可以使用:fnd28_newq_value_03151q', 'description': '不可使用,仅供参考:value of quarterly field: Working Capital'} +{'id': 61321, 'data_set_name': '可以使用:anl14_median_revenue_fp3', 'description': '不可使用,仅供参考:Median of estimations of Revenue - upcoming 3 quarters'} +{'id': 62122, 'data_set_name': '可以使用:quarterly_payout_ratio', 'description': '不可使用,仅供参考:Percentage of earnings paid as dividends for the quarter.'} +{'id': 139640, 'data_set_name': '可以使用:fnd28_nddq1_value_04447q', 'description': '不可使用,仅供参考:value of quarterly field: Other Uses - Financing'} +{'id': 139949, 'data_set_name': '可以使用:fnd28_value_08801', 'description': '不可使用,仅供参考:Value of field: Total Investment Return Year-Ago Growth Rates'} +{'id': 139468, 'data_set_name': '可以使用:fnd28_fsq1_value_04890q', 'description': '不可使用,仅供参考:value of quarterly field: Net Cash Flow - Financing'} +{'id': 139296, 'data_set_name': '可以使用:fnd28_cfq_value_03151q', 'description': '不可使用,仅供参考:value of quarterly field: Working Capital'} +{'id': 62315, 'data_set_name': '可以使用:anl44_2_operatingprofit_value', 'description': '不可使用,仅供参考:operatingprofit value'} +{'id': 62410, 'data_set_name': '可以使用:anl45_risk_free_rate', 'description': '不可使用,仅供参考:The unrealised return on an open idea'} +{'id': 139077, 'data_set_name': '可以使用:short_term_investment_cash_flow', 'description': '不可使用,仅供参考:Other Investing Cash Flow Items, Total represents all items reported within the investing activities in the cash flow statement, other than capital expenditures.'} +{'id': 140770, 'data_set_name': '可以使用:fnd72_s_pit_or_bs_q_2_bs_capital_reserve', 'description': '不可使用,仅供参考:Includes accumulative comprehensive income reserve, merger reserve, as well as all other reserves and equities not included in retained earnings'} +{'id': 139716, 'data_set_name': '可以使用:fnd28_newq_value_08421q', 'description': '不可使用,仅供参考:value of quarterly field: Capital Expenditure % Sales'} +{'id': 137528, 'data_set_name': '可以使用:equity_total_reported', 'description': '不可使用,仅供参考:Total equity as reported in the financial statements.'} +{'id': 136551, 'data_set_name': '可以使用:cash_and_equivalents_quarterly_2', 'description': '不可使用,仅供参考:Cash and short-term investments for the most recent quarter.'} +{'id': 62167, 'data_set_name': '可以使用:quarterly_revenue_per_share', 'description': '不可使用,仅供参考:Total revenue divided by average shares for the quarter.'} +{'id': 373409, 'data_set_name': '可以使用:pca_industry_grouping_method3_20', 'description': '不可使用,仅供参考:Industry grouping using third method and 20 clusters for top 3000 equities.'} +{'id': 60652, 'data_set_name': '可以使用:anl11_g_totalcorregsectperc', 'description': '不可使用,仅供参考:Sector Percentile on G Hybrid Score with 100% being the best'} +{'id': 136373, 'data_set_name': '可以使用:annual_base_salary', 'description': '不可使用,仅供参考:Base annual salary paid to the director.'} +{'id': 60967, 'data_set_name': '可以使用:anl14_low_epsrep_fy3', 'description': '不可使用,仅供参考:The lowest estimation of Reported Earnings Per Share - upcoming 3 years'} +{'id': 137404, 'data_set_name': '可以使用:total_liabilities_quarterly', 'description': '不可使用,仅供参考:Total liabilities reported for the most recent quarter.'} +{'id': 139900, 'data_set_name': '可以使用:fnd28_value_05376q', 'description': '不可使用,仅供参考:value of quarterly field: Common Dividends (Cash)'} +{'id': 136600, 'data_set_name': '可以使用:fnd17_6alldelay1_usdtorepexrate', 'description': '不可使用,仅供参考:Reporting Currency to USD exchange rate'} +{'id': 62330, 'data_set_name': '可以使用:anl44_2_roa_value', 'description': '不可使用,仅供参考:roa value'} +{'id': 62020, 'data_set_name': '可以使用:quarterly_current_liabilities_3', 'description': '不可使用,仅供参考:Sum of all current liabilities for the most recent quarter.'} +{'id': 139309, 'data_set_name': '可以使用:fnd28_cfq_value_04251q', 'description': '不可使用,仅供参考:value of quarterly field: Net Proceeds from Sale/Issuance of Common & Preferred'} +{'id': 62062, 'data_set_name': '可以使用:quarterly_free_cash_flow_per_share_prior', 'description': '不可使用,仅供参考:Free cash flow per share for the same quarter last year.'} +{'id': 139126, 'data_set_name': '可以使用:total_assets_4', 'description': '不可使用,仅供参考:Sum of all assets owned by the company.'} +{'id': 62091, 'data_set_name': '可以使用:quarterly_long_term_debt_to_equity_prior_year', 'description': '不可使用,仅供参考:Long-term debt divided by shareholder equity for the same quarter last year.'} +{'id': 373350, 'data_set_name': '可以使用:pv173_zscoresatlas_unit_name', 'description': '不可使用,仅供参考:Atlas unit name'} +{'id': 62195, 'data_set_name': '可以使用:quarterly_total_debt_to_assets_ratio', 'description': '不可使用,仅供参考:Total debt divided by total assets for the most recent quarter.'} +{'id': 139422, 'data_set_name': '可以使用:fnd28_fsa1_value_04851a', 'description': '不可使用,仅供参考:Value of annual field: Increase/Decrease in Cash & Short-Term Investments'} +{'id': 61014, 'data_set_name': '可以使用:anl14_low_ptprep_fy1', 'description': '不可使用,仅供参考:The lowest estimation of reported pretax profit - upcoming year'} +{'id': 139901, 'data_set_name': '可以使用:fnd28_value_05401q', 'description': '不可使用,仅供参考:value of quarterly field: Preferred Dividends (Cash)'} +{'id': 139288, 'data_set_name': '可以使用:fnd28_bsassetq_value_02501q', 'description': '不可使用,仅供参考:value of quarterly field: Property, Plant & Equipment - Net'} +{'id': 139614, 'data_set_name': '可以使用:fnd28_nddq1_value_03351q', 'description': '不可使用,仅供参考:value of quarterly field: Total Liabilities'} +{'id': 137783, 'data_set_name': '可以使用:fnd23_intfv_value', 'description': '不可使用,仅供参考:value.'} +{'id': 61269, 'data_set_name': '可以使用:anl14_median_epsrep_fy5', 'description': '不可使用,仅供参考:Median of Estimations of Reported Earnings Per Share - upcoming 5 years'} +{'id': 139294, 'data_set_name': '可以使用:fnd28_bsassetq_value_18280q', 'description': '不可使用,仅供参考:value of quarterly field: Goodwill/Cost in Excess of Assets Purchased'} +{'id': 137018, 'data_set_name': '可以使用:fnd17_ataxrate2', 'description': '不可使用,仅供参考:Tax rate - most recent fiscal year -1'} +{'id': 139864, 'data_set_name': '可以使用:fnd28_stpera_value_09621a', 'description': '不可使用,仅供参考:value of annual field: Price/Cash Flow Ratio - 5 Yr Avg'} +{'id': 61277, 'data_set_name': '可以使用:anl14_median_ndebt_fy1', 'description': '不可使用,仅供参考:Median of estimations of net debt - upcoming year'} +{'id': 60732, 'data_set_name': '可以使用:anl14_actvalue_ntp_fp0', 'description': '不可使用,仅供参考:Net Profit - recent last quarter'} +{'id': 136546, 'data_set_name': '可以使用:annual_total_shareholder_equity', 'description': '不可使用,仅供参考:Total shareholder equity for the most recent fiscal year.'} +========================= 数据字段结束 ======================================= + +以上数据字段和操作符, 按照Description说明组合, 但是每一个 alpha 组合的使用的数据字段和操作符不要过于集中, 在符合语法的情况下, 多尝试不同的组合 + +你再检查一下, 如果你使用了 +Operator abs does not support event inputs +Operator ts_mean does not support event inputs +Operator ts_scale does not support event inputs +Operator add does not support event inputs +Operator sign does not support event inputs +Operator greater does not support event inputs +Operator ts_av_diff does not support event inputs +Operator ts_quantile does not support event inputs +Operator ts_arg_min does not support event inputs +Operator divide does not support event inputs +Operator ts_corr does not support event inputs +Operator ts_decay_linear does not support event inputs +Operator ts_sum does not support event inputs +Operator ts_delay does not support event inputs +Operator ts_arg_max does not support event inputs +Operator ts_std_dev does not support event inputs +Operator ts_regression does not support event inputs +Operator ts_backfill does not support event inputs +Operator signed_power does not support event inputs +Operator ts_product does not support event inputs +Operator ts_zscore does not support event inputs +Operator group_rank does not support event inputs +Operator subtract does not support event inputs +Operator ts_delta does not support event inputs +Operator ts_rank does not support event inputs +Operator ts_count_nans does not support event inputs +Operator ts_covariance does not support event inputs +Operator multiply does not support event inputs +Operator if_else does not support event inputs +Operator group_neutralize does not support event inputs +Operator group_zscore does not support event inputs +Operator winsorize does not support event inputs +注意, 以上操作符不能使用事件类型的数据集, 以上操作符禁止使用事件类型的数据集!! \ No newline at end of file diff --git a/manual_prompt/2026/01/28/manual_prompt_20260128171705.txt b/manual_prompt/2026/01/28/manual_prompt_20260128171705.txt new file mode 100644 index 0000000..41694c5 --- /dev/null +++ b/manual_prompt/2026/01/28/manual_prompt_20260128171705.txt @@ -0,0 +1,814 @@ +压力波动率情绪溢价因子 +基于行为金融学和神经内分泌学交叉研究，投资者在高市场压力环境下（如VIX指数上升）会经历皮质醇等应激激素水平升高，导致认知功能受限、决策偏差增大，尤其表现为对低流动性资产的过度抛售。这种非理性行为在群体层面形成系统性定价偏差：低波动率资产（如防御性行业股票）在压力期被低估，而高波动率资产（如周期性行业股票）被高估。当压力缓解后，市场会逐渐纠正这种偏差，从而产生可捕获的超额收益。因此，通过构建压力波动率情绪溢价因子，做多低波动率资产、做空高波动率资产，可在压力释放后获取稳定收益。 +压力缓解后，市场会逐渐纠正这种偏差，从而产生可捕获的超额收益。 +从EAV数据库中提取以下核心指标：VIX指数（衡量市场压力）、个股波动率（20日收益率标准差）、行业平均波动率、个股交易量与行业平均交易量比值。计算个股波动率与行业波动率的比值作为基础因子，进行Z-score标准化处理，并通过行业中性化回归消除行业系统性影响。使用20日滚动窗口计算指标，并用线性插值填充缺失值。引入VIX指数作为压力代理变量，通过动态因子模型提取压力波动率情绪公共因子，最终因子得分由波动率偏离度、交易量异动和市场压力水平加权组合而成。 +*=========================================================================================* +输出格式： +输出必须是且仅是纯文本。 +每一行是一个完整、独立、语法正确的WebSim表达式。 +严禁任何形式的解释、编号、标点包裹（如引号）、Markdown格式或额外文本。 +===================== !!! 重点(输出方式) !!! ===================== +现在，请严格遵守以上所有规则，开始生成可立即在WebSim中运行的复合因子表达式。 +不要自行假设, 你需要用到的操作符 和 数据集, 必须从我提供给你的里面查找, 并严格按照里面的使用方法进行组合 +**输出格式**(一行一个表达式, 每个表达式中间需要添加一个空行, 只要表达式本身, 不需要赋值, 不要解释, 不需要序号, 也不要输出多余的东西)： +表达式 +表达式 +表达式 +... +表达式 +================================================================= +重申：请确保所有表达式都使用WorldQuant WebSim平台函数，不要使用pandas、numpy或其他Python库函数。输出必须是一行有效的WQ表达式。 +以下是我的账号有权限使用的操作符, 请严格按照操作符, 以及我提供的数据集, 进行生成,组合 50 个 alpha： +不要自行假设, 你需要用到的操作符 和 数据集, 必须从我提供给你的里面查找, 并严格按照里面的使用方法进行组合 +!! 数据集使用要求尽量分散, 不要重复使用 +!! 数据集使用要求尽量分散, 不要重复使用 +================================================================= +**操作符汇总 +**算术运算符 (Arithmetic): +abs(x) - 绝对值 +add(x, y, filter=false) - 加法 (x + y) +densify(x) - 分组字段稠密化 +divide(x, y) - 除法 (x / y) +inverse(x) - 倒数 (1/x) +log(x) - 自然对数 +max(x, y, ..) - 最大值 +min(x, y, ..) - 最小值 +multiply(x, y, filter=false) - 乘法 (x * y) +power(x, y) - 幂运算 (x^y) +reverse(x) - 取反 (-x) +sign(x) - 符号函数 +signed_power(x, y) - 保留符号的幂运算 +sqrt(x) - 平方根 +subtract(x, y, filter=false) - 减法 (x - y) +to_nan(x, value=0, reverse=false) - 值与NaN转换 +**逻辑运算符 (Logical): +and(input1, input2) - 逻辑与 +if_else(input1, input2, input3) - 条件判断 +input1 < input2 - 小于比较 +input1 <= input2 - 小于等于 +input1 == input2 - 等于比较 +input1 > input2 - 大于比较 +input1 >= input2 - 大于等于 +input1 != input2 - 不等于 +is_nan(input) - 是否为NaN +not(x) - 逻辑非 +or(input1, input2) - 逻辑或 +**时间序列运算符 (Time Series): +days_from_last_change(x) - 上次变化天数 +hump(x, hump=0.01) - 限制变化幅度 +jump_decay(x, d, sensitivity=0.5, force=0.1) - 跳跃衰减 +kth_element(x, d, k) - 第K个值 +last_diff_value(x, d) - 最后一个不同值 +ts_arg_max(x, d) - 最大值相对索引 +ts_arg_min(x, d) - 最小值相对索引 +ts_av_diff(x, d) - 与均值的差 +ts_backfill(x, lookback=d, k=1, ignore="NAN") - 回填 +ts_corr(x, y, d) - 时间序列相关性 +ts_count_nans(x, d) - NaN计数 +ts_covariance(y, x, d) - 协方差 +ts_decay_linear(x, d, dense=false) - 线性衰减 +ts_delay(x, d) - 延迟值 +ts_delta(x, d) - 差值 (x - 延迟值) +ts_max(x, d) - 时间序列最大值 +ts_mean(x, d) - 时间序列均值 +ts_min(x, d) - 时间序列最小值 +ts_product(x, d) - 时间序列乘积 +ts_quantile(x, d, driver="gaussian") - 分位数 +ts_rank(x, d, constant=0) - 时间序列排名 +ts_regression(y, x, d, lag=0, rettype=0) - 回归分析 +ts_scale(x, d, constant=0) - 时间序列缩放 +ts_std_dev(x, d) - 时间序列标准差 +ts_step(1) - 天数计数器 +ts_sum(x, d) - 时间序列求和 +ts_target_tvr_decay(x, lambda_min=0, lambda_max=1, target_tvr=0.1) - 目标换手率衰减 +ts_target_tvr_delta_limit(x, y, lambda_min=0, lambda_max=1, target_tvr=0.1) - 目标换手率差值限制 +ts_zscore(x, d) - 时间序列Z分数 +**横截面运算符 (Cross Sectional): +normalize(x, useStd=false, limit=0.0) - 标准化 +quantile(x, driver=gaussian, sigma=1.0) - 分位数转换 +rank(x, rate=2) - 排名 +scale(x, scale=1, longscale=1, shortscale=1) - 缩放 +scale_down(x, constant=0) - 按比例缩放 +vector_neut(x, y) - 向量中性化 +winsorize(x, std=4) - 缩尾处理 +zscore(x) - Z分数 +**向量运算符 (Vector): +vec_avg(x) - 向量均值 +vec_max(x) - 向量最大值 +vec_min(x) - 向量最小值 +vec_sum(x) - 向量求和 +**变换运算符 (Transformational): +bucket(rank(x), range="0,1,0.1" or buckets="2,5,6,7,10") - 分桶 +generate_stats(alpha) - 生成统计量 +trade_when(x, y, z) - 条件交易 +**分组运算符 (Group): +combo_a(alpha, nlength=250, mode='algo1') - 组合Alpha +group_backfill(x, group, d, std=4.0) - 分组回填 +group_cartesian_product(g1, g2) - 笛卡尔积分组 +group_max(x, group) - 分组最大值 +group_mean(x, weight, group) - 分组均值 +group_min(x, group) - 分组最小值 +group_neutralize(x, group) - 分组中性化 +group_rank(x, group) - 分组排名 +group_scale(x, group) - 分组缩放 +group_zscore(x, group) - 分组Z分数 +**特殊运算符 (Special): +in - 包含判断 +self_corr(input) - 自相关性 +universe_size - 宇宙大小 +**归约运算符 (Reduce): +reduce_avg(input, threshold=0) - 平均值归约 +reduce_choose(input, nth, ignoreNan=true) - 选择归约 +reduce_count(input, threshold) - 计数归约 +reduce_ir(input) - IR归约 +reduce_kurtosis(input) - 峰度归约 +reduce_max(input) - 最大值归约 +reduce_min(input) - 最小值归约 +reduce_norm(input) - 范数归约 +reduce_percentage(input, percentage=0.5) - 百分比归约 +reduce_powersum(input, constant=2, precise=false) - 幂和归约 +reduce_range(input) - 范围归约 +reduce_skewness(input) - 偏度归约 +reduce_stddev(input, threshold=0) - 标准差归约 +reduce_sum(input) - 求和归约 + +以下是我的账号有权限使用的操作符, 请严格按照操作符, 进行生成,组合因子 + +========================= 操作符开始 ======================================= +注意: Operator: 后面的是操作符(是可以使用的), +Description: 此字段后面的是操作符对应的描述或使用说明(禁止使用, 仅供参考), Description字段后面的内容是使用说明, 不是操作符 +特别注意!!!! 必须按照操作符字段Operator的使用说明生成 alphaOperator: abs(x) +Description: Absolute value of x +Operator: add(x, y, filter = false) +Description: Add all inputs (at least 2 inputs required). If filter = true, filter all input NaN to 0 before adding +Operator: densify(x) +Description: Converts a grouping field of many buckets into lesser number of only available buckets so as to make working with grouping fields computationally efficient +Operator: divide(x, y) +Description: x / y +Operator: inverse(x) +Description: 1 / x +Operator: log(x) +Description: Natural logarithm. For example: Log(high/low) uses natural logarithm of high/low ratio as stock weights. +Operator: max(x, y, ..) +Description: Maximum value of all inputs. At least 2 inputs are required +Operator: min(x, y ..) +Description: Minimum value of all inputs. At least 2 inputs are required +Operator: multiply(x ,y, ... , filter=false) +Description: Multiply all inputs. At least 2 inputs are required. Filter sets the NaN values to 1 +Operator: power(x, y) +Description: x ^ y +Operator: reverse(x) +Description: - x +Operator: sign(x) +Description: if input > 0, return 1; if input < 0, return -1; if input = 0, return 0; if input = NaN, return NaN; +Operator: signed_power(x, y) +Description: x raised to the power of y such that final result preserves sign of x +Operator: sqrt(x) +Description: Square root of x +Operator: subtract(x, y, filter=false) +Description: x-y. If filter = true, filter all input NaN to 0 before subtracting +Operator: and(input1, input2) +Description: Logical AND operator, returns true if both operands are true and returns false otherwise +Operator: if_else(input1, input2, input 3) +Description: If input1 is true then return input2 else return input3. +Operator: input1 < input2 +Description: If input1 < input2 return true, else return false +Operator: input1 <= input2 +Description: Returns true if input1 <= input2, return false otherwise +Operator: input1 == input2 +Description: Returns true if both inputs are same and returns false otherwise +Operator: input1 > input2 +Description: Logic comparison operators to compares two inputs +Operator: input1 >= input2 +Description: Returns true if input1 >= input2, return false otherwise +Operator: input1!= input2 +Description: Returns true if both inputs are NOT the same and returns false otherwise +Operator: is_nan(input) +Description: If (input == NaN) return 1 else return 0 +Operator: not(x) +Description: Returns the logical negation of x. If x is true (1), it returns false (0), and if input is false (0), it returns true (1). +Operator: or(input1, input2) +Description: Logical OR operator returns true if either or both inputs are true and returns false otherwise +Operator: days_from_last_change(x) +Description: Amount of days since last change of x +Operator: hump(x, hump = 0.01) +Description: Limits amount and magnitude of changes in input (thus reducing turnover) +Operator: kth_element(x, d, k) +Description: Returns K-th value of input by looking through lookback days. This operator can be used to backfill missing data if k=1 +Operator: last_diff_value(x, d) +Description: Returns last x value not equal to current x value from last d days +Operator: ts_arg_max(x, d) +Description: Returns the relative index of the max value in the time series for the past d days. If the current day has the max value for the past d days, it returns 0. If previous day has the max value for the past d days, it returns 1 +Operator: ts_arg_min(x, d) +Description: Returns the relative index of the min value in the time series for the past d days; If the current day has the min value for the past d days, it returns 0; If previous day has the min value for the past d days, it returns 1. +Operator: ts_av_diff(x, d) +Description: Returns x - tsmean(x, d), but deals with NaNs carefully. That is NaNs are ignored during mean computation +Operator: ts_backfill(x,lookback = d, k=1, ignore="NAN") +Description: Backfill is the process of replacing the NAN or 0 values by a meaningful value (i.e., a first non-NaN value) +Operator: ts_corr(x, y, d) +Description: Returns correlation of x and y for the past d days +Operator: ts_count_nans(x ,d) +Description: Returns the number of NaN values in x for the past d days +Operator: ts_covariance(y, x, d) +Description: Returns covariance of y and x for the past d days +Operator: ts_decay_linear(x, d, dense = false) +Description: Returns the linear decay on x for the past d days. Dense parameter=false means operator works in sparse mode and we treat NaN as 0. In dense mode we do not. +Operator: ts_delay(x, d) +Description: Returns x value d days ago +Operator: ts_delta(x, d) +Description: Returns x - ts_delay(x, d) +Operator: ts_mean(x, d) +Description: Returns average value of x for the past d days. +Operator: ts_product(x, d) +Description: Returns product of x for the past d days +Operator: ts_quantile(x,d, driver="gaussian" ) +Description: It calculates ts_rank and apply to its value an inverse cumulative density function from driver distribution. Possible values of driver (optional ) are "gaussian", "uniform", "cauchy" distribution where "gaussian" is the default. +Operator: ts_rank(x, d, constant = 0) +Description: Rank the values of x for each instrument over the past d days, then return the rank of the current value + constant. If not specified, by default, constant = 0. +Operator: ts_regression(y, x, d, lag = 0, rettype = 0) +Description: Returns various parameters related to regression function +Operator: ts_scale(x, d, constant = 0) +Description: Returns (x - ts_min(x, d)) / (ts_max(x, d) - ts_min(x, d)) + constant. This operator is similar to scale down operator but acts in time series space +Operator: ts_std_dev(x, d) +Description: Returns standard deviation of x for the past d days +Operator: ts_step(1) +Description: Returns days' counter +Operator: ts_sum(x, d) +Description: Sum values of x for the past d days. +Operator: ts_zscore(x, d) +Description: Z-score is a numerical measurement that describes a value's relationship to the mean of a group of values. Z-score is measured in terms of standard deviations from the mean: (x - tsmean(x,d)) / tsstddev(x,d). This operator may help reduce outliers and drawdown. +Operator: normalize(x, useStd = false, limit = 0.0) +Description: Calculates the mean value of all valid alpha values for a certain date, then subtracts that mean from each element +Operator: quantile(x, driver = gaussian, sigma = 1.0) +Description: Rank the raw vector, shift the ranked Alpha vector, apply distribution (gaussian, cauchy, uniform). If driver is uniform, it simply subtract each Alpha value with the mean of all Alpha values in the Alpha vector +Operator: rank(x, rate=2) +Description: Ranks the input among all the instruments and returns an equally distributed number between 0.0 and 1.0. For precise sort, use the rate as 0 +Operator: scale(x, scale=1, longscale=1, shortscale=1) +Description: Scales input to booksize. We can also scale the long positions and short positions to separate scales by mentioning additional parameters to the operator +Operator: winsorize(x, std=4) +Description: Winsorizes x to make sure that all values in x are between the lower and upper limits, which are specified as multiple of std. +Operator: zscore(x) +Description: Z-score is a numerical measurement that describes a value's relationship to the mean of a group of values. Z-score is measured in terms of standard deviations from the mean +Operator: vec_avg(x) +Description: Taking mean of the vector field x +Operator: vec_sum(x) +Description: Sum of vector field x +Operator: bucket(rank(x), range="0, 1, 0.1" or buckets = "2,5,6,7,10") +Description: Convert float values into indexes for user-specified buckets. Bucket is useful for creating group values, which can be passed to GROUP as input +Operator: trade_when(x, y, z) +Description: Used in order to change Alpha values only under a specified condition and to hold Alpha values in other cases. It also allows to close Alpha positions (assign NaN values) under a specified condition +Operator: group_backfill(x, group, d, std = 4.0) +Description: If a certain value for a certain date and instrument is NaN, from the set of same group instruments, calculate winsorized mean of all non-NaN values over last d days +Operator: group_mean(x, weight, group) +Description: All elements in group equals to the mean +Operator: group_neutralize(x, group) +Description: Neutralizes Alpha against groups. These groups can be subindustry, industry, sector, country or a constant +Operator: group_rank(x, group) +Description: Each elements in a group is assigned the corresponding rank in this group +Operator: group_scale(x, group) +Description: Normalizes the values in a group to be between 0 and 1. (x - groupmin) / (groupmax - groupmin) +Operator: group_zscore(x, group) +Description: Calculates group Z-score - numerical measurement that describes a value's relationship to the mean of a group of values. Z-score is measured in terms of standard deviations from the mean. zscore = (data - mean) / stddev of x for each instrument within its group. +========================= 操作符结束 ======================================= + +========================= 数据字段开始 ======================================= +注意: data_set_name: 后面的是数据字段(可以使用), description: 此字段后面的是数据字段对应的描述或使用说明(不能使用) + +{'id': 60875, 'data_set_name': '可以使用:anl14_high_revenue_fy2', 'description': '不可使用,仅供参考:The highest estimation of revenue - upcoming 2 years'} +{'id': 60939, 'data_set_name': '可以使用:anl14_low_ebit_fy5', 'description': '不可使用,仅供参考:The Lowest Estimation of Earnings Before Interest & Taxes - Upcoming 5 Years'} +{'id': 60870, 'data_set_name': '可以使用:anl14_high_revenue_fp2', 'description': '不可使用,仅供参考:The highest estimation of revenue - upcoming 2 quarters'} +{'id': 60786, 'data_set_name': '可以使用:anl14_high_ebit_fy2', 'description': '不可使用,仅供参考:The highest estimation of Earnings Before Interest & Taxes - upcoming 2 years'} +{'id': 60959, 'data_set_name': '可以使用:anl14_low_eps_fy5', 'description': '不可使用,仅供参考:The Lowest Estimation of Earnings Per Share - upcoming 5 years'} +{'id': 139119, 'data_set_name': '可以使用:statement_of_cash_flows', 'description': '不可使用,仅供参考:Statement of cash flows for the annual period.'} +{'id': 140167, 'data_set_name': '可以使用:fnd31_qsg5additionalfactor3_monthly_pcdwf', 'description': '不可使用,仅供参考:Leading 12 Month Cash Yield. It is defined as the average quarterly forward-looking cash flows per share divided by close price.'} +{'id': 60783, 'data_set_name': '可以使用:anl14_high_ebit_fp4', 'description': '不可使用,仅供参考:The highest estimation of earnings before interest & taxes - upcoming 4 quarters'} +{'id': 139088, 'data_set_name': '可以使用:short_term_net_cash_balance', 'description': '不可使用,仅供参考:Net Cash – Beginning Balance represents the beginning balance of cash and cash equivalents, as\r\ndefined by a company.'} +{'id': 138995, 'data_set_name': '可以使用:other_off_balance_liabilities', 'description': '不可使用,仅供参考:Other off-balance sheet liabilities for the annual period.'} +{'id': 140126, 'data_set_name': '可以使用:fnd31_devnorthamericaadditionalfactor4_pbdwf', 'description': '不可使用,仅供参考:Leading 12-Month Book Yield. It is defined as the average quarterly forward-looking book value per share divided by close price.'} +{'id': 373426, 'data_set_name': '可以使用:resampled_pca_factor_1', 'description': '不可使用,仅供参考:First principal component factor derived from robust resampling of returns for top 2000 securities.'} +{'id': 140601, 'data_set_name': '可以使用:fnd72_pit_or_cf_q_cf_other_non_cash_adj_less', 'description': '不可使用,仅供参考:Other Non-Cash Adjustments'} +{'id': 140859, 'data_set_name': '可以使用:fnd72_s_pit_or_cf_q_cf_incr_cap_stock', 'description': '不可使用,仅供参考:Increase in Capital Stocks'} +{'id': 60995, 'data_set_name': '可以使用:anl14_low_ntprep_fp4', 'description': '不可使用,仅供参考:The lowest estimation of reported net profit - upcoming 4 quarters'} +{'id': 373383, 'data_set_name': '可以使用:industry_grouping_level2_top1500', 'description': '不可使用,仅供参考:Industry classification at level 2 for the top 1500 instruments.'} +{'id': 60977, 'data_set_name': '可以使用:anl14_low_ndebt_fy1', 'description': '不可使用,仅供参考:The lowest estimation of net debt - upcoming year'} +{'id': 60837, 'data_set_name': '可以使用:anl14_high_ntp_fy1', 'description': '不可使用,仅供参考:The highest estimation of Net Profit - upcoming year'} +{'id': 140905, 'data_set_name': '可以使用:fnd72_s_pit_or_is_q_is_sga_other_op_depr_op_maint', 'description': '不可使用,仅供参考:Includes selling & administrative expenses and other operating expenses after cost of goods sold'} +{'id': 139074, 'data_set_name': '可以使用:short_term_group_payables', 'description': '不可使用,仅供参考:Short-term payables to group companies.'} +{'id': 137413, 'data_set_name': '可以使用:twelve_month_high_price', 'description': '不可使用,仅供参考:Highest price traded in the last twelve months.'} +{'id': 60874, 'data_set_name': '可以使用:anl14_high_revenue_fy1', 'description': '不可使用,仅供参考:The Highest Estimation of Revenue - upcoming year'} +{'id': 373645, 'data_set_name': '可以使用:pv72_ibesptg2_score', 'description': '不可使用,仅供参考:pv_score'} +{'id': 136550, 'data_set_name': '可以使用:cash_and_equivalents_annual_2', 'description': '不可使用,仅供参考:Cash and short-term investments for the most recent fiscal year.'} +{'id': 60869, 'data_set_name': '可以使用:anl14_high_revenue_fp1', 'description': '不可使用,仅供参考:The highest estimation of Revenue - upcoming quarter'} +{'id': 62154, 'data_set_name': '可以使用:quarterly_return_on_assets_percent', 'description': '不可使用,仅供参考:Annualized net income divided by average assets for the quarter, as a percentage.'} +{'id': 136445, 'data_set_name': '可以使用:other_cash_payments_2', 'description': '不可使用,仅供参考:Value of ad hoc cash payments such as relocation or fringe benefits (alternate).'} +{'id': 140568, 'data_set_name': '可以使用:fnd72_pit_or_cf_a_cf_other_fnc_act', 'description': '不可使用,仅供参考:Other Financing Activities'} +{'id': 62404, 'data_set_name': '可以使用:anl45_rel_index_ret', 'description': '不可使用,仅供参考:Difference in return % between making investment in idea and comparative index'} +{'id': 138941, 'data_set_name': '可以使用:long_term_loans_liabilities_2', 'description': '不可使用,仅供参考:Long-term loans and related liabilities outstanding.'} +{'id': 137543, 'data_set_name': '可以使用:financing_cashflow_net', 'description': '不可使用,仅供参考:Net cash flow from financing activities.'} +{'id': 60829, 'data_set_name': '可以使用:anl14_high_ndebt_fy3', 'description': '不可使用,仅供参考:The Highest Estimation of Net Debt - upcoming 3 years'} +{'id': 60526, 'data_set_name': '可以使用:anl11_cit3reg_industryperc', 'description': '不可使用,仅供参考:Industry Percentile on CIT3 100% being the best'} +{'id': 62061, 'data_set_name': '可以使用:quarterly_free_cash_flow_per_share_non_annualized_alt', 'description': '不可使用,仅供参考:Alternate calculation of free cash flow per share for the quarter, not annualized.'} +{'id': 373582, 'data_set_name': '可以使用:pv37_low_14', 'description': '不可使用,仅供参考:14-Day Lowest Price'} +{'id': 60800, 'data_set_name': '可以使用:anl14_high_eps_fp1', 'description': '不可使用,仅供参考:The highest estimation of Earnings Per Share - upcoming quarter'} +{'id': 62411, 'data_set_name': '可以使用:anl45_stock_ret_per', 'description': '不可使用,仅供参考:Net total return from all ideas since previous market close'} +{'id': 61988, 'data_set_name': '可以使用:quarterly_capital_spending_per_share', 'description': '不可使用,仅供参考:Capital spending divided by average shares for the quarter.'} +{'id': 137351, 'data_set_name': '可以使用:quarterly_debt_to_assets_ratio', 'description': '不可使用,仅供参考:Ratio of total debt to total assets for the most recent quarter.'} +{'id': 60938, 'data_set_name': '可以使用:anl14_low_ebit_fy4', 'description': '不可使用,仅供参考:The lowest estimation of earnings before interest & taxes - upcoming 4 years'} +{'id': 373324, 'data_set_name': '可以使用:pv173_rawratiosbondreturn20deqwt', 'description': '不可使用,仅供参考:It is defined as the equally weighted single bond return over last 20 days with the filter of the bonds that mature between 3 years and 7 years.'} +{'id': 140715, 'data_set_name': '可以使用:fnd72_pit_or_is_q_is_sga_other_op_depr_op_maint', 'description': '不可使用,仅供参考:Includes selling & administrative expenses and other operating expenses after cost of goods sold'} +{'id': 140504, 'data_set_name': '可以使用:fnd72_pit_or_bs_q_bs_mkt_sec_other_st_invest', 'description': '不可使用,仅供参考:Marketable Securities & Other ST Investments'} +{'id': 136548, 'data_set_name': '可以使用:book_value_per_share_annual', 'description': '不可使用,仅供参考:Book value per share for the most recent fiscal year.'} +{'id': 60981, 'data_set_name': '可以使用:anl14_low_ndebt_fy5', 'description': '不可使用,仅供参考:The Lowest Estimation of Net Debt - Upcoming 5 Years'} +{'id': 60658, 'data_set_name': '可以使用:anl11_global_industrycount', 'description': '不可使用,仅供参考:Global Industry Count'} +{'id': 140135, 'data_set_name': '可以使用:fnd31_devnorthamericaadditionalfactor4_twepsstdrev', 'description': '不可使用,仅供参考:Time Weighted Earnings Revision Dispersion. It is defined as the 6-month average of the time-weighted sum of the FY1 and FY2 earnings estimate revisions adjusted by the standard deviations of earnings estimates.'} +{'id': 138935, 'data_set_name': '可以使用:long_term_debt_value', 'description': '不可使用,仅供参考:Total amount of long-term debt obligations for the period.'} +{'id': 60824, 'data_set_name': '可以使用:anl14_high_ndebt_fp2', 'description': '不可使用,仅供参考:The Highest Estimation of Net Debt - Upcoming 2 Quarters'} +{'id': 137426, 'data_set_name': '可以使用:accounts_current_assets_equity', 'description': '不可使用,仅供参考:Current assets related to equity accounts.'} +{'id': 140159, 'data_set_name': '可以使用:fnd31_qsg5additionalfactor3_monthly_divgp', 'description': '不可使用,仅供参考:Shareholder Payout to Gross Profit. It is defined as total dividends paid divided by (revenue - cost of goods sold).'} +{'id': 137541, 'data_set_name': '可以使用:financing_cash_flow_2', 'description': '不可使用,仅供参考:Net cash flow from financing activities.'} +{'id': 60923, 'data_set_name': '可以使用:anl14_low_div_fp4', 'description': '不可使用,仅供参考:The Lowest Estimation of Dividend - Upcoming 4 Quarters'} +{'id': 62134, 'data_set_name': '可以使用:quarterly_price_to_sales_ratio', 'description': '不可使用,仅供参考:Price divided by sales per share for the quarter.'} +{'id': 139109, 'data_set_name': '可以使用:short_term_securities_investments_2', 'description': '不可使用,仅供参考:Short-term investments in securities at period end.'} +{'id': 139108, 'data_set_name': '可以使用:short_term_securities_investments', 'description': '不可使用,仅供参考:Short-term securities investments reported for the interim period.'} +{'id': 62188, 'data_set_name': '可以使用:quarterly_total_assets_change_yoy', 'description': '不可使用,仅供参考:Year-over-year percent change in total assets for the quarter.'} +{'id': 60984, 'data_set_name': '可以使用:anl14_low_ntp_fp3', 'description': '不可使用,仅供参考:The lowest estimation of net profit - upcoming 3 quarters'} +{'id': 62057, 'data_set_name': '可以使用:quarterly_free_cash_flow_per_share_alt', 'description': '不可使用,仅供参考:Alternative calculation of free cash flow per share for the quarter.'} +{'id': 61642, 'data_set_name': '可以使用:rtk_ptg_high', 'description': '不可使用,仅供参考:The highest of Price Target -'} +{'id': 140074, 'data_set_name': '可以使用:fnd28_wcannuala_value_08251', 'description': '不可使用,仅供参考:value of field: Fixed Charge Coverage Ratio'} +{'id': 140175, 'data_set_name': '可以使用:fnd31_qsg5additionalfactor3_monthly_y5speq4rqsr', 'description': '不可使用,仅供参考:R Sqr of 5-yr TTM EPS Trend Line. It is defined as the slope coefficient between monthly dates and the corresponding trailing 12-month earnings per share before extra items in the prior 20 quarters.'} +{'id': 60965, 'data_set_name': '可以使用:anl14_low_epsrep_fy1', 'description': '不可使用,仅供参考:The Lowest Estimation of Reported Earnings Per Share - upcoming year'} +{'id': 136446, 'data_set_name': '可以使用:security_market_price_ltip', 'description': '不可使用,仅供参考:Price of the stock at the relevant report date for LTIP compensation.'} +{'id': 373350, 'data_set_name': '可以使用:pv173_zscoresatlas_unit_name', 'description': '不可使用,仅供参考:Atlas unit name'} +{'id': 136444, 'data_set_name': '可以使用:other_board_total_count', 'description': '不可使用,仅供参考:Total number of other boards the director has served on.'} +{'id': 373384, 'data_set_name': '可以使用:industry_grouping_level2_top2000', 'description': '不可使用,仅供参考:Industry classification group at level 2 for the top 2000 instruments.'} +{'id': 138931, 'data_set_name': '可以使用:long_term_debt', 'description': '不可使用,仅供参考:Total value of long-term debt obligations outstanding.'} +{'id': 137550, 'data_set_name': '可以使用:fixed_long_term_debt_repayment', 'description': '不可使用,仅供参考:Repayment amount of fixed long-term debt during the period.'} +{'id': 139076, 'data_set_name': '可以使用:short_term_interest_notes', 'description': '不可使用,仅供参考:Short-term interest-bearing notes outstanding.'} +{'id': 373429, 'data_set_name': '可以使用:resampled_pca_factor_12', 'description': '不可使用,仅供参考:Twelfth principal component factor derived from robust resampling of returns for top 2000 securities.'} +{'id': 137452, 'data_set_name': '可以使用:accumulated_other_comprehensive_assets', 'description': '不可使用,仅供参考:Accumulated other comprehensive assets at period end.'} +{'id': 373362, 'data_set_name': '可以使用:pv173_zscoresmt5yzspreadsbst', 'description': '不可使用,仅供参考:It is defined as the region relative 5-year mid z-spreadIn the bond z-spread curve'} +{'id': 139346, 'data_set_name': '可以使用:fnd28_cfsourceusea_value_04351a', 'description': '不可使用,仅供参考:value of annual field: Disposal of Fixed Assets'} +{'id': 373371, 'data_set_name': '可以使用:pv173_zscorestilt5y1ydiff60dema60d', 'description': '不可使用,仅供参考:It is defined as the 60-day exponential average of the difference of factor loading of the second principal component for the 5-year z-spread and 1-year z-spread'} +{'id': 60716, 'data_set_name': '可以使用:social_score_total_correlation', 'description': '不可使用,仅供参考:Social score weighted by metrics most strongly correlated (positive and negative) to financial returns.'} +{'id': 136557, 'data_set_name': '可以使用:daily_volume_percent_shares_out', 'description': '不可使用,仅供参考:Average daily trading volume as a percentage of shares outstanding.'} +{'id': 139122, 'data_set_name': '可以使用:stockholders_other_comprehensive_assets', 'description': '不可使用,仅供参考:Other comprehensive assets attributable to stockholders.'} +{'id': 138996, 'data_set_name': '可以使用:other_operating_assets', 'description': '不可使用,仅供参考:Other operating assets reported for the annual period.'} +{'id': 140171, 'data_set_name': '可以使用:fnd31_qsg5additionalfactor3_monthly_rev6fy2', 'description': "不可使用,仅供参考:Averaged Last 6-M EPS Revisions for FY2. It is defined as the average of prior 6-month monthly changes in a stock's consensus analysts earnings forecasts for fiscal year 2, scaled by previous month-end trading price."} +{'id': 138976, 'data_set_name': '可以使用:other_assets_prepaid_balance', 'description': '不可使用,仅供参考:[Quarterly] Accounts Payable'} +{'id': 373348, 'data_set_name': '可以使用:pv173_rawratiostwist120dlast', 'description': '不可使用,仅供参考:It is defined as the average factor loading of the third principal component for the z-spread between 6 months and 40 years'} +{'id': 140125, 'data_set_name': '可以使用:fnd31_devnorthamericaadditionalfactor4_numrevy2', 'description': '不可使用,仅供参考:Net # of Revisions for Fiscal Year 2. It is defined as the weighted average of the number of FY2 analyst earnings forecasts raised less the number lowered within a month, divided by the total number of analyst forecasts.'} +{'id': 60821, 'data_set_name': '可以使用:anl14_high_nav_fy2', 'description': '不可使用,仅供参考:The Highest Estimation of Net Asset Value - Upcoming 2 Years'} +{'id': 139062, 'data_set_name': '可以使用:short_term_cash_market_securities', 'description': '不可使用,仅供参考:Short-term cash and marketable securities held.'} +{'id': 139050, 'data_set_name': '可以使用:shareholders_net_total_dividends', 'description': '不可使用,仅供参考:Total net dividends to shareholders for the interim period.'} +{'id': 139342, 'data_set_name': '可以使用:fnd28_cfsourceusea_value_04201a', 'description': '不可使用,仅供参考:value of annual field: Funds from Operations'} +{'id': 139048, 'data_set_name': '可以使用:share_premium_reserve', 'description': '不可使用,仅供参考:Preferred Stock – Non-Redeemable, Total.'} +{'id': 60744, 'data_set_name': '可以使用:anl14_high_bvps_fp3', 'description': '不可使用,仅供参考:The Highest Estimation of Book Value Per Share - upcoming 3 quarters'} +{'id': 373439, 'data_set_name': '可以使用:resampled_pca_factor_9', 'description': '不可使用,仅供参考:Ninth principal component factor derived from robust resampling of returns for top 2000 securities.'} +{'id': 60570, 'data_set_name': '可以使用:anl11_e1reg_industryrnk', 'description': '不可使用,仅供参考:Industry Rank on E1 1 being the highest rank'} +{'id': 60657, 'data_set_name': '可以使用:anl11_g_ttcrg_industryrnk', 'description': '不可使用,仅供参考:Industry Rank on G Hybrid Score with 1 being the highest rank'} +{'id': 137341, 'data_set_name': '可以使用:public_float_shares', 'description': '不可使用,仅供参考:Number of shares available for public trading (float).'} +{'id': 140445, 'data_set_name': '可以使用:fnd72_pit_or_bs_a_bs_other_cur_asset_less_prepay', 'description': '不可使用,仅供参考:Other assets which are not account and notes receivable, inventory, derivative assets, deferred tax assets, prepaid expenses, assets for held for sale, income taxes receivable, and assets of discontinued operations'} +{'id': 373411, 'data_set_name': '可以使用:pca_industry_grouping_method3_50', 'description': '不可使用,仅供参考:Industry grouping using third method and 50 clusters for top 3000 equities.'} +{'id': 62429, 'data_set_name': '可以使用:anl46_performancepercentile', 'description': '不可使用,仅供参考:A value from 1 to 100 expressing how well the Indicator has predicted price movement on the stock in the last 12 months'} +{'id': 140652, 'data_set_name': '可以使用:fnd72_pit_or_is_a_is_pension_category_other', 'description': '不可使用,仅供参考:The percentage of the total fair value of pension plan assets invested in other assets besides equity, debt, company stock, and cash at period end'} +{'id': 140506, 'data_set_name': '可以使用:fnd72_pit_or_bs_q_bs_options_outstanding', 'description': '不可使用,仅供参考:Options Outstanding End of Period'} +{'id': 137414, 'data_set_name': '可以使用:twelve_month_high_price_2', 'description': '不可使用,仅供参考:The highest price at which the security traded in the last twelve months.'} +{'id': 373432, 'data_set_name': '可以使用:resampled_pca_factor_2', 'description': '不可使用,仅供参考:Second principal component factor derived from robust resampling of returns for top 2000 securities.'} +{'id': 373427, 'data_set_name': '可以使用:resampled_pca_factor_10', 'description': '不可使用,仅供参考:Tenth principal component factor derived from robust resampling of returns for top 2000 securities.'} +{'id': 373197, 'data_set_name': '可以使用:sharesout', 'description': '不可使用,仅供参考:Daily outstanding shares (in millions)'} +{'id': 138934, 'data_set_name': '可以使用:long_term_debt_total_2', 'description': '不可使用,仅供参考:Total long-term debt outstanding.'} +{'id': 60758, 'data_set_name': '可以使用:anl14_high_capex_fy2', 'description': '不可使用,仅供参考:The Highest Estimation of Capital Expenditures - upcoming 2 years'} +{'id': 138965, 'data_set_name': '可以使用:net_premiums_written', 'description': '不可使用,仅供参考:Net premiums written during the period.'} +{'id': 136803, 'data_set_name': '可以使用:fnd17_alldelay1_pehigh', 'description': '不可使用,仅供参考:P/E excluding extraordinary items, High'} +{'id': 60934, 'data_set_name': '可以使用:anl14_low_ebit_fp5', 'description': '不可使用,仅供参考:The Lowest estimation of Earnings Before Interest & Taxes - upcoming 5 quarters'} +{'id': 61037, 'data_set_name': '可以使用:anl14_low_roe_fy1', 'description': '不可使用,仅供参考:The Lowest Estimation of Returns on Equity - Upcoming Year'} +{'id': 139044, 'data_set_name': '可以使用:share_capital_subscribed_2', 'description': '不可使用,仅供参考:Total value of subscribed share capital at the end of the period.'} +{'id': 60892, 'data_set_name': '可以使用:anl14_low_bvps_fp1', 'description': '不可使用,仅供参考:The lowest estimation of book value per share - upcoming quarter'} +{'id': 138997, 'data_set_name': '可以使用:other_operating_cash_flows', 'description': '不可使用,仅供参考:Other operating cash flows reported in interim financials.'} +{'id': 373524, 'data_set_name': '可以使用:top1500_pca_factor2_grouping50', 'description': '不可使用,仅供参考:Second principal component grouping for top 1500 securities with 50 clusters.'} +{'id': 136373, 'data_set_name': '可以使用:annual_base_salary', 'description': '不可使用,仅供参考:Base annual salary paid to the director.'} +{'id': 373330, 'data_set_name': '可以使用:pv173_rawratiosmt5yzspreadchg60dsbst', 'description': '不可使用,仅供参考:It is defined as the 60-day change of 5-year mid z-spreadIn the bond z-spread curve'} +{'id': 137397, 'data_set_name': '可以使用:shares_outstanding_current', 'description': '不可使用,仅供参考:Current number of shares outstanding for the security.'} +{'id': 62079, 'data_set_name': '可以使用:quarterly_long_term_debt_2', 'description': '不可使用,仅供参考:Long-term debt for the most recent quarter.'} +{'id': 62026, 'data_set_name': '可以使用:quarterly_current_ratio_prior_year', 'description': '不可使用,仅供参考:Current assets divided by current liabilities for the same quarter last year.'} +{'id': 60946, 'data_set_name': '可以使用:anl14_low_ebitda_fy2', 'description': '不可使用,仅供参考:The Lowest Estimation of Earnings Before Interest, Taxes, Depreciation & Amortization - Upcoming 2 Years'} +{'id': 60820, 'data_set_name': '可以使用:anl14_high_nav_fy1', 'description': '不可使用,仅供参考:The Highest Estimation of Net Asset Value - upcoming year'} +{'id': 62092, 'data_set_name': '可以使用:quarterly_long_term_debt_to_equity_ratio', 'description': '不可使用,仅供参考:Long-term debt divided by equity for the most recent quarter.'} +{'id': 140156, 'data_set_name': '可以使用:fnd31_qsg5additionalfactor3_monthly_apg', 'description': '不可使用,仅供参考:TTM Gross Profit to Assets. It is defined as the TTM (Sales - COGS)/Total Assets.'} +{'id': 140542, 'data_set_name': '可以使用:fnd72_pit_or_bs_q_minority_noncontrolling_interest', 'description': '不可使用,仅供参考:Minority Interest'} +{'id': 373351, 'data_set_name': '可以使用:pv173_zscoresbondreturn20deqwt', 'description': '不可使用,仅供参考:It is defined as the equally weighted single bond return over last 20 days with the filter of the bonds that mature between 3 years and 7 years.'} +{'id': 137494, 'data_set_name': '可以使用:current_maturities_short_term', 'description': '不可使用,仅供参考:Current maturities of short-term debt obligations.'} +{'id': 139038, 'data_set_name': '可以使用:share_based_deferred_tax', 'description': '不可使用,仅供参考:Deferred tax liabilities related to share-based compensation.'} +{'id': 60634, 'data_set_name': '可以使用:anl11_esgreg_industryperc', 'description': '不可使用,仅供参考:Industry Percentile on ESG Score with 100% being the best'} +{'id': 139126, 'data_set_name': '可以使用:total_assets_4', 'description': '不可使用,仅供参考:Sum of all assets owned by the company.'} +{'id': 373381, 'data_set_name': '可以使用:industry_grouping_level20_top2000', 'description': '不可使用,仅供参考:Industry classification group at level 20 for the top 2000 instruments.'} +{'id': 140165, 'data_set_name': '可以使用:fnd31_qsg5additionalfactor3_monthly_pbdwf', 'description': '不可使用,仅供参考:Leading 12-Month Book Yield. It is defined as the average quarterly forward-looking book value per share divided by close price.'} +{'id': 140849, 'data_set_name': '可以使用:fnd72_s_pit_or_cf_q_cf_cash_from_inv_act', 'description': '不可使用,仅供参考:Cash From Investing Activities'} +{'id': 136525, 'data_set_name': '可以使用:annual_book_value_per_share_2', 'description': '不可使用,仅供参考:Book value per share for the most recent fiscal year.'} +{'id': 140926, 'data_set_name': '可以使用:fnd86_fundamental_score', 'description': '不可使用,仅供参考:Fundamental score'} +{'id': 138952, 'data_set_name': '可以使用:long_term_trade_debt_2', 'description': '不可使用,仅供参考:Long-term trade debt outstanding at period end.'} +{'id': 61034, 'data_set_name': '可以使用:anl14_low_roe_fp2', 'description': '不可使用,仅供参考:The lowest estimation of returns on equity - upcoming 2 quarters'} +{'id': 137388, 'data_set_name': '可以使用:reporting_to_pricing_fx_rate', 'description': '不可使用,仅供参考:Exchange rate used to convert reporting currency to pricing currency.'} +{'id': 62080, 'data_set_name': '可以使用:quarterly_long_term_debt_3', 'description': '不可使用,仅供参考:Sum of all long-term debt for the most recent quarter.'} +{'id': 137512, 'data_set_name': '可以使用:dividend_per_share_declared', 'description': '不可使用,仅供参考:Dividend per share declared for the period.'} +{'id': 136527, 'data_set_name': '可以使用:annual_cash_per_share_value', 'description': '不可使用,仅供参考:Cash and short-term investments per share for the most recent fiscal year.'} +{'id': 138977, 'data_set_name': '可以使用:other_assets_purchase_balance', 'description': '不可使用,仅供参考:represents changes in accounts payable during the period. An increase inaccounts payable has a positive impact on cash flow from operating activities, and vice versa.'} +{'id': 139046, 'data_set_name': '可以使用:share_dividend_equity', 'description': '不可使用,仅供参考:Share dividend equity reported for the annual period.'} +{'id': 60540, 'data_set_name': '可以使用:anl11_citreg_industryperc', 'description': '不可使用,仅供参考:Industry Percentile on CIT Score with 100% being the best'} +{'id': 62136, 'data_set_name': '可以使用:quarterly_price_to_sales_ratio_3', 'description': '不可使用,仅供参考:Price divided by sales per share for the quarter.'} +{'id': 61001, 'data_set_name': '可以使用:anl14_low_ntprep_fy5', 'description': '不可使用,仅供参考:The lowest estimation of reported net profit - upcoming 5 years'} +{'id': 140935, 'data_set_name': '可以使用:srp_earnings_score', 'description': '不可使用,仅供参考:Earnings score'} +{'id': 60876, 'data_set_name': '可以使用:anl14_high_revenue_fy3', 'description': '不可使用,仅供参考:The highest estimation of revenue - upcoming 3 years'} +{'id': 137331, 'data_set_name': '可以使用:pricing_currency_code_emi1', 'description': '不可使用,仅供参考:Currency code for price reporting in this module.'} +{'id': 60998, 'data_set_name': '可以使用:anl14_low_ntprep_fy2', 'description': '不可使用,仅供参考:The lowest estimation of reported net profit - upcoming 2 years'} +{'id': 136947, 'data_set_name': '可以使用:fnd17_alldelay1_ttmpelow', 'description': '不可使用,仅供参考:P/E excluding extraordinary items low, trailing 12 months'} +{'id': 140444, 'data_set_name': '可以使用:fnd72_pit_or_bs_a_bs_other_cur_asset', 'description': '不可使用,仅供参考:Other Current Assets'} +{'id': 60994, 'data_set_name': '可以使用:anl14_low_ntprep_fp3', 'description': '不可使用,仅供参考:The lowest estimation of reported net profit - upcoming 3 quarters'} +{'id': 140558, 'data_set_name': '可以使用:fnd72_pit_or_cf_a_cf_form_of_statement', 'description': '不可使用,仅供参考:No longer supported'} +{'id': 62094, 'data_set_name': '可以使用:quarterly_long_term_debt_to_total_capital', 'description': '不可使用,仅供参考:Ratio of long-term debt to total capital for the quarter.'} +{'id': 62386, 'data_set_name': '可以使用:anl45_index_period_start_prc', 'description': '不可使用,仅供参考:Benchmark/Index start price based on the performance period you chose'} +{'id': 137364, 'data_set_name': '可以使用:quarterly_revenue_per_share_4', 'description': '不可使用,仅供参考:Revenue per share for the most recent quarter (annualized).'} +{'id': 140921, 'data_set_name': '可以使用:fnd86_average_score', 'description': '不可使用,仅供参考:Average score'} +{'id': 61000, 'data_set_name': '可以使用:anl14_low_ntprep_fy4', 'description': '不可使用,仅供参考:The lowest estimation of Reported Net Profit - upcoming 4 years'} +{'id': 373336, 'data_set_name': '可以使用:pv173_rawratiosmt5yzspreadzscore60d', 'description': '不可使用,仅供参考:It is defined as the 60-day z-score of 5-year mid z-spreadIn the bond z-spread curve.'} +{'id': 140663, 'data_set_name': '可以使用:fnd72_pit_or_is_a_is_trading_acct_prof', 'description': '不可使用,仅供参考:Trading Acct'} +{'id': 62172, 'data_set_name': '可以使用:quarterly_sga_to_revenue_ratio', 'description': '不可使用,仅供参考:Selling, general, and administrative expenses as a percentage of revenue for the quarter.'} +{'id': 139344, 'data_set_name': '可以使用:fnd28_cfsourceusea_value_04301a', 'description': '不可使用,仅供参考:value of annual field: Proceeds From Stock Options'} +{'id': 139087, 'data_set_name': '可以使用:short_term_long_term_liabilities', 'description': '不可使用,仅供参考:Short-term portion of long-term liabilities.'} +{'id': 373610, 'data_set_name': '可以使用:pv37_volume_global', 'description': '不可使用,仅供参考:Global Trading Volume'} +{'id': 137453, 'data_set_name': '可以使用:accumulated_provision_depreciation_assets', 'description': '不可使用,仅供参考:[Quarterly] Provision for Doubtful Accounts'} +{'id': 140797, 'data_set_name': '可以使用:fnd72_s_pit_or_bs_q_bs_allow_doubtful_acc_rec', 'description': '不可使用,仅供参考:A contra asset account that is subtracted from trade and notes receivables on the balance sheet'} +{'id': 139102, 'data_set_name': '可以使用:short_term_payables_other', 'description': '不可使用,仅供参考:[Quarterly] Total Plan Obligations'} +{'id': 140426, 'data_set_name': '可以使用:fnd72_pit_or_bs_a_bs_common_stock', 'description': '不可使用,仅供参考:Common Stocks'} +{'id': 140131, 'data_set_name': '可以使用:fnd31_devnorthamericaadditionalfactor4_psdwf', 'description': '不可使用,仅供参考:Leading 12 Month Sales Yield. It is defined as the FY1 estimate of Sales value per share divided by its trading price.'} +{'id': 137473, 'data_set_name': '可以使用:assets_total_2', 'description': '不可使用,仅供参考:Total assets held by the company.'} +{'id': 136618, 'data_set_name': '可以使用:fnd17_acurratio', 'description': '不可使用,仅供参考:Current ratio - most recent fiscal year'} +{'id': 139172, 'data_set_name': '可以使用:voting_shares_granted_awards', 'description': '不可使用,仅供参考:Number of voting shares granted as awards.'} +{'id': 139082, 'data_set_name': '可以使用:short_term_loans_advances', 'description': '不可使用,仅供参考:Short-term loans and advances provided by the company.'} +{'id': 60593, 'data_set_name': '可以使用:anl11_emp2reg_industryrnk', 'description': '不可使用,仅供参考:Industry Rank on the second EMP score with 1 as highest'} +{'id': 140119, 'data_set_name': '可以使用:fnd31_devnorthamericaadditionalfactor4_apgghcy3', 'description': '不可使用,仅供参考:3-Year Change in Gross Profit to Assets. It is defined as the 3-Year on Year Change in Gross Profit to Assets.'} +{'id': 60964, 'data_set_name': '可以使用:anl14_low_epsrep_fp5', 'description': '不可使用,仅供参考:The Lowest Estimation of Reported Earnings Per Share - upcoming 5 quarters'} +{'id': 373398, 'data_set_name': '可以使用:pca_industry_grouping_method1_2', 'description': '不可使用,仅供参考:Industry grouping using first method and 2 clusters for top 3000 equities.'} +{'id': 136795, 'data_set_name': '可以使用:fnd17_alldelay1_nlow', 'description': '不可使用,仅供参考:Price - 12 month low'} +{'id': 140800, 'data_set_name': '可以使用:fnd72_s_pit_or_bs_q_bs_capital_stock', 'description': '不可使用,仅供参考:Capital Stock: When a company issues shares with par value for cash, a part of the proceeds are recorded as capital stock'} +{'id': 62001, 'data_set_name': '可以使用:quarterly_cash_flow_per_share_nonannualized', 'description': '不可使用,仅供参考:Cash flow per share for the quarter, not annualized.'} +{'id': 60789, 'data_set_name': '可以使用:anl14_high_ebit_fy5', 'description': '不可使用,仅供参考:The Highest Estimation of Earnings Before Interest & Taxes - upcoming 5 years'} +{'id': 140139, 'data_set_name': '可以使用:fnd31_devnorthamericaadditionalfactor4_yoychgroepct', 'description': '不可使用,仅供参考:Yearly Change in ROE - Percentage Method. It is defined as the yearly percentage change in the ratio of trailing-twelve-month earnings before extraordinary items divided by current total book equity.'} +{'id': 138949, 'data_set_name': '可以使用:long_term_tax_payable_2', 'description': '不可使用,仅供参考:Long-term tax liabilities due beyond one year.'} +{'id': 62376, 'data_set_name': '可以使用:anl45_bias_weighted_ret', 'description': '不可使用,仅供参考:Non-Functional'} +{'id': 60802, 'data_set_name': '可以使用:anl14_high_eps_fp3', 'description': '不可使用,仅供参考:The Highest estimation of Earnings Per Share - upcoming 3 quarters'} +{'id': 60910, 'data_set_name': '可以使用:anl14_low_capex_fy4', 'description': '不可使用,仅供参考:The Lowest Estimation of Capital Expenditures - upcoming 4 years'} +{'id': 61023, 'data_set_name': '可以使用:anl14_low_revenue_fp5', 'description': '不可使用,仅供参考:The lowest estimation of revenue - upcoming 5 quarters'} +{'id': 60890, 'data_set_name': '可以使用:anl14_high_roe_fy4', 'description': '不可使用,仅供参考:The Highest Estimation of Returns on Equity - upcoming 4 years'} +{'id': 373437, 'data_set_name': '可以使用:resampled_pca_factor_7', 'description': '不可使用,仅供参考:Seventh principal component factor derived from robust resampling of returns for top 2000 securities.'} +{'id': 136558, 'data_set_name': '可以使用:daily_volume_to_shares_outstanding', 'description': '不可使用,仅供参考:Average daily trading volume as a percentage of shares outstanding.'} +{'id': 373273, 'data_set_name': '可以使用:pv132_high_52week', 'description': '不可使用,仅供参考:52-Week High'} +{'id': 60845, 'data_set_name': '可以使用:anl14_high_ntprep_fp4', 'description': '不可使用,仅供参考:The Highest Estimation of Reported Net Profit - upcoming 4 quarters'} +{'id': 140850, 'data_set_name': '可以使用:fnd72_s_pit_or_cf_q_cf_cash_from_oper', 'description': '不可使用,仅供参考:Total amount of cash a company generates from its operation'} +{'id': 60841, 'data_set_name': '可以使用:anl14_high_ntp_fy5', 'description': '不可使用,仅供参考:The Highest Estimation of Net Profit - Upcoming 5 Years'} +{'id': 60776, 'data_set_name': '可以使用:anl14_high_div_fy2', 'description': '不可使用,仅供参考:The Highest Estimation of Dividend - Upcoming 2 Years'} +{'id': 373387, 'data_set_name': '可以使用:industry_grouping_level50_top2000', 'description': '不可使用,仅供参考:Industry classification group at level 50 for the top 2000 instruments.'} +{'id': 62054, 'data_set_name': '可以使用:quarterly_free_cash_flow_per_share', 'description': '不可使用,仅供参考:Free cash flow per share for the latest quarter.'} +{'id': 373587, 'data_set_name': '可以使用:pv37_low_global2h', 'description': '不可使用,仅供参考:Global Lowest Price 2H'} +{'id': 62124, 'data_set_name': '可以使用:quarterly_pe_ratio_high', 'description': '不可使用,仅供参考:Highest price-to-earnings ratio for the most recent quarter.'} +{'id': 140802, 'data_set_name': '可以使用:fnd72_s_pit_or_bs_q_bs_common_stock', 'description': '不可使用,仅供参考:Common Stocks'} +{'id': 62227, 'data_set_name': '可以使用:ttm_return_on_equity_percent', 'description': '不可使用,仅供参考:Net income divided by average equity for trailing twelve months, as a percentage.'} +{'id': 140934, 'data_set_name': '可以使用:srp_average_score', 'description': '不可使用,仅供参考:Average score'} +{'id': 139348, 'data_set_name': '可以使用:fnd28_cfsourceusea_value_04401a', 'description': '不可使用,仅供参考:value of annual field: Long Term Borrowings'} +{'id': 139158, 'data_set_name': '可以使用:value_investment_assets_total', 'description': '不可使用,仅供参考:Total value of investment assets held.'} +{'id': 62035, 'data_set_name': '可以使用:quarterly_dividend_per_share_3', 'description': '不可使用,仅供参考:Dividend paid per share for the most recent quarter.'} +{'id': 60988, 'data_set_name': '可以使用:anl14_low_ntp_fy2', 'description': '不可使用,仅供参考:The Lowest Estimation of Net Profit - Upcoming 2 Years'} +{'id': 373377, 'data_set_name': '可以使用:industry_grouping_level10_top1500', 'description': '不可使用,仅供参考:Industry classification at level 10 for the top 1500 instruments.'} +{'id': 373438, 'data_set_name': '可以使用:resampled_pca_factor_8', 'description': '不可使用,仅供参考:Eighth principal component factor derived from robust resampling of returns for top 2000 securities.'} +{'id': 60834, 'data_set_name': '可以使用:anl14_high_ntp_fp3', 'description': '不可使用,仅供参考:The Highest Estimation of Net Profit - upcoming 3 quarters'} +{'id': 60969, 'data_set_name': '可以使用:anl14_low_epsrep_fy5', 'description': '不可使用,仅供参考:The Lowest Estimation of Reported Earnings Per Share - upcoming 5 years'} +{'id': 61009, 'data_set_name': '可以使用:anl14_low_ptp_fy1', 'description': '不可使用,仅供参考:The lowest estimation of pretax profit - upcoming year'} +{'id': 137401, 'data_set_name': '可以使用:total_assets_annual_2', 'description': '不可使用,仅供参考:Total assets reported for the most recent fiscal year.'} +{'id': 60535, 'data_set_name': '可以使用:anl11_cit_ttcrg_industryperc', 'description': '不可使用,仅供参考:Industry Percentile on CIT Hybrid Score with 100% being the best'} +{'id': 60819, 'data_set_name': '可以使用:anl14_high_epsrep_fy5', 'description': '不可使用,仅供参考:The highest estimation of reported earnings per share - upcoming 5 years'} +{'id': 62085, 'data_set_name': '可以使用:quarterly_long_term_debt_to_assets_2', 'description': '不可使用,仅供参考:Long-term debt divided by total assets for the quarter.'} +{'id': 137347, 'data_set_name': '可以使用:quarterly_cash_per_share_amt', 'description': '不可使用,仅供参考:Cash and short-term investments per share for the most recent quarter.'} +{'id': 60538, 'data_set_name': '可以使用:anl11_citposcorreg_industryrnk', 'description': '不可使用,仅供参考:Industry Rank on CIT Max Correlation Score with 1 being the highest rank'} +{'id': 140547, 'data_set_name': '可以使用:fnd72_pit_or_cf_a_cf_cash_from_fnc_act', 'description': '不可使用,仅供参考:Cash from Financing Activities'} +{'id': 373537, 'data_set_name': '可以使用:top2000_factor3_group10_score', 'description': '不可使用,仅供参考:Third factor score for top 2000 securities, grouped into 10 clusters.'} +{'id': 60745, 'data_set_name': '可以使用:anl14_high_bvps_fp4', 'description': '不可使用,仅供参考:The highest estimation of book value per share - upcoming 4 quarters'} +{'id': 62081, 'data_set_name': '可以使用:quarterly_long_term_debt_per_share', 'description': '不可使用,仅供参考:Long-term debt divided by shares outstanding for the quarter.'} +{'id': 139358, 'data_set_name': '可以使用:fnd28_cfsourceusea_value_04760a', 'description': '不可使用,仅供参考:value of annual field: Increase in Investments'} +{'id': 139336, 'data_set_name': '可以使用:fnd28_cfsourceusea_value_04049a', 'description': '不可使用,仅供参考:value of annual field: Depreciation and Depletion'} +{'id': 137433, 'data_set_name': '可以使用:accounts_receivable_current_assets', 'description': '不可使用,仅供参考:Accounts receivable included in current assets for the annual period.'} +{'id': 137352, 'data_set_name': '可以使用:quarterly_debt_to_equity_ratio_2', 'description': '不可使用,仅供参考:Ratio of total debt to total equity for the most recent quarter.'} +{'id': 60955, 'data_set_name': '可以使用:anl14_low_eps_fy1', 'description': '不可使用,仅供参考:The lowest estimation of earnings per share - upcoming year'} +{'id': 373356, 'data_set_name': '可以使用:pv173_zscoresmt5yzspreadchg60d', 'description': '不可使用,仅供参考:It is defined as the 60-day change of 5-year mid z-spreadIn the bond z-spread curve.'} +{'id': 60996, 'data_set_name': '可以使用:anl14_low_ntprep_fp5', 'description': '不可使用,仅供参考:The Lowest Estimation of Reported Net Profit - upcoming 5 quarters'} +{'id': 373365, 'data_set_name': '可以使用:pv173_zscoresreltermspreadtransfema120d', 'description': '不可使用,仅供参考:It is defined as the 120-day exponential average of transformed relative term spread.'} +{'id': 138916, 'data_set_name': '可以使用:investment_securities_financial_assets', 'description': '不可使用,仅供参考:Sale of assets to be leased out under operating lease contracts.'} +{'id': 136706, 'data_set_name': '可以使用:fnd17_alldelay1_arevstrt', 'description': '不可使用,仅供参考:Reinvestment Rate - most recent fiscal year'} +{'id': 60993, 'data_set_name': '可以使用:anl14_low_ntprep_fp2', 'description': '不可使用,仅供参考:The Lowest Estimation of Reported Net Profit - upcoming 2 quarters'} +{'id': 60794, 'data_set_name': '可以使用:anl14_high_ebitda_fp5', 'description': '不可使用,仅供参考:The Highest Estimation of Earnings Before Interest, Taxes, Depreciation & Amortization - upcoming 5 quarters'} +{'id': 138905, 'data_set_name': '可以使用:interest_income_nonperforming_2', 'description': '不可使用,仅供参考:Interest income from nonperforming assets.'} +{'id': 137537, 'data_set_name': '可以使用:finance_lease_debt_total', 'description': '不可使用,仅供参考:Total finance lease debt outstanding.'} +{'id': 139127, 'data_set_name': '可以使用:total_assets_annual_atot', 'description': '不可使用,仅供参考:Total assets reported for the annual period.'} +{'id': 137446, 'data_set_name': '可以使用:accrued_interest_long_term', 'description': '不可使用,仅供参考:Interest accrued on long-term obligations.'} +{'id': 373410, 'data_set_name': '可以使用:pca_industry_grouping_method3_5', 'description': '不可使用,仅供参考:Industry grouping using third method and 5 clusters for top 3000 equities.'} +{'id': 62138, 'data_set_name': '可以使用:quarterly_quick_ratio_2', 'description': '不可使用,仅供参考:Quick ratio for the most recent quarter.'} +{'id': 140753, 'data_set_name': '可以使用:fnd72_s_pit_or_bs_q_1_bs_sh_cap_and_apic', 'description': '不可使用,仅供参考:Share Capital & APIC'} +{'id': 139338, 'data_set_name': '可以使用:fnd28_cfsourceusea_value_04051a', 'description': '不可使用,仅供参考:value of annual field: Depreciation, Depletion & Amortization'} +{'id': 373434, 'data_set_name': '可以使用:resampled_pca_factor_4', 'description': '不可使用,仅供参考:Fourth principal component factor derived from robust resampling of returns for top 2000 securities.'} +{'id': 373658, 'data_set_name': '可以使用:pv72_pv_score_float', 'description': '不可使用,仅供参考:pv_score'} +{'id': 373585, 'data_set_name': '可以使用:pv37_low_global1h', 'description': '不可使用,仅供参考:Global Lowest Price 1H'} +{'id': 138936, 'data_set_name': '可以使用:long_term_liabilities_total_2', 'description': '不可使用,仅供参考:Total long-term liabilities as of the reporting date.'} +{'id': 61974, 'data_set_name': '可以使用:price_to_cash_flow_per_share_quarterly', 'description': '不可使用,仅供参考:Ratio of share price to cash flow per share for the most recent quarter.'} +{'id': 138983, 'data_set_name': '可以使用:other_interest_liabilities_2', 'description': '不可使用,仅供参考:represents changes in inventories during the period. An increase in inventories has a negative impact on cash flow from operating activities, and vice versa.'} +{'id': 61002, 'data_set_name': '可以使用:anl14_low_opp_fp1', 'description': '不可使用,仅供参考:The Lowest estimation of Operating Profit - upcoming quarter'} +{'id': 140816, 'data_set_name': '可以使用:fnd72_s_pit_or_bs_q_bs_other_asset_def_chng_other', 'description': '不可使用,仅供参考:Other Assets/Def Change'} +{'id': 373572, 'data_set_name': '可以使用:pv37_high_global2', 'description': '不可使用,仅供参考:Global Highest Price 2'} +{'id': 60966, 'data_set_name': '可以使用:anl14_low_epsrep_fy2', 'description': '不可使用,仅供参考:The Lowest Estimation of Reported Earnings Per Share - upcoming 2 years'} +{'id': 60850, 'data_set_name': '可以使用:anl14_high_ntprep_fy4', 'description': '不可使用,仅供参考:The highest estimation of Reported Net Profit - upcoming 4 years'} +{'id': 60717, 'data_set_name': '可以使用:social_totalcorr_industry_percentile', 'description': '不可使用,仅供参考:Percentile ranking within industry peer group for social score weighted by total correlation to financial returns.'} +{'id': 138926, 'data_set_name': '可以使用:long_term_contract_liabilities', 'description': '不可使用,仅供参考:Long-term contract liabilities reported for the annual period.'} +{'id': 136546, 'data_set_name': '可以使用:annual_total_shareholder_equity', 'description': '不可使用,仅供参考:Total shareholder equity for the most recent fiscal year.'} +{'id': 140924, 'data_set_name': '可以使用:fnd86_earnings_score', 'description': '不可使用,仅供参考:Earnings score'} +{'id': 60768, 'data_set_name': '可以使用:anl14_high_cfps_fy3', 'description': '不可使用,仅供参考:The Highest Estimation of Cash Flow Per Share - Upcoming 3 Years'} +{'id': 139077, 'data_set_name': '可以使用:short_term_investment_cash_flow', 'description': '不可使用,仅供参考:Other Investing Cash Flow Items, Total represents all items reported within the investing activities in the cash flow statement, other than capital expenditures.'} +{'id': 136830, 'data_set_name': '可以使用:fnd17_alldelay1_qcurratio', 'description': '不可使用,仅供参考:Current ratio - most recent quarter'} +{'id': 140172, 'data_set_name': '可以使用:fnd31_qsg5additionalfactor3_monthly_slope4qeps5y', 'description': '不可使用,仅供参考:Slope of 5-Year TTM EPS Trend Line. It is defined as the slope coefficient between monthly dates and the corresponding trailing 12-month earnings per share before extra items in the prior 20 quarters.'} +{'id': 61026, 'data_set_name': '可以使用:anl14_low_revenue_fy3', 'description': '不可使用,仅供参考:The Lowest Estimation of Revenue - upcoming 3 years'} +{'id': 60752, 'data_set_name': '可以使用:anl14_high_capex_fp1', 'description': '不可使用,仅供参考:The highest estimation of capital expenditures - upcoming quarter'} +{'id': 60972, 'data_set_name': '可以使用:anl14_low_nav_fy3', 'description': '不可使用,仅供参考:The Lowest Estimation of Net Asset Value - upcoming 3 years'} +{'id': 60809, 'data_set_name': '可以使用:anl14_high_eps_fy5', 'description': '不可使用,仅供参考:The highest estimation of earnings per share - upcoming 5 years'} +{'id': 373390, 'data_set_name': '可以使用:industry_grouping_level5_top2000', 'description': '不可使用,仅供参考:Industry classification group at level 5 for the top 2000 instruments.'} +{'id': 373331, 'data_set_name': '可以使用:pv173_rawratiosmt5yzspreadchgstd20d', 'description': '不可使用,仅供参考:It is defined as the 20-day standard deviation of changeIn 5-year mid z-spreadIn the bond z-spread curve.'} +{'id': 140153, 'data_set_name': '可以使用:fnd31_ohlsonscore', 'description': "不可使用,仅供参考:Ohlson Bankruptcy Score. It is a model assessing a company's probability of bankruptcy by considering firm size, capital structure, financial performance, and liquidity. It is derived from Ohlson (1980)."} +{'id': 138915, 'data_set_name': '可以使用:investment_income_cash_flow', 'description': '不可使用,仅供参考:Cash used for discontinued operations classified as Other Investing Cash Flow'} +{'id': 373309, 'data_set_name': '可以使用:pv173_ranksmt5yzspreadzscore60d', 'description': '不可使用,仅供参考:It is defined as the 60-day z-score of 5-year mid z-spreadIn the bond z-spread curve.'} +{'id': 373430, 'data_set_name': '可以使用:resampled_pca_factor_13', 'description': '不可使用,仅供参考:Thirteenth principal component factor derived from robust resampling of returns for top 2000 securities.'} +{'id': 60848, 'data_set_name': '可以使用:anl14_high_ntprep_fy2', 'description': '不可使用,仅供参考:The highest estimation of reported net profit - upcoming 2 years'} +{'id': 61031, 'data_set_name': '可以使用:anl14_low_roa_fy3', 'description': '不可使用,仅供参考:The lowest estimation of returns on assets - upcoming 3 years'} +{'id': 140079, 'data_set_name': '可以使用:fnd28_wcannuala_value_08651', 'description': '不可使用,仅供参考:value of field: Reinvestment Rate - per Share'} +{'id': 140438, 'data_set_name': '可以使用:fnd72_pit_or_bs_a_bs_mkt_sec_other_st_invest', 'description': '不可使用,仅供参考:Marketable Securities & Other ST Investments'} +{'id': 136808, 'data_set_name': '可以使用:fnd17_alldelay1_pr5dayprc', 'description': '不可使用,仅供参考:Price - 5 Day % Change'} +{'id': 60704, 'data_set_name': '可以使用:social_poscorr_industry_position', 'description': '不可使用,仅供参考:Company’s position within industry peer group for social score weighted by positive correlation to financial returns.'} +{'id': 137334, 'data_set_name': '可以使用:pricing_currency_code_ras3', 'description': '不可使用,仅供参考:Currency code for price reporting in this module.'} +{'id': 373413, 'data_set_name': '可以使用:pca_industry_grouping_method4_2', 'description': '不可使用,仅供参考:Industry grouping using fourth method and 2 clusters for top 3000 equities.'} +{'id': 137337, 'data_set_name': '可以使用:pricing_currency_code_ras6', 'description': '不可使用,仅供参考:Currency code for price reporting in this module.'} +{'id': 61019, 'data_set_name': '可以使用:anl14_low_revenue_fp1', 'description': '不可使用,仅供参考:The lowest estimation of revenue - upcoming quarter'} +{'id': 139151, 'data_set_name': '可以使用:valuation_investment_assets_total', 'description': '不可使用,仅供参考:Total valuation allowance for investment assets.'} +{'id': 373376, 'data_set_name': '可以使用:pv173_zscorestwist6m1yavg120dema120d', 'description': '不可使用,仅供参考:It is defined as the 120-day exponential average of average factor loading of the third principal component for the 6-month and 1-year z-spread'} +{'id': 62186, 'data_set_name': '可以使用:quarterly_total_assets_change_percent', 'description': '不可使用,仅供参考:Percent change in total assets compared to the same quarter last year.'} +{'id': 140157, 'data_set_name': '可以使用:fnd31_qsg5additionalfactor3_monthly_apgghcy1', 'description': '不可使用,仅供参考:1-Year Change in Gross Profit to Assets. It is defined as the Year on Year Change in Gross Profit to Assets.'} +{'id': 137547, 'data_set_name': '可以使用:financing_proceeds_share_sale', 'description': '不可使用,仅供参考:If the sale, issuance or repurchase of common and preferred stock is not separately delineated, then the total is classified as Issuance (Retirement) of Stock, Net.'} +{'id': 137459, 'data_set_name': '可以使用:amortization_of_acquired_assets', 'description': '不可使用,仅供参考:Amortization expense related to acquired assets during the period.'} +{'id': 60836, 'data_set_name': '可以使用:anl14_high_ntp_fp5', 'description': '不可使用,仅供参考:The highest estimation of Net Profit - upcoming 5 quarters'} +{'id': 140590, 'data_set_name': '可以使用:fnd72_pit_or_cf_q_cf_form_of_statement', 'description': '不可使用,仅供参考:No longer supported'} +{'id': 373646, 'data_set_name': '可以使用:pv72_ibesptg2_score_float', 'description': '不可使用,仅供参考:pv_score'} +{'id': 373367, 'data_set_name': '可以使用:pv173_zscoresshift60dchg60d', 'description': '不可使用,仅供参考:It is defined as the 60-day change of ShiftIn Bond Z-spread Curve.'} +{'id': 60774, 'data_set_name': '可以使用:anl14_high_div_fp5', 'description': '不可使用,仅供参考:The Highest estimation of Dividend - upcoming 5 quarters'} +{'id': 137281, 'data_set_name': '可以使用:fnd17_ttmpehigh', 'description': '不可使用,仅供参考:P/E excluding extraordinary items high, trailing 12 months'} +{'id': 373388, 'data_set_name': '可以使用:industry_grouping_level50_top3000_2', 'description': '不可使用,仅供参考:Industry classification group at level 50 for the top 3000 instruments.'} +{'id': 61022, 'data_set_name': '可以使用:anl14_low_revenue_fp4', 'description': '不可使用,仅供参考:The Lowest Estimation of Revenue - upcoming 4 quarters'} +{'id': 138901, 'data_set_name': '可以使用:intangible_software_assets', 'description': '不可使用,仅供参考:Intangible software assets reported in interim financials.'} +{'id': 62060, 'data_set_name': '可以使用:quarterly_free_cash_flow_per_share_non_annualized_2', 'description': '不可使用,仅供参考:Free cash flow per share for the most recent quarter, not annualized.'} +{'id': 61024, 'data_set_name': '可以使用:anl14_low_revenue_fy1', 'description': '不可使用,仅供参考:The Lowest Estimation of Revenue - upcoming year'} +{'id': 140819, 'data_set_name': '可以使用:fnd72_s_pit_or_bs_q_bs_other_cur_liab', 'description': '不可使用,仅供参考:Includes customer deposits, short-term deferred income tax liabilities, refunds due customers, and other current liabilities'} +{'id': 136438, 'data_set_name': '可以使用:matched_share_award_value', 'description': '不可使用,仅供参考:Value of shares awarded plus matched percentage for a given plan.'} +{'id': 60925, 'data_set_name': '可以使用:anl14_low_div_fy1', 'description': '不可使用,仅供参考:The lowest estimation of dividend - upcoming year'} +{'id': 137393, 'data_set_name': '可以使用:revenue_per_share_quarterly', 'description': '不可使用,仅供参考:Revenue per share for the most recent quarter, annualized.'} +{'id': 136455, 'data_set_name': '可以使用:total_other_listed_boards_count_2', 'description': '不可使用,仅供参考:Total number of other listed boards served on by directors (alternate).'} +{'id': 60589, 'data_set_name': '可以使用:anl11_emp1reg_industryrnk', 'description': '不可使用,仅供参考:Industry Rank on the first EMP score with 1 as highest'} +{'id': 62169, 'data_set_name': '可以使用:quarterly_revenue_per_share_3', 'description': '不可使用,仅供参考:Total revenue divided by average shares for the quarter.'} +{'id': 62157, 'data_set_name': '可以使用:quarterly_return_on_average_equity', 'description': '不可使用,仅供参考:Annualized net income divided by average equity for the quarter.'} +{'id': 136549, 'data_set_name': '可以使用:book_value_per_share_quarterly', 'description': '不可使用,仅供参考:Book value per share for the most recent quarter.'} +{'id': 136436, 'data_set_name': '可以使用:matched_share_award_quantity', 'description': '不可使用,仅供参考:Number of shares awarded plus matched percentage in a plan.'} +{'id': 139055, 'data_set_name': '可以使用:short_term_bank_advances_investments', 'description': '不可使用,仅供参考:Short-term bank advances and investments.'} +{'id': 60714, 'data_set_name': '可以使用:social_score_subsector_percentile', 'description': '不可使用,仅供参考:Percentile ranking within subsector peer group for social score.'} +{'id': 373358, 'data_set_name': '可以使用:pv173_zscoresmt5yzspreadchgstd20d', 'description': '不可使用,仅供参考:It is defined as the 20-day standard deviation of changeIn 5-year mid z-spreadIn the bond z-spread curve.'} +{'id': 138643, 'data_set_name': '可以使用:fnd23_intfvmalld1_dare', 'description': '不可使用,仅供参考:Research and development costs expensed directly in the income statement.'} +{'id': 60843, 'data_set_name': '可以使用:anl14_high_ntprep_fp2', 'description': '不可使用,仅供参考:The Highest Estimation of Reported Net Profit - Upcoming 2 Quarters'} +{'id': 373339, 'data_set_name': '可以使用:pv173_rawratiosreltermspreadtransfema120dsbst', 'description': '不可使用,仅供参考:It is defined as the 120-day exponential average of transformed relative term spread'} +{'id': 137513, 'data_set_name': '可以使用:dividends_noncontrolling_interest', 'description': '不可使用,仅供参考:Dividends paid to noncontrolling interest holders during the period.'} +{'id': 140697, 'data_set_name': '可以使用:fnd72_pit_or_is_q_is_expense_stock_based_comp', 'description': '不可使用,仅供参考:The expense for stock-based compensation'} +{'id': 139054, 'data_set_name': '可以使用:short_term_bank_acceptances_issued', 'description': '不可使用,仅供参考:Short-term bank acceptances issued during the period.'} +{'id': 60932, 'data_set_name': '可以使用:anl14_low_ebit_fp3', 'description': '不可使用,仅供参考:The Lowest Estimation of Earnings Before Interest & Taxes - Upcoming 3 Quarters'} +{'id': 60806, 'data_set_name': '可以使用:anl14_high_eps_fy2', 'description': '不可使用,仅供参考:The Highest Estimation of Earnings Per Share - upcoming 2 years'} +{'id': 373648, 'data_set_name': '可以使用:pv72_ibesptg_score', 'description': '不可使用,仅供参考:pv_score'} +{'id': 60607, 'data_set_name': '可以使用:anl11_empposcorreg_industryperc', 'description': '不可使用,仅供参考:Industry Percentile on EMP Max Correlation Score with 100% being the best'} +{'id': 140744, 'data_set_name': '可以使用:fnd72_s_pit_or_bs_q_1_bs_mkt_sec_other_st_invest', 'description': '不可使用,仅供参考:Marketable Securities & Other ST Investments'} +{'id': 60947, 'data_set_name': '可以使用:anl14_low_ebitda_fy3', 'description': '不可使用,仅供参考:The Lowest Estimation of Earnings Before Interest, Taxes, Depreciation & Amortization - upcoming 3 years'} +{'id': 139092, 'data_set_name': '可以使用:short_term_non_current_cash', 'description': '不可使用,仅供参考:Short-term cash classified as non-current.'} +{'id': 373590, 'data_set_name': '可以使用:pv37_low_global4h', 'description': '不可使用,仅供参考:Global Lowest Price 4H'} +{'id': 62141, 'data_set_name': '可以使用:quarterly_quick_ratio_prior', 'description': '不可使用,仅供参考:Quick ratio for the same quarter last year.'} +{'id': 136999, 'data_set_name': '可以使用:fnd17_arev', 'description': '不可使用,仅供参考:Revenue - most recent fiscal year'} +{'id': 62195, 'data_set_name': '可以使用:quarterly_total_debt_to_assets_ratio', 'description': '不可使用,仅供参考:Total debt divided by total assets for the most recent quarter.'} +{'id': 137332, 'data_set_name': '可以使用:pricing_currency_code_ras1', 'description': '不可使用,仅供参考:Currency code for price reporting in this module.'} +{'id': 140711, 'data_set_name': '可以使用:fnd72_pit_or_is_q_is_other_adj_comp_inc', 'description': '不可使用,仅供参考:Other Adjustments - Other Comprehensive Income'} +{'id': 140076, 'data_set_name': '可以使用:fnd28_wcannuala_value_08311', 'description': '不可使用,仅供参考:value of field: Cash Flow/Sales'} +{'id': 373660, 'data_set_name': '可以使用:pv96_dvd_declared_dt', 'description': '不可使用,仅供参考:The date which a company announces a dividend distribution'} +{'id': 139121, 'data_set_name': '可以使用:stock_dividends_payable_recorded', 'description': '不可使用,仅供参考:Stock dividends declared but not yet paid.'} +{'id': 140170, 'data_set_name': '可以使用:fnd31_qsg5additionalfactor3_monthly_psdwf', 'description': '不可使用,仅供参考:Leading 12 Month Sales Yield. It is defined as the FY1 estimate of Sales value per share divided by its trading price.'} +{'id': 140138, 'data_set_name': '可以使用:fnd31_devnorthamericaadditionalfactor4_yoychgroeart', 'description': '不可使用,仅供参考:Yearly Change in ROE - Arithmetic Difference. It is defined as the yearly arithmetic change in the ratio of trailing-twelve-month earnings before extraordinary items divided by current total book equity.'} +{'id': 140162, 'data_set_name': '可以使用:fnd31_qsg5additionalfactor3_monthly_mpg', 'description': '不可使用,仅供参考:TTM Gross Profit Margin. It is defined as the TTM (Sales-COGS)/Sales.'} +{'id': 138950, 'data_set_name': '可以使用:long_term_tax_payable_4', 'description': '不可使用,仅供参考:Long-term tax payable reported in interim financials.'} +{'id': 62118, 'data_set_name': '可以使用:quarterly_non_annualized_revenue_per_share', 'description': '不可使用,仅供参考:Revenue per share for the quarter, not annualized.'} +{'id': 373403, 'data_set_name': '可以使用:pca_industry_grouping_method2_2', 'description': '不可使用,仅供参考:Industry grouping using second method and 2 clusters for top 3000 equities.'} +{'id': 60985, 'data_set_name': '可以使用:anl14_low_ntp_fp4', 'description': '不可使用,仅供参考:The lowest estimation of net profit - upcoming 4 quarters'} +{'id': 140190, 'data_set_name': '可以使用:fnd44_fscore', 'description': '不可使用,仅供参考:A model intent on predicting accounting misstatements. The results of the model show the accounting metrics most indicative of misconduct. These accounting metrics can be classified into six broad categories: accruals, financial performance, market-related, nonfinancial, off balance sheet, and market incentives. Note that there are three F-score models. The F-Score is estimated over a longer period than the M-Score. But it shows challenging outof-sample performance in the US marke'} +{'id': 373374, 'data_set_name': '可以使用:pv173_zscorestilt6m1yavg60dlast', 'description': '不可使用,仅供参考:It is defined as the average factor loading of the second principal component for the 6-month and 1-year z-spread'} +{'id': 138998, 'data_set_name': '可以使用:other_operating_liabilities_balance', 'description': '不可使用,仅供参考:other liabilities'} +{'id': 139066, 'data_set_name': '可以使用:short_term_creditors', 'description': '不可使用,仅供参考:Amount of short-term creditors at the reporting date.'} +{'id': 137399, 'data_set_name': '可以使用:tangible_book_value_per_share_quarterly', 'description': '不可使用,仅供参考:Tangible book value per share for the most recent quarter.'} +{'id': 373325, 'data_set_name': '可以使用:pv173_rawratioseqbddivrgbdrtn120d', 'description': '不可使用,仅供参考:It is defined as the difference of market equity return and estimated equity return based on 120-day linear regression of equity return on aggregated bond return and MSCI ACWIIndex return.'} +{'id': 136569, 'data_set_name': '可以使用:fifty_day_average_price', 'description': '不可使用,仅供参考:The average closing price over the last 50 trading days.'} +{'id': 62022, 'data_set_name': '可以使用:quarterly_current_ratio_2', 'description': '不可使用,仅供参考:Current assets divided by current liabilities for the most recent quarter.'} +{'id': 139094, 'data_set_name': '可以使用:short_term_noncurrent_investments', 'description': '不可使用,仅供参考:Short-term portion of noncurrent investments.'} +{'id': 137455, 'data_set_name': '可以使用:allowance_for_doubtful_accounts', 'description': '不可使用,仅供参考:[Quarterly] Advertising Expense, Supplemental'} +{'id': 136449, 'data_set_name': '可以使用:share_award_value_2', 'description': '不可使用,仅供参考:Value of shares awarded to the director during the period (alternate).'} +{'id': 138929, 'data_set_name': '可以使用:long_term_current_liabilities_4', 'description': '不可使用,仅供参考:Long-term current liabilities reported in interim financials.'} +{'id': 60751, 'data_set_name': '可以使用:anl14_high_bvps_fy5', 'description': '不可使用,仅供参考:The Highest Estimation of Book Value Per Share - upcoming 5 years'} +{'id': 373642, 'data_set_name': '可以使用:pv72_ibeseps_score', 'description': '不可使用,仅供参考:pv_score'} +{'id': 62392, 'data_set_name': '可以使用:anl45_net_market_exposure', 'description': '不可使用,仅供参考:Difference in fractional total value between long and short ideas'} +{'id': 140858, 'data_set_name': '可以使用:fnd72_s_pit_or_cf_q_cf_form_of_statement', 'description': '不可使用,仅供参考:No longer supported'} +{'id': 136547, 'data_set_name': '可以使用:annual_total_shareholders_equity', 'description': "不可使用,仅供参考:Total shareholders' equity for the most recent fiscal year."} +{'id': 62387, 'data_set_name': '可以使用:anl45_index_ret_per', 'description': '不可使用,仅供参考:Return % had the investment been made in an index'} +{'id': 139343, 'data_set_name': '可以使用:fnd28_cfsourceusea_value_04251a', 'description': '不可使用,仅供参考:value of annual field: Net Proceeds from Sale/Issuance of Common & Preferred'} +{'id': 137471, 'data_set_name': '可以使用:annual_short_term_notes_current', 'description': '不可使用,仅供参考:Annual short-term notes currently outstanding.'} +{'id': 373332, 'data_set_name': '可以使用:pv173_rawratiosmt5yzspreadchgstd20dsbst', 'description': '不可使用,仅供参考:It is defined as the 20-day standard deviation of changeIn 5-year mid z-spreadIn the bond z-spread curve'} +{'id': 140166, 'data_set_name': '可以使用:fnd31_qsg5additionalfactor3_monthly_pbwt', 'description': '不可使用,仅供参考:Time Weighted Book Yield. It is defined as the time-weighted average of book value per share estimates in fiscal year 1 and fiscal year 2 divided by close price.'} +{'id': 136843, 'data_set_name': '可以使用:fnd17_alldelay1_qpayratio', 'description': '不可使用,仅供参考:Payout ratio - most recent quarter'} +{'id': 139162, 'data_set_name': '可以使用:voting_common_shares_outstanding', 'description': '不可使用,仅供参考:Number of voting common shares currently outstanding.'} +{'id': 60749, 'data_set_name': '可以使用:anl14_high_bvps_fy3', 'description': '不可使用,仅供参考:The highest estimation of Book Value Per Share - upcoming 3 years'} +{'id': 137151, 'data_set_name': '可以使用:fnd17_qpelow', 'description': '不可使用,仅供参考:P/E excluding extraordinary items low, most recent quarter'} +{'id': 137365, 'data_set_name': '可以使用:quarterly_revenue_per_share_nonannualized_2', 'description': '不可使用,仅供参考:Revenue per share for the most recent quarter (not annualized).'} +{'id': 140735, 'data_set_name': '可以使用:fnd72_s_pit_or_bs_q_1_bs_amt_of_tsy_stock', 'description': '不可使用,仅供参考:Amount of Treasury Shares'} +{'id': 60747, 'data_set_name': '可以使用:anl14_high_bvps_fy1', 'description': '不可使用,仅供参考:The highest estimation of Book Value Per Share - upcoming year'} +{'id': 60597, 'data_set_name': '可以使用:anl11_emp3reg_industryrnk', 'description': '不可使用,仅供参考:Industry Rank on the third EMP score with 1 as highest'} +{'id': 139083, 'data_set_name': '可以使用:short_term_loans_advances_quarterly', 'description': '不可使用,仅供参考:Short-term loans and advances provided by the company for the quarter.'} +{'id': 136702, 'data_set_name': '可以使用:fnd17_alldelay1_arev', 'description': '不可使用,仅供参考:Revenue - most recent fiscal year'} +{'id': 60974, 'data_set_name': '可以使用:anl14_low_ndebt_fp2', 'description': '不可使用,仅供参考:The Lowest Estimation of Net Debt - Upcoming 2 Quarters'} +{'id': 138889, 'data_set_name': '可以使用:goat_accumulated_other_comprehensive_income', 'description': '不可使用,仅供参考:Accumulated other comprehensive income for GOAT at the reporting date.'} +{'id': 137376, 'data_set_name': '可以使用:rep_to_pricing_fx_rate_emi1', 'description': '不可使用,仅供参考:Exchange rate used to convert reporting currency to pricing currency for this module.'} +{'id': 136543, 'data_set_name': '可以使用:annual_total_assets_value', 'description': '不可使用,仅供参考:Total assets for the most recent fiscal year.'} +{'id': 60773, 'data_set_name': '可以使用:anl14_high_div_fp4', 'description': '不可使用,仅供参考:The highest estimation of dividend - upcoming 4 quarters'} +{'id': 140600, 'data_set_name': '可以使用:fnd72_pit_or_cf_q_cf_other_inv_act', 'description': '不可使用,仅供参考:Other Investing Activities'} +{'id': 60979, 'data_set_name': '可以使用:anl14_low_ndebt_fy3', 'description': '不可使用,仅供参考:The lowest estimation of net debt - upcoming 3 years'} +{'id': 140868, 'data_set_name': '可以使用:fnd72_s_pit_or_cf_q_cf_other_non_cash_adjust', 'description': '不可使用,仅供参考:Other Non-Cash Adjustments'} +{'id': 60761, 'data_set_name': '可以使用:anl14_high_capex_fy5', 'description': '不可使用,仅供参考:The Highest Estimation of Capital Expenditures - upcoming 5 years'} +{'id': 139095, 'data_set_name': '可以使用:short_term_notes_current_2', 'description': '不可使用,仅供参考:Short-term notes currently outstanding.'} +{'id': 60619, 'data_set_name': '可以使用:anl11_ereg_industryperc', 'description': '不可使用,仅供参考:Industry Percentile on E Score with 100% being the best'} +{'id': 60606, 'data_set_name': '可以使用:anl11_emp_ttcrg_industryrnk', 'description': '不可使用,仅供参考:Industry Rank on EMP Hybrid Score with 1 being the highest rank'} +{'id': 373539, 'data_set_name': '可以使用:top2000_factor3_group2_score', 'description': '不可使用,仅供参考:Third factor score for top 2000 securities, grouped into 2 clusters.'} +{'id': 62185, 'data_set_name': '可以使用:quarterly_total_assets', 'description': '不可使用,仅供参考:Sum of all assets reported for the most recent quarter.'} +{'id': 139152, 'data_set_name': '可以使用:valuation_market_adjustment_amount', 'description': '不可使用,仅供参考:Amount of market adjustment made to valuation.'} +{'id': 373368, 'data_set_name': '可以使用:pv173_zscoresshift60dlast', 'description': '不可使用,仅供参考:It is defined as the average factor loading of the first principal component for the z-spread between 6 months and 40 years'} +{'id': 373534, 'data_set_name': '可以使用:top2000_factor2_group2_score', 'description': '不可使用,仅供参考:Second factor score for top 2000 securities, grouped into 2 clusters.'} +{'id': 137348, 'data_set_name': '可以使用:quarterly_cash_per_share_value', 'description': '不可使用,仅供参考:Cash and short-term investments per share for the most recent quarter.'} +{'id': 140509, 'data_set_name': '可以使用:fnd72_pit_or_bs_q_bs_other_cur_asset_less_prepay', 'description': '不可使用,仅供参考:Other assets which are not accounts and notes receivable, inventory, derivative assets, deferred tax assets, prepaid expenses, assets held for sale, income taxes receivable, and assets of discontinued operations'} +{'id': 138988, 'data_set_name': '可以使用:other_long_term_obligations_7', 'description': '不可使用,仅供参考:includes diverse cash flows that are reported outside of the operating, investing and financing sections of the cash flow statement.'} +{'id': 60855, 'data_set_name': '可以使用:anl14_high_opp_fy3', 'description': '不可使用,仅供参考:The highest estimation of operating profit - upcoming 3 years'} +{'id': 373428, 'data_set_name': '可以使用:resampled_pca_factor_11', 'description': '不可使用,仅供参考:Eleventh principal component factor derived from robust resampling of returns for top 2000 securities.'} +{'id': 373194, 'data_set_name': '可以使用:returns', 'description': '不可使用,仅供参考:Daily returns'} +{'id': 139098, 'data_set_name': '可以使用:short_term_notes_issued_net', 'description': '不可使用,仅供参考:Net amount of short-term notes issued during the period.'} +{'id': 373364, 'data_set_name': '可以使用:pv173_zscoresmt5yzspreadzscore60dsbst', 'description': '不可使用,仅供参考:It is defined as the 60-day z-score of 5-year mid z-spreadIn the bond z-spread curve'} +{'id': 140160, 'data_set_name': '可以使用:fnd31_qsg5additionalfactor3_monthly_earningstorpedo', 'description': '不可使用,仅供参考:Earnings Torpedo. It is defined as Next 4 Quarter EPS Estimate + TTM Real Earnings Surprise/TTM EPS where, TTM Earnings Surprise = Actual EPS - Est EPS, summed over the last 4 quarters.'} +{'id': 62155, 'data_set_name': '可以使用:quarterly_return_on_assets_percent_2', 'description': '不可使用,仅供参考:Annualized net income divided by average assets for the quarter, as a percentage.'} +{'id': 140197, 'data_set_name': '可以使用:fnd44_score', 'description': "不可使用,仅供参考:Based on the various accounting models, we develop an overall score indicative of a company's accounting quality. This is an equally weighted score."} +{'id': 138978, 'data_set_name': '可以使用:other_borrowings_total', 'description': '不可使用,仅供参考:Total other borrowings reported for the annual period.'} +{'id': 139058, 'data_set_name': '可以使用:short_term_bank_investment_total', 'description': '不可使用,仅供参考:Total short-term bank investments at period end.'} +{'id': 373573, 'data_set_name': '可以使用:pv37_high_global2h', 'description': '不可使用,仅供参考:Global Highest Price 2H'} +{'id': 373220, 'data_set_name': '可以使用:customer_revenue_share_percent', 'description': '不可使用,仅供参考:Percentage of company revenue attributed to a specific customer.'} +{'id': 137150, 'data_set_name': '可以使用:fnd17_qpehigh', 'description': '不可使用,仅供参考:P/E excluding extraordinary items high, most recent quarter'} +{'id': 373400, 'data_set_name': '可以使用:pca_industry_grouping_method1_5', 'description': '不可使用,仅供参考:Industry grouping using first method and 5 clusters for top 3000 equities.'} +{'id': 60823, 'data_set_name': '可以使用:anl14_high_ndebt_fp1', 'description': '不可使用,仅供参考:The Highest Estimation of Net Debt - upcoming quarter'} +{'id': 140446, 'data_set_name': '可以使用:fnd72_pit_or_bs_a_bs_other_cur_liab', 'description': '不可使用,仅供参考:Includes customer deposits, short-term deferred income tax liabilities, refunds due customers, and other current liabilities'} +{'id': 373412, 'data_set_name': '可以使用:pca_industry_grouping_method4_10', 'description': '不可使用,仅供参考:Industry grouping using fourth method and 10 clusters for top 3000 equities.'} +{'id': 60828, 'data_set_name': '可以使用:anl14_high_ndebt_fy2', 'description': '不可使用,仅供参考:The highest estimation of Net Debt - upcoming 2 years'} +{'id': 62125, 'data_set_name': '可以使用:quarterly_pe_ratio_high_2', 'description': '不可使用,仅供参考:Highest price-to-earnings ratio for the quarter.'} +{'id': 139100, 'data_set_name': '可以使用:short_term_other_current_assets_2', 'description': '不可使用,仅供参考:Short-term other current assets reported in interim financials.'} +{'id': 60967, 'data_set_name': '可以使用:anl14_low_epsrep_fy3', 'description': '不可使用,仅供参考:The lowest estimation of Reported Earnings Per Share - upcoming 3 years'} +{'id': 60956, 'data_set_name': '可以使用:anl14_low_eps_fy2', 'description': '不可使用,仅供参考:The lowest estimation of earnings per share - upcoming 2 years'} +{'id': 60771, 'data_set_name': '可以使用:anl14_high_div_fp2', 'description': '不可使用,仅供参考:The highest estimation of dividend - upcoming 2 quarters'} +{'id': 138948, 'data_set_name': '可以使用:long_term_secured_debt', 'description': '不可使用,仅供参考:Long-term secured debt outstanding at period end.'} +{'id': 373401, 'data_set_name': '可以使用:pca_industry_grouping_method1_50', 'description': '不可使用,仅供参考:Industry grouping using first method and 50 clusters for top 3000 equities.'} +{'id': 62082, 'data_set_name': '可以使用:quarterly_long_term_debt_per_share_2', 'description': '不可使用,仅供参考:Long-term debt per share for the most recent quarter.'} +{'id': 373652, 'data_set_name': '可以使用:pv72_ibesrec2_score_float', 'description': '不可使用,仅供参考:pv_score'} +{'id': 60827, 'data_set_name': '可以使用:anl14_high_ndebt_fy1', 'description': '不可使用,仅供参考:The Highest Estimation of Net Debt - upcoming year'} +{'id': 139078, 'data_set_name': '可以使用:short_term_investment_cashflow', 'description': '不可使用,仅供参考:Cash flow from short-term investment activities.'} +{'id': 62189, 'data_set_name': '可以使用:quarterly_total_assets_db', 'description': '不可使用,仅供参考:Total assets for the most recent quarter (alternate source).'} +{'id': 139370, 'data_set_name': '可以使用:fnd28_cfsourceusea_value_04840a', 'description': '不可使用,仅供参考:value of annual field: Effect of Exchange Rate on Cash'} +{'id': 373584, 'data_set_name': '可以使用:pv37_low_global', 'description': '不可使用,仅供参考:Global Lowest Price'} +{'id': 137369, 'data_set_name': '可以使用:quarterly_total_assets_3', 'description': '不可使用,仅供参考:Total assets reported for the most recent quarter.'} +{'id': 61975, 'data_set_name': '可以使用:quarter_total_assets', 'description': '不可使用,仅供参考:Total assets reported for the most recent quarter.'} +{'id': 60990, 'data_set_name': '可以使用:anl14_low_ntp_fy4', 'description': '不可使用,仅供参考:The lowest estimation of net profit - upcoming 4 years'} +{'id': 373649, 'data_set_name': '可以使用:pv72_ibesptg_score_float', 'description': '不可使用,仅供参考:pv_score'} +{'id': 62126, 'data_set_name': '可以使用:quarterly_pe_ratio_low', 'description': '不可使用,仅供参考:Lowest price-to-earnings ratio for the most recent quarter.'} +{'id': 140176, 'data_set_name': '可以使用:fnd31_qsg5additionalfactor3_monthly_y5speq4vc', 'description': '不可使用,仅供参考:Stability of 5-yr TTM Earnings per Share. It is defined as the standard deviation of the last 20-Month trailing 12-month earnings per share (EPS) divided by the mean of these EPSs.'} +{'id': 62123, 'data_set_name': '可以使用:quarterly_payout_ratio_2', 'description': '不可使用,仅供参考:Percentage of earnings paid as dividends for the quarter.'} +{'id': 60866, 'data_set_name': '可以使用:anl14_high_ptprep_fy3', 'description': '不可使用,仅供参考:The Highest Estimation of Reported Pretax Profit - upcoming 3 years'} +{'id': 60982, 'data_set_name': '可以使用:anl14_low_ntp_fp1', 'description': '不可使用,仅供参考:The lowest estimation of net profit - upcoming quarter'} +{'id': 373643, 'data_set_name': '可以使用:pv72_ibeseps_score_float', 'description': '不可使用,仅供参考:pv_score'} +{'id': 60896, 'data_set_name': '可以使用:anl14_low_bvps_fp5', 'description': '不可使用,仅供参考:The lowest estimation of Book Value Per Share - upcoming 5 quarters'} +{'id': 140891, 'data_set_name': '可以使用:fnd72_s_pit_or_is_q_is_eqy_earn_from_invest_assoc', 'description': '不可使用,仅供参考:Earnings from investments in associated companies, joint ventures, unconsolidated subsidiaries that are valued according to the equity method'} +{'id': 373433, 'data_set_name': '可以使用:resampled_pca_factor_3', 'description': '不可使用,仅供参考:Third principal component factor derived from robust resampling of returns for top 2000 securities.'} +{'id': 373407, 'data_set_name': '可以使用:pca_industry_grouping_method3_10', 'description': '不可使用,仅供参考:Industry grouping using third method and 10 clusters for top 3000 equities.'} +{'id': 140939, 'data_set_name': '可以使用:srp_relative_valuation_score', 'description': '不可使用,仅供参考:Relative valuation score'} +{'id': 373575, 'data_set_name': '可以使用:pv37_high_global3h', 'description': '不可使用,仅供参考:Global Highest Price 3H'} +{'id': 60527, 'data_set_name': '可以使用:anl11_cit3reg_industryrnk', 'description': '不可使用,仅供参考:Industry Rank on CIT3 1 being the highest rank'} +{'id': 140188, 'data_set_name': '可以使用:fnd44_cashflow_accruals', 'description': '不可使用,仅供参考:Cash Flow Accruals'} +{'id': 373349, 'data_set_name': '可以使用:pv173_rawratiostwist6m1yavg120dema120d', 'description': '不可使用,仅供参考:It is defined as the 120-day exponential average of average factor loading of the third principal component for the 6-month and 1-year z-spread'} +{'id': 60880, 'data_set_name': '可以使用:anl14_high_roa_fy2', 'description': '不可使用,仅供参考:The Highest Estimation of Returns on Assets - upcoming 2 years'} +{'id': 60913, 'data_set_name': '可以使用:anl14_low_cfps_fp2', 'description': '不可使用,仅供参考:The lowest estimation of Cash Flow Per Share - upcoming 2 quarters'} +{'id': 62480, 'data_set_name': '可以使用:anl69_best_pe_ratio', 'description': '不可使用,仅供参考:P/E Ratio'} +{'id': 136456, 'data_set_name': '可以使用:total_remuneration_period', 'description': '不可使用,仅供参考:Total remuneration awarded to the director for the period.'} +{'id': 61021, 'data_set_name': '可以使用:anl14_low_revenue_fp3', 'description': '不可使用,仅供参考:The lowest estimation of revenue - upcoming 3 quarters'} +{'id': 137340, 'data_set_name': '可以使用:pricing_currency_code_rasv2ff', 'description': '不可使用,仅供参考:Currency code for price reporting in this module.'} +{'id': 139072, 'data_set_name': '可以使用:short_term_deposits', 'description': '不可使用,仅供参考:depreciation.'} +{'id': 139120, 'data_set_name': '可以使用:statement_of_cash_flows_2', 'description': '不可使用,仅供参考:Statement of cash flows for the period.'} +{'id': 62000, 'data_set_name': '可以使用:quarterly_cash_flow_per_share_non_annualized_alt', 'description': '不可使用,仅供参考:Alternate calculation of cash flow per share for the quarter, not annualized.'} +{'id': 139093, 'data_set_name': '可以使用:short_term_non_current_investments', 'description': '不可使用,仅供参考:Short-term investments that are classified as non-current.'} +{'id': 60975, 'data_set_name': '可以使用:anl14_low_ndebt_fp3', 'description': '不可使用,仅供参考:The Lowest Estimation of Net Debt - Upcoming 3 Quarters'} +{'id': 60812, 'data_set_name': '可以使用:anl14_high_epsrep_fp3', 'description': '不可使用,仅供参考:The Highest Estimation of Reported Earnings Per Share - upcoming 3 quarters'} +{'id': 140749, 'data_set_name': '可以使用:fnd72_s_pit_or_bs_q_1_bs_other_st_liab', 'description': '不可使用,仅供参考:Other ST Liabilities'} +{'id': 138890, 'data_set_name': '可以使用:goat_interest_income_nonperforming', 'description': '不可使用,仅供参考:Interest income from nonperforming assets for GOAT.'} +{'id': 61997, 'data_set_name': '可以使用:quarterly_cash_flow_per_share_alt', 'description': '不可使用,仅供参考:Alternative calculation of cash flow per share for the quarter.'} +{'id': 140127, 'data_set_name': '可以使用:fnd31_devnorthamericaadditionalfactor4_pbwt', 'description': '不可使用,仅供参考:Time Weighted Book Yield. It is defined as the time-weighted average of book value per share estimates in fiscal year 1 and fiscal year 2 divided by close price.'} +{'id': 60931, 'data_set_name': '可以使用:anl14_low_ebit_fp2', 'description': '不可使用,仅供参考:The Lowest estimation of Earnings Before Interest & Taxes - upcoming 2 quarters'} +{'id': 136705, 'data_set_name': '可以使用:fnd17_alldelay1_arevps', 'description': '不可使用,仅供参考:Revenue/share - most recent fiscal year'} +{'id': 137411, 'data_set_name': '可以使用:ttm_total_assets_value', 'description': '不可使用,仅供参考:Total assets for the trailing twelve months.'} +{'id': 60643, 'data_set_name': '可以使用:anl11_g2reg_industryperc', 'description': '不可使用,仅供参考:Industry Percentile on G2 100% being the best'} +{'id': 60536, 'data_set_name': '可以使用:anl11_cit_ttcrg_industryrnk', 'description': '不可使用,仅供参考:Industry Rank on CIT Hybrid Score with 1 being the highest rank'} +{'id': 60926, 'data_set_name': '可以使用:anl14_low_div_fy2', 'description': '不可使用,仅供参考:The Lowest Estimation of Dividend - Upcoming 2 Years'} +{'id': 373189, 'data_set_name': '可以使用:industry', 'description': '不可使用,仅供参考:Industry grouping'} +{'id': 136381, 'data_set_name': '可以使用:current_other_board_total', 'description': '不可使用,仅供参考:Number of other boards the director currently serves on.'} +{'id': 62213, 'data_set_name': '可以使用:quarterly_working_capital_per_share_to_price_db', 'description': '不可使用,仅供参考:Working capital per share divided by price for the quarter (alternate source).'} +{'id': 60897, 'data_set_name': '可以使用:anl14_low_bvps_fy1', 'description': '不可使用,仅供参考:The Lowest Estimation of Book Value Per Share - Upcoming Year'} +{'id': 61973, 'data_set_name': '可以使用:lowest_pe_ratio_quarterly', 'description': '不可使用,仅供参考:Lowest price-to-earnings ratio for the most recent quarter.'} +{'id': 137442, 'data_set_name': '可以使用:accrued_assets_total', 'description': '不可使用,仅供参考:Total accrued assets as of the reporting date.'} +{'id': 61005, 'data_set_name': '可以使用:anl14_low_opp_fy3', 'description': '不可使用,仅供参考:The Lowest Estimation of Operating Profit - Upcoming 3 Years'} +{'id': 136437, 'data_set_name': '可以使用:matched_share_award_quantity_ltip', 'description': '不可使用,仅供参考:Number of shares awarded plus matched percentage in a plan for LTIP compensation.'} +{'id': 138882, 'data_set_name': '可以使用:free_firm_cash_flow', 'description': '不可使用,仅供参考:other financing cash flow'} +{'id': 373609, 'data_set_name': '可以使用:pv37_volume_15', 'description': '不可使用,仅供参考:Trading volume'} +{'id': 137098, 'data_set_name': '可以使用:fnd17_nlow', 'description': '不可使用,仅供参考:Price - 12 month low'} +{'id': 139099, 'data_set_name': '可以使用:short_term_notes_payable', 'description': '不可使用,仅供参考:Short-term notes payable outstanding at period end.'} +{'id': 373522, 'data_set_name': '可以使用:top1500_pca_factor2_grouping20', 'description': '不可使用,仅供参考:Second principal component grouping for top 1500 securities with 20 clusters.'} +{'id': 60917, 'data_set_name': '可以使用:anl14_low_cfps_fy2', 'description': '不可使用,仅供参考:The Lowest Estimation of Cash Flow Per Share - upcoming 2 years'} +{'id': 136806, 'data_set_name': '可以使用:fnd17_alldelay1_pr1dayprc', 'description': '不可使用,仅供参考:Price - 1 Day % Change'} +{'id': 60999, 'data_set_name': '可以使用:anl14_low_ntprep_fy3', 'description': '不可使用,仅供参考:The lowest estimation of reported net profit - upcoming 3 years'} +{'id': 137107, 'data_set_name': '可以使用:fnd17_pelow', 'description': '不可使用,仅供参考:P/E excluding extraordinary items, Low'} +{'id': 62091, 'data_set_name': '可以使用:quarterly_long_term_debt_to_equity_prior_year', 'description': '不可使用,仅供参考:Long-term debt divided by shareholder equity for the same quarter last year.'} +{'id': 140177, 'data_set_name': '可以使用:fnd31_qsg5additionalfactor3_monthly_yoychgroeart', 'description': '不可使用,仅供参考:Yearly Change in ROE - Arithmetic Difference. It is defined as the yearly arithmetic change in the ratio of trailing-twelve-month earnings before extraordinary items divided by current total book equity.'} +{'id': 62405, 'data_set_name': '可以使用:anl45_rel_index_ret_per', 'description': '不可使用,仅供参考:Difference in return % between making investment in idea and comparative index'} +{'id': 60522, 'data_set_name': '可以使用:anl11_cit2reg_industryperc', 'description': '不可使用,仅供参考:Industry Percentile on CIT2 100% being the best'} +{'id': 138904, 'data_set_name': '可以使用:interest_income_cashflow', 'description': '不可使用,仅供参考:Cash flow from interest income activities.'} +{'id': 61016, 'data_set_name': '可以使用:anl14_low_ptprep_fy3', 'description': '不可使用,仅供参考:The lowest estimation of reported pretax profit - upcoming 3 years'} +{'id': 139075, 'data_set_name': '可以使用:short_term_interest_expense_other_2', 'description': '不可使用,仅供参考:Other short-term interest expenses.'} +{'id': 136691, 'data_set_name': '可以使用:fnd17_alldelay1_apayratio2', 'description': '不可使用,仅供参考:Payout ratio - most recent fiscal year -1'} +{'id': 60586, 'data_set_name': '可以使用:anl11_e_ttcrg_industryperc', 'description': '不可使用,仅供参考:Industry Percentile on E Hybrid Score with 100% being the best'} +{'id': 140631, 'data_set_name': '可以使用:fnd72_pit_or_is_a_is_expected_return_pension', 'description': '不可使用,仅供参考:The component of net pension expense that pertains to the expected return on pension plan assets'} +{'id': 373608, 'data_set_name': '可以使用:pv37_volume_14', 'description': '不可使用,仅供参考:14-Day Trading Volume'} +{'id': 61967, 'data_set_name': '可以使用:debt_to_equity_ratio_quarterly_prior_year', 'description': '不可使用,仅供参考:Ratio of total debt to total equity for the same quarter one year ago.'} +{'id': 140848, 'data_set_name': '可以使用:fnd72_s_pit_or_cf_q_cf_cash_from_fnc_act', 'description': '不可使用,仅供参考:Cash from Financing Activities'} +{'id': 140569, 'data_set_name': '可以使用:fnd72_pit_or_cf_a_cf_other_inv_act', 'description': '不可使用,仅供参考:Other Investing Activities'} +{'id': 136998, 'data_set_name': '可以使用:fnd17_arecvbl', 'description': '不可使用,仅供参考:Receivables - most recent fiscal year'} +{'id': 60790, 'data_set_name': '可以使用:anl14_high_ebitda_fp1', 'description': '不可使用,仅供参考:The highest estimation of earnings before interest, taxes, depreciation & amortization - upcoming quarter'} +{'id': 140798, 'data_set_name': '可以使用:fnd72_s_pit_or_bs_q_bs_amt_of_tsy_stock', 'description': '不可使用,仅供参考:Amount of Treasury Shares'} +{'id': 136383, 'data_set_name': '可以使用:director_attrition_ratio_2', 'description': '不可使用,仅供参考:Proportion of directors who have left a role in the preceding period (alternate).'} +{'id': 139101, 'data_set_name': '可以使用:short_term_other_operating_expenses', 'description': '不可使用,仅供参考:Short-term other operating expenses reported in interim financials.'} +{'id': 60891, 'data_set_name': '可以使用:anl14_high_roe_fy5', 'description': '不可使用,仅供参考:The Highest estimation of Returns on Equity - upcoming 5 years'} +{'id': 60942, 'data_set_name': '可以使用:anl14_low_ebitda_fp3', 'description': '不可使用,仅供参考:The Lowest Estimation of Earnings Before Interest, Taxes, Depreciation & Amortization - upcoming 3 quarters'} +========================= 数据字段结束 ======================================= + +以上数据字段和操作符, 按照Description说明组合, 但是每一个 alpha 组合的使用的数据字段和操作符不要过于集中, 在符合语法的情况下, 多尝试不同的组合 + +你再检查一下, 如果你使用了 +Operator abs does not support event inputs +Operator ts_mean does not support event inputs +Operator ts_scale does not support event inputs +Operator add does not support event inputs +Operator sign does not support event inputs +Operator greater does not support event inputs +Operator ts_av_diff does not support event inputs +Operator ts_quantile does not support event inputs +Operator ts_arg_min does not support event inputs +Operator divide does not support event inputs +Operator ts_corr does not support event inputs +Operator ts_decay_linear does not support event inputs +Operator ts_sum does not support event inputs +Operator ts_delay does not support event inputs +Operator ts_arg_max does not support event inputs +Operator ts_std_dev does not support event inputs +Operator ts_regression does not support event inputs +Operator ts_backfill does not support event inputs +Operator signed_power does not support event inputs +Operator ts_product does not support event inputs +Operator ts_zscore does not support event inputs +Operator group_rank does not support event inputs +Operator subtract does not support event inputs +Operator ts_delta does not support event inputs +Operator ts_rank does not support event inputs +Operator ts_count_nans does not support event inputs +Operator ts_covariance does not support event inputs +Operator multiply does not support event inputs +Operator if_else does not support event inputs +Operator group_neutralize does not support event inputs +Operator group_zscore does not support event inputs +Operator winsorize does not support event inputs +注意, 以上操作符不能使用事件类型的数据集, 以上操作符禁止使用事件类型的数据集!! \ No newline at end of file diff --git a/manual_prompt/2026/01/28/manual_prompt_20260128174445.txt b/manual_prompt/2026/01/28/manual_prompt_20260128174445.txt new file mode 100644 index 0000000..09fd9c4 --- /dev/null +++ b/manual_prompt/2026/01/28/manual_prompt_20260128174445.txt @@ -0,0 +1,817 @@ +研究因子名称 +盈利质量溢价因子 +假设 +高质量的盈利通常表现为持续性、可预测性和较低的应计成分，这类公司往往具备更强的经营现金流支撑和更低的财务操纵风险。根据行为金融学中的“代表性启发”偏差，市场投资者容易过度关注账面利润而忽视盈利构成的质量，导致高质量盈利公司被系统性低估，从而在中长期产生超额收益。 +实施方案 +基于财报数据，构建一个综合盈利质量指标：首先计算应计利润占总利润的比例（应计比率），再结合经营性现金流与净利润的比值、盈利波动率以及资产周转稳定性等维度进行标准化加权，最终形成横截面上的排序因子。该因子值越高，代表公司盈利质量越好，预期未来收益越高。 + +*=========================================================================================* +输出格式： +输出必须是且仅是纯文本。 +每一行是一个完整、独立、语法正确的WebSim表达式。 +严禁任何形式的解释、编号、标点包裹（如引号）、Markdown格式或额外文本。 +===================== !!! 重点(输出方式) !!! ===================== +现在，请严格遵守以上所有规则，开始生成可立即在WebSim中运行的复合因子表达式。 +不要自行假设, 你需要用到的操作符 和 数据集, 必须从我提供给你的里面查找, 并严格按照里面的使用方法进行组合 +**输出格式**(一行一个表达式, 每个表达式中间需要添加一个空行, 只要表达式本身, 不需要赋值, 不要解释, 不需要序号, 也不要输出多余的东西)： +表达式 +表达式 +表达式 +... +表达式 +================================================================= +重申：请确保所有表达式都使用WorldQuant WebSim平台函数，不要使用pandas、numpy或其他Python库函数。输出必须是一行有效的WQ表达式。 +以下是我的账号有权限使用的操作符, 请严格按照操作符, 以及我提供的数据集, 进行生成,组合 50 个 alpha： +不要自行假设, 你需要用到的操作符 和 数据集, 必须从我提供给你的里面查找, 并严格按照里面的使用方法进行组合 +!! 数据集使用要求尽量分散, 不要重复使用 +!! 数据集使用要求尽量分散, 不要重复使用 +================================================================= +**操作符汇总 +**算术运算符 (Arithmetic): +abs(x) - 绝对值 +add(x, y, filter=false) - 加法 (x + y) +densify(x) - 分组字段稠密化 +divide(x, y) - 除法 (x / y) +inverse(x) - 倒数 (1/x) +log(x) - 自然对数 +max(x, y, ..) - 最大值 +min(x, y, ..) - 最小值 +multiply(x, y, filter=false) - 乘法 (x * y) +power(x, y) - 幂运算 (x^y) +reverse(x) - 取反 (-x) +sign(x) - 符号函数 +signed_power(x, y) - 保留符号的幂运算 +sqrt(x) - 平方根 +subtract(x, y, filter=false) - 减法 (x - y) +to_nan(x, value=0, reverse=false) - 值与NaN转换 +**逻辑运算符 (Logical): +and(input1, input2) - 逻辑与 +if_else(input1, input2, input3) - 条件判断 +input1 < input2 - 小于比较 +input1 <= input2 - 小于等于 +input1 == input2 - 等于比较 +input1 > input2 - 大于比较 +input1 >= input2 - 大于等于 +input1 != input2 - 不等于 +is_nan(input) - 是否为NaN +not(x) - 逻辑非 +or(input1, input2) - 逻辑或 +**时间序列运算符 (Time Series): +days_from_last_change(x) - 上次变化天数 +hump(x, hump=0.01) - 限制变化幅度 +jump_decay(x, d, sensitivity=0.5, force=0.1) - 跳跃衰减 +kth_element(x, d, k) - 第K个值 +last_diff_value(x, d) - 最后一个不同值 +ts_arg_max(x, d) - 最大值相对索引 +ts_arg_min(x, d) - 最小值相对索引 +ts_av_diff(x, d) - 与均值的差 +ts_backfill(x, lookback=d, k=1, ignore="NAN") - 回填 +ts_corr(x, y, d) - 时间序列相关性 +ts_count_nans(x, d) - NaN计数 +ts_covariance(y, x, d) - 协方差 +ts_decay_linear(x, d, dense=false) - 线性衰减 +ts_delay(x, d) - 延迟值 +ts_delta(x, d) - 差值 (x - 延迟值) +ts_max(x, d) - 时间序列最大值 +ts_mean(x, d) - 时间序列均值 +ts_min(x, d) - 时间序列最小值 +ts_product(x, d) - 时间序列乘积 +ts_quantile(x, d, driver="gaussian") - 分位数 +ts_rank(x, d, constant=0) - 时间序列排名 +ts_regression(y, x, d, lag=0, rettype=0) - 回归分析 +ts_scale(x, d, constant=0) - 时间序列缩放 +ts_std_dev(x, d) - 时间序列标准差 +ts_step(1) - 天数计数器 +ts_sum(x, d) - 时间序列求和 +ts_target_tvr_decay(x, lambda_min=0, lambda_max=1, target_tvr=0.1) - 目标换手率衰减 +ts_target_tvr_delta_limit(x, y, lambda_min=0, lambda_max=1, target_tvr=0.1) - 目标换手率差值限制 +ts_zscore(x, d) - 时间序列Z分数 +**横截面运算符 (Cross Sectional): +normalize(x, useStd=false, limit=0.0) - 标准化 +quantile(x, driver=gaussian, sigma=1.0) - 分位数转换 +rank(x, rate=2) - 排名 +scale(x, scale=1, longscale=1, shortscale=1) - 缩放 +scale_down(x, constant=0) - 按比例缩放 +vector_neut(x, y) - 向量中性化 +winsorize(x, std=4) - 缩尾处理 +zscore(x) - Z分数 +**向量运算符 (Vector): +vec_avg(x) - 向量均值 +vec_max(x) - 向量最大值 +vec_min(x) - 向量最小值 +vec_sum(x) - 向量求和 +**变换运算符 (Transformational): +bucket(rank(x), range="0,1,0.1" or buckets="2,5,6,7,10") - 分桶 +generate_stats(alpha) - 生成统计量 +trade_when(x, y, z) - 条件交易 +**分组运算符 (Group): +combo_a(alpha, nlength=250, mode='algo1') - 组合Alpha +group_backfill(x, group, d, std=4.0) - 分组回填 +group_cartesian_product(g1, g2) - 笛卡尔积分组 +group_max(x, group) - 分组最大值 +group_mean(x, weight, group) - 分组均值 +group_min(x, group) - 分组最小值 +group_neutralize(x, group) - 分组中性化 +group_rank(x, group) - 分组排名 +group_scale(x, group) - 分组缩放 +group_zscore(x, group) - 分组Z分数 +**特殊运算符 (Special): +in - 包含判断 +self_corr(input) - 自相关性 +universe_size - 宇宙大小 +**归约运算符 (Reduce): +reduce_avg(input, threshold=0) - 平均值归约 +reduce_choose(input, nth, ignoreNan=true) - 选择归约 +reduce_count(input, threshold) - 计数归约 +reduce_ir(input) - IR归约 +reduce_kurtosis(input) - 峰度归约 +reduce_max(input) - 最大值归约 +reduce_min(input) - 最小值归约 +reduce_norm(input) - 范数归约 +reduce_percentage(input, percentage=0.5) - 百分比归约 +reduce_powersum(input, constant=2, precise=false) - 幂和归约 +reduce_range(input) - 范围归约 +reduce_skewness(input) - 偏度归约 +reduce_stddev(input, threshold=0) - 标准差归约 +reduce_sum(input) - 求和归约 + +以下是我的账号有权限使用的操作符, 请严格按照操作符, 进行生成,组合因子 + +========================= 操作符开始 ======================================= +注意: Operator: 后面的是操作符(是可以使用的), +Description: 此字段后面的是操作符对应的描述或使用说明(禁止使用, 仅供参考), Description字段后面的内容是使用说明, 不是操作符 +特别注意!!!! 必须按照操作符字段Operator的使用说明生成 alphaOperator: abs(x) +Description: Absolute value of x +Operator: add(x, y, filter = false) +Description: Add all inputs (at least 2 inputs required). If filter = true, filter all input NaN to 0 before adding +Operator: densify(x) +Description: Converts a grouping field of many buckets into lesser number of only available buckets so as to make working with grouping fields computationally efficient +Operator: divide(x, y) +Description: x / y +Operator: inverse(x) +Description: 1 / x +Operator: log(x) +Description: Natural logarithm. For example: Log(high/low) uses natural logarithm of high/low ratio as stock weights. +Operator: max(x, y, ..) +Description: Maximum value of all inputs. At least 2 inputs are required +Operator: min(x, y ..) +Description: Minimum value of all inputs. At least 2 inputs are required +Operator: multiply(x ,y, ... , filter=false) +Description: Multiply all inputs. At least 2 inputs are required. Filter sets the NaN values to 1 +Operator: power(x, y) +Description: x ^ y +Operator: reverse(x) +Description: - x +Operator: sign(x) +Description: if input > 0, return 1; if input < 0, return -1; if input = 0, return 0; if input = NaN, return NaN; +Operator: signed_power(x, y) +Description: x raised to the power of y such that final result preserves sign of x +Operator: sqrt(x) +Description: Square root of x +Operator: subtract(x, y, filter=false) +Description: x-y. If filter = true, filter all input NaN to 0 before subtracting +Operator: and(input1, input2) +Description: Logical AND operator, returns true if both operands are true and returns false otherwise +Operator: if_else(input1, input2, input 3) +Description: If input1 is true then return input2 else return input3. +Operator: input1 < input2 +Description: If input1 < input2 return true, else return false +Operator: input1 <= input2 +Description: Returns true if input1 <= input2, return false otherwise +Operator: input1 == input2 +Description: Returns true if both inputs are same and returns false otherwise +Operator: input1 > input2 +Description: Logic comparison operators to compares two inputs +Operator: input1 >= input2 +Description: Returns true if input1 >= input2, return false otherwise +Operator: input1!= input2 +Description: Returns true if both inputs are NOT the same and returns false otherwise +Operator: is_nan(input) +Description: If (input == NaN) return 1 else return 0 +Operator: not(x) +Description: Returns the logical negation of x. If x is true (1), it returns false (0), and if input is false (0), it returns true (1). +Operator: or(input1, input2) +Description: Logical OR operator returns true if either or both inputs are true and returns false otherwise +Operator: days_from_last_change(x) +Description: Amount of days since last change of x +Operator: hump(x, hump = 0.01) +Description: Limits amount and magnitude of changes in input (thus reducing turnover) +Operator: kth_element(x, d, k) +Description: Returns K-th value of input by looking through lookback days. This operator can be used to backfill missing data if k=1 +Operator: last_diff_value(x, d) +Description: Returns last x value not equal to current x value from last d days +Operator: ts_arg_max(x, d) +Description: Returns the relative index of the max value in the time series for the past d days. If the current day has the max value for the past d days, it returns 0. If previous day has the max value for the past d days, it returns 1 +Operator: ts_arg_min(x, d) +Description: Returns the relative index of the min value in the time series for the past d days; If the current day has the min value for the past d days, it returns 0; If previous day has the min value for the past d days, it returns 1. +Operator: ts_av_diff(x, d) +Description: Returns x - tsmean(x, d), but deals with NaNs carefully. That is NaNs are ignored during mean computation +Operator: ts_backfill(x,lookback = d, k=1, ignore="NAN") +Description: Backfill is the process of replacing the NAN or 0 values by a meaningful value (i.e., a first non-NaN value) +Operator: ts_corr(x, y, d) +Description: Returns correlation of x and y for the past d days +Operator: ts_count_nans(x ,d) +Description: Returns the number of NaN values in x for the past d days +Operator: ts_covariance(y, x, d) +Description: Returns covariance of y and x for the past d days +Operator: ts_decay_linear(x, d, dense = false) +Description: Returns the linear decay on x for the past d days. Dense parameter=false means operator works in sparse mode and we treat NaN as 0. In dense mode we do not. +Operator: ts_delay(x, d) +Description: Returns x value d days ago +Operator: ts_delta(x, d) +Description: Returns x - ts_delay(x, d) +Operator: ts_mean(x, d) +Description: Returns average value of x for the past d days. +Operator: ts_product(x, d) +Description: Returns product of x for the past d days +Operator: ts_quantile(x,d, driver="gaussian" ) +Description: It calculates ts_rank and apply to its value an inverse cumulative density function from driver distribution. Possible values of driver (optional ) are "gaussian", "uniform", "cauchy" distribution where "gaussian" is the default. +Operator: ts_rank(x, d, constant = 0) +Description: Rank the values of x for each instrument over the past d days, then return the rank of the current value + constant. If not specified, by default, constant = 0. +Operator: ts_regression(y, x, d, lag = 0, rettype = 0) +Description: Returns various parameters related to regression function +Operator: ts_scale(x, d, constant = 0) +Description: Returns (x - ts_min(x, d)) / (ts_max(x, d) - ts_min(x, d)) + constant. This operator is similar to scale down operator but acts in time series space +Operator: ts_std_dev(x, d) +Description: Returns standard deviation of x for the past d days +Operator: ts_step(1) +Description: Returns days' counter +Operator: ts_sum(x, d) +Description: Sum values of x for the past d days. +Operator: ts_zscore(x, d) +Description: Z-score is a numerical measurement that describes a value's relationship to the mean of a group of values. Z-score is measured in terms of standard deviations from the mean: (x - tsmean(x,d)) / tsstddev(x,d). This operator may help reduce outliers and drawdown. +Operator: normalize(x, useStd = false, limit = 0.0) +Description: Calculates the mean value of all valid alpha values for a certain date, then subtracts that mean from each element +Operator: quantile(x, driver = gaussian, sigma = 1.0) +Description: Rank the raw vector, shift the ranked Alpha vector, apply distribution (gaussian, cauchy, uniform). If driver is uniform, it simply subtract each Alpha value with the mean of all Alpha values in the Alpha vector +Operator: rank(x, rate=2) +Description: Ranks the input among all the instruments and returns an equally distributed number between 0.0 and 1.0. For precise sort, use the rate as 0 +Operator: scale(x, scale=1, longscale=1, shortscale=1) +Description: Scales input to booksize. We can also scale the long positions and short positions to separate scales by mentioning additional parameters to the operator +Operator: winsorize(x, std=4) +Description: Winsorizes x to make sure that all values in x are between the lower and upper limits, which are specified as multiple of std. +Operator: zscore(x) +Description: Z-score is a numerical measurement that describes a value's relationship to the mean of a group of values. Z-score is measured in terms of standard deviations from the mean +Operator: vec_avg(x) +Description: Taking mean of the vector field x +Operator: vec_sum(x) +Description: Sum of vector field x +Operator: bucket(rank(x), range="0, 1, 0.1" or buckets = "2,5,6,7,10") +Description: Convert float values into indexes for user-specified buckets. Bucket is useful for creating group values, which can be passed to GROUP as input +Operator: trade_when(x, y, z) +Description: Used in order to change Alpha values only under a specified condition and to hold Alpha values in other cases. It also allows to close Alpha positions (assign NaN values) under a specified condition +Operator: group_backfill(x, group, d, std = 4.0) +Description: If a certain value for a certain date and instrument is NaN, from the set of same group instruments, calculate winsorized mean of all non-NaN values over last d days +Operator: group_mean(x, weight, group) +Description: All elements in group equals to the mean +Operator: group_neutralize(x, group) +Description: Neutralizes Alpha against groups. These groups can be subindustry, industry, sector, country or a constant +Operator: group_rank(x, group) +Description: Each elements in a group is assigned the corresponding rank in this group +Operator: group_scale(x, group) +Description: Normalizes the values in a group to be between 0 and 1. (x - groupmin) / (groupmax - groupmin) +Operator: group_zscore(x, group) +Description: Calculates group Z-score - numerical measurement that describes a value's relationship to the mean of a group of values. Z-score is measured in terms of standard deviations from the mean. zscore = (data - mean) / stddev of x for each instrument within its group. +========================= 操作符结束 ======================================= + +========================= 数据字段开始 ======================================= +注意: data_set_name: 后面的是数据字段(可以使用), description: 此字段后面的是数据字段对应的描述或使用说明(不能使用) + +{'id': 140416, 'data_set_name': '可以使用:fnd72_pit_or_bs_a_bs_accrued_income_taxes', 'description': "不可使用,仅供参考:This field includes all taxes payable as disclosed under the company's current liabilities"} +{'id': 136988, 'data_set_name': '可以使用:fnd17_apayratio2', 'description': '不可使用,仅供参考:Payout ratio - most recent fiscal year -1'} +{'id': 60986, 'data_set_name': '可以使用:anl14_low_ntp_fp5', 'description': '不可使用,仅供参考:The lowest estimation of Net Profit - upcoming 5 quarters'} +{'id': 137321, 'data_set_name': '可以使用:net_income_quarterly_4', 'description': '不可使用,仅供参考:Net income for the most recent quarter after all expenses and taxes.'} +{'id': 138914, 'data_set_name': '可以使用:investment_contracts_total', 'description': '不可使用,仅供参考:Total value of investment contracts held.'} +{'id': 139158, 'data_set_name': '可以使用:value_investment_assets_total', 'description': '不可使用,仅供参考:Total value of investment assets held.'} +{'id': 136438, 'data_set_name': '可以使用:matched_share_award_value', 'description': '不可使用,仅供参考:Value of shares awarded plus matched percentage for a given plan.'} +{'id': 60865, 'data_set_name': '可以使用:anl14_high_ptprep_fy2', 'description': '不可使用,仅供参考:The Highest Estimation of Reported Pretax Profit - upcoming 2 years'} +{'id': 62001, 'data_set_name': '可以使用:quarterly_cash_flow_per_share_nonannualized', 'description': '不可使用,仅供参考:Cash flow per share for the quarter, not annualized.'} +{'id': 137318, 'data_set_name': '可以使用:net_income_available_to_common_incl_extraordinary_annual', 'description': '不可使用,仅供参考:Net income available to common shareholders including extraordinary items for the most recent fiscal year.'} +{'id': 62186, 'data_set_name': '可以使用:quarterly_total_assets_change_percent', 'description': '不可使用,仅供参考:Percent change in total assets compared to the same quarter last year.'} +{'id': 62110, 'data_set_name': '可以使用:quarterly_net_loans_change_yoy', 'description': '不可使用,仅供参考:Year-over-year percent change in net loans for the quarter.'} +{'id': 137433, 'data_set_name': '可以使用:accounts_receivable_current_assets', 'description': '不可使用,仅供参考:Accounts receivable included in current assets for the annual period.'} +{'id': 60758, 'data_set_name': '可以使用:anl14_high_capex_fy2', 'description': '不可使用,仅供参考:The Highest Estimation of Capital Expenditures - upcoming 2 years'} +{'id': 373589, 'data_set_name': '可以使用:pv37_low_global3h', 'description': '不可使用,仅供参考:Global Lowest Price 3H'} +{'id': 62151, 'data_set_name': '可以使用:quarterly_research_and_development_expense_2', 'description': '不可使用,仅供参考:Research and development expense for the most recent quarter.'} +{'id': 60781, 'data_set_name': '可以使用:anl14_high_ebit_fp2', 'description': '不可使用,仅供参考:The Highest Estimation of Earnings Before Interest & Taxes - upcoming 2 quarters'} +{'id': 139282, 'data_set_name': '可以使用:fnd28_bsassetq_value_02149q', 'description': '不可使用,仅供参考:value of quarterly field: Other Current Assets'} +{'id': 62173, 'data_set_name': '可以使用:quarterly_sga_to_sales_ratio', 'description': '不可使用,仅供参考:Selling, general, and administrative expenses as a percent of revenue for the quarter.'} +{'id': 139149, 'data_set_name': '可以使用:valuation_intangible_assets_net', 'description': '不可使用,仅供参考:Net valuation allowance for intangible assets.'} +{'id': 140448, 'data_set_name': '可以使用:fnd72_pit_or_bs_a_bs_other_st_liab', 'description': '不可使用,仅供参考:Other ST Liabilities'} +{'id': 62005, 'data_set_name': '可以使用:quarterly_cash_per_share_3', 'description': '不可使用,仅供参考:Cash plus short-term investments divided by shares outstanding for the quarter.'} +{'id': 62366, 'data_set_name': '可以使用:operatingprofit_currency_code', 'description': '不可使用,仅供参考:Currency in which the operating profit forecast is reported.'} +{'id': 140827, 'data_set_name': '可以使用:fnd72_s_pit_or_bs_q_bs_sh_cap_and_apic', 'description': '不可使用,仅供参考:Share Capital & APIC'} +{'id': 60747, 'data_set_name': '可以使用:anl14_high_bvps_fy1', 'description': '不可使用,仅供参考:The highest estimation of Book Value Per Share - upcoming year'} +{'id': 62212, 'data_set_name': '可以使用:quarterly_working_capital_per_share_to_price_2', 'description': '不可使用,仅供参考:Ratio of working capital per share to current price for the quarter.'} +{'id': 139128, 'data_set_name': '可以使用:total_current_assets_4', 'description': '不可使用,仅供参考:Total current assets reported for the annual period.'} +{'id': 60716, 'data_set_name': '可以使用:social_score_total_correlation', 'description': '不可使用,仅供参考:Social score weighted by metrics most strongly correlated (positive and negative) to financial returns.'} +{'id': 140483, 'data_set_name': '可以使用:fnd72_pit_or_bs_q_bs_accrued_income_taxes', 'description': "不可使用,仅供参考:This field includes all taxes payable as disclosed under the company's current liabilities"} +{'id': 373528, 'data_set_name': '可以使用:top2000_factor1_group20_score', 'description': '不可使用,仅供参考:First factor score for top 2000 securities, grouped into 20 clusters.'} +{'id': 60917, 'data_set_name': '可以使用:anl14_low_cfps_fy2', 'description': '不可使用,仅供参考:The Lowest Estimation of Cash Flow Per Share - upcoming 2 years'} +{'id': 60760, 'data_set_name': '可以使用:anl14_high_capex_fy4', 'description': '不可使用,仅供参考:The highest estimation of capital expenditures - upcoming 4 years'} +{'id': 61873, 'data_set_name': '可以使用:anl15_s_cal_fy0_total', 'description': '不可使用,仅供参考:The total of earnings estimation for the current calendarized fiscal year aggregated within GICS sector grouping.'} +{'id': 62738, 'data_set_name': '可以使用:anl69_sales_expected_report_dt', 'description': '不可使用,仅供参考:Expected Earnings Report Date'} +{'id': 140821, 'data_set_name': '可以使用:fnd72_s_pit_or_bs_q_bs_other_st_liab', 'description': '不可使用,仅供参考:Other ST Liabilities'} +{'id': 60822, 'data_set_name': '可以使用:anl14_high_nav_fy3', 'description': '不可使用,仅供参考:The Highest Estimation of Net Asset Value - upcoming 3 years'} +{'id': 140705, 'data_set_name': '可以使用:fnd72_pit_or_is_q_is_invest_income', 'description': '不可使用,仅供参考:Investment Income'} +{'id': 139037, 'data_set_name': '可以使用:share_based_awards', 'description': '不可使用,仅供参考:Value of share-based awards granted to employees or management.'} +{'id': 140125, 'data_set_name': '可以使用:fnd31_devnorthamericaadditionalfactor4_numrevy2', 'description': '不可使用,仅供参考:Net # of Revisions for Fiscal Year 2. It is defined as the weighted average of the number of FY2 analyst earnings forecasts raised less the number lowered within a month, divided by the total number of analyst forecasts.'} +{'id': 139038, 'data_set_name': '可以使用:share_based_deferred_tax', 'description': '不可使用,仅供参考:Deferred tax liabilities related to share-based compensation.'} +{'id': 140129, 'data_set_name': '可以使用:fnd31_devnorthamericaadditionalfactor4_pcwt', 'description': '不可使用,仅供参考:Time-Weighted Cash Yield. It is defined as the time-weighted average of cash flows per share estimates in fiscal year 1 and fiscal year 2 divided by close price.'} +{'id': 138948, 'data_set_name': '可以使用:long_term_secured_debt', 'description': '不可使用,仅供参考:Long-term secured debt outstanding at period end.'} +{'id': 62555, 'data_set_name': '可以使用:anl69_dps_expected_report_time', 'description': '不可使用,仅供参考:Expected Earnings Report Time'} +{'id': 139078, 'data_set_name': '可以使用:short_term_investment_cashflow', 'description': '不可使用,仅供参考:Cash flow from short-term investment activities.'} +{'id': 136705, 'data_set_name': '可以使用:fnd17_alldelay1_arevps', 'description': '不可使用,仅供参考:Revenue/share - most recent fiscal year'} +{'id': 137370, 'data_set_name': '可以使用:quarterly_total_debt_4', 'description': '不可使用,仅供参考:Total debt reported for the most recent quarter.'} +{'id': 139042, 'data_set_name': '可以使用:share_capital_ordinary_quarterly', 'description': '不可使用,仅供参考:Total value of ordinary share capital issued by the company for the quarter.'} +{'id': 60604, 'data_set_name': '可以使用:anl11_emp_totalcorregsubsecrnk', 'description': '不可使用,仅供参考:SubSector Rank on EMP Hybrid Score with 1 being the highest rank'} +{'id': 137372, 'data_set_name': '可以使用:quarterly_total_liabilities_5', 'description': '不可使用,仅供参考:Total liabilities reported for the most recent quarter.'} +{'id': 60809, 'data_set_name': '可以使用:anl14_high_eps_fy5', 'description': '不可使用,仅供参考:The highest estimation of earnings per share - upcoming 5 years'} +{'id': 136549, 'data_set_name': '可以使用:book_value_per_share_quarterly', 'description': '不可使用,仅供参考:Book value per share for the most recent quarter.'} +{'id': 60655, 'data_set_name': '可以使用:anl11_g_totalcorregsubsecrnk', 'description': '不可使用,仅供参考:SubSector Rank on G Hybrid Score with 1 being the highest rank'} +{'id': 139064, 'data_set_name': '可以使用:short_term_contract_expenses', 'description': '不可使用,仅供参考:Expenses related to short-term contracts.'} +{'id': 60808, 'data_set_name': '可以使用:anl14_high_eps_fy4', 'description': '不可使用,仅供参考:The Highest Estimation of Earnings Per Share - upcoming 4 years'} +{'id': 61006, 'data_set_name': '可以使用:anl14_low_ptp_fp1', 'description': '不可使用,仅供参考:The Lowest Estimation of Pretax Profit - upcoming quarter'} +{'id': 60927, 'data_set_name': '可以使用:anl14_low_div_fy3', 'description': '不可使用,仅供参考:The lowest estimation of Dividend - upcoming 3 years'} +{'id': 138935, 'data_set_name': '可以使用:long_term_debt_value', 'description': '不可使用,仅供参考:Total amount of long-term debt obligations for the period.'} +{'id': 62055, 'data_set_name': '可以使用:quarterly_free_cash_flow_per_share_2', 'description': '不可使用,仅供参考:Free cash flow per share for the most recent quarter.'} +{'id': 373540, 'data_set_name': '可以使用:top2000_factor3_group50_score', 'description': '不可使用,仅供参考:Third factor score for top 2000 securities, grouped into 50 clusters.'} +{'id': 62060, 'data_set_name': '可以使用:quarterly_free_cash_flow_per_share_non_annualized_2', 'description': '不可使用,仅供参考:Free cash flow per share for the most recent quarter, not annualized.'} +{'id': 60925, 'data_set_name': '可以使用:anl14_low_div_fy1', 'description': '不可使用,仅供参考:The lowest estimation of dividend - upcoming year'} +{'id': 138968, 'data_set_name': '可以使用:operating_income_2', 'description': '不可使用,仅供参考:Income generated from regular business operations.'} +{'id': 60806, 'data_set_name': '可以使用:anl14_high_eps_fy2', 'description': '不可使用,仅供参考:The Highest Estimation of Earnings Per Share - upcoming 2 years'} +{'id': 373426, 'data_set_name': '可以使用:resampled_pca_factor_1', 'description': '不可使用,仅供参考:First principal component factor derived from robust resampling of returns for top 2000 securities.'} +{'id': 140813, 'data_set_name': '可以使用:fnd72_s_pit_or_bs_q_bs_mkt_sec_other_st_invest', 'description': '不可使用,仅供参考:Marketable Securities & Other ST Investments'} +{'id': 373572, 'data_set_name': '可以使用:pv37_high_global2', 'description': '不可使用,仅供参考:Global Highest Price 2'} +{'id': 140547, 'data_set_name': '可以使用:fnd72_pit_or_cf_a_cf_cash_from_fnc_act', 'description': '不可使用,仅供参考:Cash from Financing Activities'} +{'id': 62576, 'data_set_name': '可以使用:anl69_dpss_expected_report_time', 'description': '不可使用,仅供参考:Expected Earnings Report Time'} +{'id': 138945, 'data_set_name': '可以使用:long_term_other_current_liabilities', 'description': '不可使用,仅供参考:Other current liabilities classified as long-term.'} +{'id': 140558, 'data_set_name': '可以使用:fnd72_pit_or_cf_a_cf_form_of_statement', 'description': '不可使用,仅供参考:No longer supported'} +{'id': 373427, 'data_set_name': '可以使用:resampled_pca_factor_10', 'description': '不可使用,仅供参考:Tenth principal component factor derived from robust resampling of returns for top 2000 securities.'} +{'id': 138887, 'data_set_name': '可以使用:future_tax_liabilities_fair_value', 'description': '不可使用,仅供参考:Fair value of future tax liabilities.'} +{'id': 137449, 'data_set_name': '可以使用:accrued_short_term_interest', 'description': '不可使用,仅供参考:Interest accrued on short-term obligations.'} +{'id': 62205, 'data_set_name': '可以使用:quarterly_total_liabilities_2', 'description': '不可使用,仅供参考:Total liabilities for the most recent quarter.'} +{'id': 62079, 'data_set_name': '可以使用:quarterly_long_term_debt_2', 'description': '不可使用,仅供参考:Long-term debt for the most recent quarter.'} +{'id': 140722, 'data_set_name': '可以使用:fnd72_pit_or_is_q_net_income', 'description': '不可使用,仅供参考:Net Income'} +{'id': 140718, 'data_set_name': '可以使用:fnd72_pit_or_is_q_is_tot_cash_com_dvd', 'description': '不可使用,仅供参考:Dividends paid to common shareholders from the profits of the company'} +{'id': 62611, 'data_set_name': '可以使用:anl69_eps_expected_report_dt', 'description': '不可使用,仅供参考:Expected Earnings Report Date'} +{'id': 62086, 'data_set_name': '可以使用:quarterly_long_term_debt_to_assets_ratio', 'description': '不可使用,仅供参考:Long-term debt divided by total assets for the most recent quarter.'} +{'id': 140134, 'data_set_name': '可以使用:fnd31_devnorthamericaadditionalfactor4_tw_ebitdaev', 'description': '不可使用,仅供参考:Time Weighted EBITDA/EV. It is defined as the time-weighted EBITDA/Enterprise Value for FY1 and FY2.'} +{'id': 140568, 'data_set_name': '可以使用:fnd72_pit_or_cf_a_cf_other_fnc_act', 'description': '不可使用,仅供参考:Other Financing Activities'} +{'id': 137394, 'data_set_name': '可以使用:revenue_per_share_quarterly_non_annualized', 'description': '不可使用,仅供参考:Revenue per share for the most recent quarter, not annualized.'} +{'id': 60870, 'data_set_name': '可以使用:anl14_high_revenue_fp2', 'description': '不可使用,仅供参考:The highest estimation of revenue - upcoming 2 quarters'} +{'id': 62419, 'data_set_name': '可以使用:anl45_treynor_ratio', 'description': '不可使用,仅供参考:A ratio that measures returns earned in excess of that which could have been earned on a riskless investment (such as the risk free rate).??'} +{'id': 373522, 'data_set_name': '可以使用:top1500_pca_factor2_grouping20', 'description': '不可使用,仅供参考:Second principal component grouping for top 1500 securities with 20 clusters.'} +{'id': 137137, 'data_set_name': '可以使用:fnd17_qcurratio2', 'description': '不可使用,仅供参考:Current ratio - most recent quarter, 1 year ago'} +{'id': 373327, 'data_set_name': '可以使用:pv173_rawratioseqbddivrgsprd5y120dsbst', 'description': '不可使用,仅供参考:It is defined as the difference of market equity return and estimated equity return based on 120-day linear regression of equity return on 5-year mid z-spread percentage change and MSCI ACWIIndex return'} +{'id': 61995, 'data_set_name': '可以使用:quarterly_cash_flow_per_share_2', 'description': '不可使用,仅供参考:Cash flow per share for the most recent quarter.'} +{'id': 139069, 'data_set_name': '可以使用:short_term_debt_bonds_face_value', 'description': '不可使用,仅供参考:Face value of short-term debt bonds outstanding.'} +{'id': 140531, 'data_set_name': '可以使用:fnd72_pit_or_bs_q_bs_total_avail_line_of_credit', 'description': '不可使用,仅供参考:Total remaining amount of the lines of credit'} +{'id': 138888, 'data_set_name': '可以使用:future_tax_liabilities_fund', 'description': '不可使用,仅供参考:Future tax liabilities fund for the interim period.'} +{'id': 61016, 'data_set_name': '可以使用:anl14_low_ptprep_fy3', 'description': '不可使用,仅供参考:The lowest estimation of reported pretax profit - upcoming 3 years'} +{'id': 140194, 'data_set_name': '可以使用:fnd44_perc_accruals', 'description': '不可使用,仅供参考:Net Accruals / Percentage Accruals'} +{'id': 61870, 'data_set_name': '可以使用:anl15_s_18_m_total', 'description': '不可使用,仅供参考:The total of 18-month forward earnings estimation aggregated within GICS sector grouping.'} +{'id': 137478, 'data_set_name': '可以使用:benefit_expense_total', 'description': '不可使用,仅供参考:Total benefit expense for the period.'} +{'id': 62648, 'data_set_name': '可以使用:anl69_net_best_crncy_iso', 'description': '不可使用,仅供参考:Currency (ISO)'} +{'id': 139294, 'data_set_name': '可以使用:fnd28_bsassetq_value_18280q', 'description': '不可使用,仅供参考:value of quarterly field: Goodwill/Cost in Excess of Assets Purchased'} +{'id': 60857, 'data_set_name': '可以使用:anl14_high_ptp_fp2', 'description': '不可使用,仅供参考:The highest estimation of pretax profit - upcoming 2 quarters'} +{'id': 62090, 'data_set_name': '可以使用:quarterly_long_term_debt_to_equity_prior', 'description': '不可使用,仅供参考:Long-term debt divided by shareholder equity for the same quarter last year.'} +{'id': 137484, 'data_set_name': '可以使用:cash_proceeds_received', 'description': '不可使用,仅供参考:Cash proceeds received during the period.'} +{'id': 60941, 'data_set_name': '可以使用:anl14_low_ebitda_fp2', 'description': '不可使用,仅供参考:The Lowest Estimation of Earnings Before Interest, Taxes, Depreciation & Amortization - upcoming 2 quarters'} +{'id': 140715, 'data_set_name': '可以使用:fnd72_pit_or_is_q_is_sga_other_op_depr_op_maint', 'description': '不可使用,仅供参考:Includes selling & administrative expenses and other operating expenses after cost of goods sold'} +{'id': 61999, 'data_set_name': '可以使用:quarterly_cash_flow_per_share_non_annualized', 'description': '不可使用,仅供参考:Cash flow per share for the quarter, not annualized.'} +{'id': 136447, 'data_set_name': '可以使用:share_award_quantity', 'description': '不可使用,仅供参考:Number of shares awarded in a given incentive plan.'} +{'id': 62462, 'data_set_name': '可以使用:anl69_best_net_4wk_up', 'description': '不可使用,仅供参考:Net Inc 4 Wk Up'} +{'id': 60789, 'data_set_name': '可以使用:anl14_high_ebit_fy5', 'description': '不可使用,仅供参考:The Highest Estimation of Earnings Before Interest & Taxes - upcoming 5 years'} +{'id': 60783, 'data_set_name': '可以使用:anl14_high_ebit_fp4', 'description': '不可使用,仅供参考:The highest estimation of earnings before interest & taxes - upcoming 4 quarters'} +{'id': 137149, 'data_set_name': '可以使用:fnd17_qpayratio', 'description': '不可使用,仅供参考:Payout ratio - most recent quarter'} +{'id': 140167, 'data_set_name': '可以使用:fnd31_qsg5additionalfactor3_monthly_pcdwf', 'description': '不可使用,仅供参考:Leading 12 Month Cash Yield. It is defined as the average quarterly forward-looking cash flows per share divided by close price.'} +{'id': 136550, 'data_set_name': '可以使用:cash_and_equivalents_annual_2', 'description': '不可使用,仅供参考:Cash and short-term investments for the most recent fiscal year.'} +{'id': 140442, 'data_set_name': '可以使用:fnd72_pit_or_bs_a_bs_options_outstanding', 'description': '不可使用,仅供参考:Options Outstanding End of Period'} +{'id': 62181, 'data_set_name': '可以使用:quarterly_tangible_book_value_per_share_3', 'description': '不可使用,仅供参考:Tangible book value divided by shares outstanding for the quarter.'} +{'id': 136785, 'data_set_name': '可以使用:fnd17_alldelay1_netdebt_a', 'description': '不可使用,仅供参考:Net Debt, LFY'} +{'id': 137348, 'data_set_name': '可以使用:quarterly_cash_per_share_value', 'description': '不可使用,仅供参考:Cash and short-term investments per share for the most recent quarter.'} +{'id': 137519, 'data_set_name': '可以使用:earnings_from_subsidiaries', 'description': '不可使用,仅供参考:Earnings derived from subsidiary companies.'} +{'id': 139046, 'data_set_name': '可以使用:share_dividend_equity', 'description': '不可使用,仅供参考:Share dividend equity reported for the annual period.'} +{'id': 62721, 'data_set_name': '可以使用:anl69_roes_expected_report_dt', 'description': '不可使用,仅供参考:Expected Earnings Report Date'} +{'id': 373523, 'data_set_name': '可以使用:top1500_pca_factor2_grouping5', 'description': '不可使用,仅供参考:Second principal component grouping for top 1500 securities with 5 clusters.'} +{'id': 62460, 'data_set_name': '可以使用:anl69_best_net_4wk_chg', 'description': '不可使用,仅供参考:Net Inc 4 Wk Chg'} +{'id': 139052, 'data_set_name': '可以使用:shareholders_other_equity_2', 'description': '不可使用,仅供参考:Other equity attributable to shareholders.'} +{'id': 60997, 'data_set_name': '可以使用:anl14_low_ntprep_fy1', 'description': '不可使用,仅供参考:The Lowest Estimation of Reported Net Profit - Upcoming Year'} +{'id': 140126, 'data_set_name': '可以使用:fnd31_devnorthamericaadditionalfactor4_pbdwf', 'description': '不可使用,仅供参考:Leading 12-Month Book Yield. It is defined as the average quarterly forward-looking book value per share divided by close price.'} +{'id': 62376, 'data_set_name': '可以使用:anl45_bias_weighted_ret', 'description': '不可使用,仅供参考:Non-Functional'} +{'id': 139083, 'data_set_name': '可以使用:short_term_loans_advances_quarterly', 'description': '不可使用,仅供参考:Short-term loans and advances provided by the company for the quarter.'} +{'id': 140195, 'data_set_name': '可以使用:fnd44_probability_restatement', 'description': '不可使用,仅供参考:Using an alternative data; we devise a model to predict accounting restatements. We show that restatements can lead to a significantly adverse stock price reaction. As such, our restatement model is useful at predicting underperforming stocks. This enhanced\r\nrestatement model employes machine learning and uses over 250 unconventional factors as inputs. We apply the model that was trained using US data to global stocks.'} +{'id': 140598, 'data_set_name': '可以使用:fnd72_pit_or_cf_q_cf_net_inc', 'description': '不可使用,仅供参考:Net Income'} +{'id': 373530, 'data_set_name': '可以使用:top2000_factor1_group50_score', 'description': '不可使用,仅供参考:First factor score for top 2000 securities, grouped into 50 clusters.'} +{'id': 136987, 'data_set_name': '可以使用:fnd17_apayratio', 'description': '不可使用,仅供参考:Payout ratio - most recent fiscal year'} +{'id': 60913, 'data_set_name': '可以使用:anl14_low_cfps_fp2', 'description': '不可使用,仅供参考:The lowest estimation of Cash Flow Per Share - upcoming 2 quarters'} +{'id': 137354, 'data_set_name': '可以使用:quarterly_earnings_before_tax_5', 'description': '不可使用,仅供参考:Earnings before taxes for the most recent quarter.'} +{'id': 136845, 'data_set_name': '可以使用:fnd17_alldelay1_qpelow', 'description': '不可使用,仅供参考:P/E excluding extraordinary items low, most recent quarter'} +{'id': 62059, 'data_set_name': '可以使用:quarterly_free_cash_flow_per_share_non_annualized', 'description': '不可使用,仅供参考:Free cash flow per share for the quarter, not annualized.'} +{'id': 373516, 'data_set_name': '可以使用:top1500_pca_factor1_grouping2', 'description': '不可使用,仅供参考:First principal component grouping for top 1500 securities with 2 clusters.'} +{'id': 62091, 'data_set_name': '可以使用:quarterly_long_term_debt_to_equity_prior_year', 'description': '不可使用,仅供参考:Long-term debt divided by shareholder equity for the same quarter last year.'} +{'id': 137514, 'data_set_name': '可以使用:dividends_withheld_settled', 'description': '不可使用,仅供参考:Dividends withheld and settled during the period.'} +{'id': 60803, 'data_set_name': '可以使用:anl14_high_eps_fp4', 'description': '不可使用,仅供参考:The Highest Estimation of Earnings Per Share - upcoming 4 quarters'} +{'id': 62362, 'data_set_name': '可以使用:forecast_currency_tangible_book_value_per_share', 'description': '不可使用,仅供参考:Currency in which the tangible book value per share forecast is denominated.'} +{'id': 138894, 'data_set_name': '可以使用:income_before_tax_value', 'description': '不可使用,仅供参考:Earnings before provision for income taxes for the period.'} +{'id': 62202, 'data_set_name': '可以使用:quarterly_total_debt_to_equity_ratio_prior_year', 'description': '不可使用,仅供参考:Total debt divided by equity for the same quarter last year.'} +{'id': 139268, 'data_set_name': '可以使用:fnd28_bsasseta_value_02301a', 'description': '不可使用,仅供参考:value of annual field: Property, Plant and Equipment - Gross'} +{'id': 60776, 'data_set_name': '可以使用:anl14_high_div_fy2', 'description': '不可使用,仅供参考:The Highest Estimation of Dividend - Upcoming 2 Years'} +{'id': 62465, 'data_set_name': '可以使用:anl69_best_net_gaap', 'description': '不可使用,仅供参考:Net GAAP'} +{'id': 140571, 'data_set_name': '可以使用:fnd72_pit_or_cf_a_cf_other_non_cash_adjust', 'description': '不可使用,仅供参考:Other Non-Cash Adjustments'} +{'id': 140160, 'data_set_name': '可以使用:fnd31_qsg5additionalfactor3_monthly_earningstorpedo', 'description': '不可使用,仅供参考:Earnings Torpedo. It is defined as Next 4 Quarter EPS Estimate + TTM Real Earnings Surprise/TTM EPS where, TTM Earnings Surprise = Actual EPS - Est EPS, summed over the last 4 quarters.'} +{'id': 140686, 'data_set_name': '可以使用:fnd72_pit_or_is_q_is_abnormal_item', 'description': '不可使用,仅供参考:Abnormal Items'} +{'id': 61693, 'data_set_name': '可以使用:anl15_gr_cal_fy1_total', 'description': '不可使用,仅供参考:The total of earnings estimation for the calendarized 1 fiscal year aggregated within GICS group grouping.'} +{'id': 62311, 'data_set_name': '可以使用:anl44_2_operatingprofit_coveredby', 'description': '不可使用,仅供参考:operatingprofit coveredby'} +{'id': 137360, 'data_set_name': '可以使用:quarterly_net_income_3', 'description': '不可使用,仅供参考:Net income for the most recent quarter.'} +{'id': 138904, 'data_set_name': '可以使用:interest_income_cashflow', 'description': '不可使用,仅供参考:Cash flow from interest income activities.'} +{'id': 60892, 'data_set_name': '可以使用:anl14_low_bvps_fp1', 'description': '不可使用,仅供参考:The lowest estimation of book value per share - upcoming quarter'} +{'id': 373588, 'data_set_name': '可以使用:pv37_low_global30m', 'description': '不可使用,仅供参考:Global Lowest Price 30M'} +{'id': 61025, 'data_set_name': '可以使用:anl14_low_revenue_fy2', 'description': '不可使用,仅供参考:The Lowest Estimation of Revenue - upcoming 2 years'} +{'id': 61014, 'data_set_name': '可以使用:anl14_low_ptprep_fy1', 'description': '不可使用,仅供参考:The lowest estimation of reported pretax profit - upcoming year'} +{'id': 60862, 'data_set_name': '可以使用:anl14_high_ptp_fy4', 'description': '不可使用,仅供参考:The Highest Estimation of Pretax Profit - upcoming 4 years'} +{'id': 62124, 'data_set_name': '可以使用:quarterly_pe_ratio_high', 'description': '不可使用,仅供参考:Highest price-to-earnings ratio for the most recent quarter.'} +{'id': 139073, 'data_set_name': '可以使用:short_term_financing_cash_flow', 'description': '不可使用,仅供参考:financing cash flow items represents sum of increase/decrease ind eposits, increase/decrease in federal funds sold or REPOs and FHLB plus other financing cash flow.'} +{'id': 60926, 'data_set_name': '可以使用:anl14_low_div_fy2', 'description': '不可使用,仅供参考:The Lowest Estimation of Dividend - Upcoming 2 Years'} +{'id': 136381, 'data_set_name': '可以使用:current_other_board_total', 'description': '不可使用,仅供参考:Number of other boards the director currently serves on.'} +{'id': 138902, 'data_set_name': '可以使用:interest_expense_net', 'description': '不可使用,仅供参考:Net interest expense for the period.'} +{'id': 62092, 'data_set_name': '可以使用:quarterly_long_term_debt_to_equity_ratio', 'description': '不可使用,仅供参考:Long-term debt divided by equity for the most recent quarter.'} +{'id': 139287, 'data_set_name': '可以使用:fnd28_bsassetq_value_02401q', 'description': '不可使用,仅供参考:value of quarterly field: Accumulated Depreciation'} +{'id': 139030, 'data_set_name': '可以使用:revenue_from_domestic_operations', 'description': '不可使用,仅供参考:[Quarterly] Expected Rate of Return - Domestic'} +{'id': 139016, 'data_set_name': '可以使用:quarterly_total_share_capital', 'description': '不可使用,仅供参考:Total share capital at the end of the quarter.'} +{'id': 138931, 'data_set_name': '可以使用:long_term_debt', 'description': '不可使用,仅供参考:Total value of long-term debt obligations outstanding.'} +{'id': 137512, 'data_set_name': '可以使用:dividend_per_share_declared', 'description': '不可使用,仅供参考:Dividend per share declared for the period.'} +{'id': 373281, 'data_set_name': '可以使用:pv132_px_low', 'description': '不可使用,仅供参考:Low Price'} +{'id': 60962, 'data_set_name': '可以使用:anl14_low_epsrep_fp3', 'description': '不可使用,仅供参考:The lowest estimation of reported earnings per share - upcoming 3 quarters'} +{'id': 140116, 'data_set_name': '可以使用:fnd31_creditrisk', 'description': '不可使用,仅供参考:Credit Risk is measured by CDS levels based on end-of-day par spreads.'} +{'id': 138937, 'data_set_name': '可以使用:long_term_liabilities_total_3', 'description': '不可使用,仅供参考:Total long-term liabilities outstanding at period end.'} +{'id': 136515, 'data_set_name': '可以使用:fnd13_statementq_sopi', 'description': '不可使用,仅供参考:Operating Income'} +{'id': 62099, 'data_set_name': '可以使用:quarterly_net_income_available_to_common_2', 'description': '不可使用,仅供参考:Net income available to common shareholders for the most recent quarter.'} +{'id': 60790, 'data_set_name': '可以使用:anl14_high_ebitda_fp1', 'description': '不可使用,仅供参考:The highest estimation of earnings before interest, taxes, depreciation & amortization - upcoming quarter'} +{'id': 137410, 'data_set_name': '可以使用:ttm_net_income_value', 'description': '不可使用,仅供参考:Net income for the trailing twelve months.'} +{'id': 60792, 'data_set_name': '可以使用:anl14_high_ebitda_fp3', 'description': '不可使用,仅供参考:The highest estimation of Earnings Before Interest, Taxes, Depreciation & Amortization - upcoming 3 quarters'} +{'id': 140891, 'data_set_name': '可以使用:fnd72_s_pit_or_is_q_is_eqy_earn_from_invest_assoc', 'description': '不可使用,仅供参考:Earnings from investments in associated companies, joint ventures, unconsolidated subsidiaries that are valued according to the equity method'} +{'id': 373576, 'data_set_name': '可以使用:pv37_high_global4h', 'description': '不可使用,仅供参考:Global Highest Price 4H'} +{'id': 62199, 'data_set_name': '可以使用:quarterly_total_debt_to_equity_2', 'description': '不可使用,仅供参考:Total debt divided by shareholder equity for the quarter.'} +{'id': 62299, 'data_set_name': '可以使用:anl44_2_netdebt_prevalue', 'description': '不可使用,仅供参考:netdebt prevalue'} +{'id': 139291, 'data_set_name': '可以使用:fnd28_bsassetq_value_02654q', 'description': '不可使用,仅供参考:value of quarterly field: Total Intangible Other Assets - Gross'} +{'id': 139168, 'data_set_name': '可以使用:voting_investment_total', 'description': '不可使用,仅供参考:Total investments with voting rights.'} +{'id': 137344, 'data_set_name': '可以使用:quarterly_book_value_per_share_5', 'description': '不可使用,仅供参考:Book value per share for the most recent quarter.'} +{'id': 140653, 'data_set_name': '可以使用:fnd72_pit_or_is_a_is_pension_expense_income', 'description': '不可使用,仅供参考:The net amount of pension cost that is recognized in the income statement'} +{'id': 60824, 'data_set_name': '可以使用:anl14_high_ndebt_fp2', 'description': '不可使用,仅供参考:The Highest Estimation of Net Debt - Upcoming 2 Quarters'} +{'id': 139124, 'data_set_name': '可以使用:stockholders_other_paid_in_items', 'description': '不可使用,仅供参考:Other paid-in items attributable to stockholders.'} +{'id': 136557, 'data_set_name': '可以使用:daily_volume_percent_shares_out', 'description': '不可使用,仅供参考:Average daily trading volume as a percentage of shares outstanding.'} +{'id': 138942, 'data_set_name': '可以使用:long_term_loans_total_debt', 'description': '不可使用,仅供参考:Total long-term loans and debt reported for the interim period.'} +{'id': 60979, 'data_set_name': '可以使用:anl14_low_ndebt_fy3', 'description': '不可使用,仅供参考:The lowest estimation of net debt - upcoming 3 years'} +{'id': 139142, 'data_set_name': '可以使用:unrealized_other_gain_balancesheet', 'description': '不可使用,仅供参考:[Quarterly] Right-of-Use Assets - Operating Lease, Gross - Supplemental'} +{'id': 62127, 'data_set_name': '可以使用:quarterly_pe_ratio_maximum', 'description': '不可使用,仅供参考:Highest price-to-earnings ratio observed in the latest quarter.'} +{'id': 60584, 'data_set_name': '可以使用:anl11_e_totalcorregsubsecperc', 'description': '不可使用,仅供参考:SubSector Percentile on E Hybrid Score with 100% being the best'} +{'id': 137481, 'data_set_name': '可以使用:cash_and_equivalents_3', 'description': '不可使用,仅供参考:Total cash and cash equivalents held at the end of the period.'} +{'id': 136999, 'data_set_name': '可以使用:fnd17_arev', 'description': '不可使用,仅供参考:Revenue - most recent fiscal year'} +{'id': 139259, 'data_set_name': '可以使用:fnd28_bsasseta_value_02099a', 'description': '不可使用,仅供参考:value of annual field: Finished Goods'} +{'id': 60914, 'data_set_name': '可以使用:anl14_low_cfps_fp3', 'description': '不可使用,仅供参考:The lowest estimation of Cash Flow Per Share - upcoming 3 quarters'} +{'id': 62190, 'data_set_name': '可以使用:quarterly_total_debt', 'description': '不可使用,仅供参考:Total debt reported for the most recent quarter.'} +{'id': 60919, 'data_set_name': '可以使用:anl14_low_cfps_fy4', 'description': '不可使用,仅供参考:The Lowest Estimation of Cash Flow Per Share - upcoming 4 years'} +{'id': 60530, 'data_set_name': '可以使用:anl11_cit_totalcor', 'description': "不可使用,仅供参考:The CIT Hybrid Score weights the three CIT scores based on which are strongly rank correlated (to the positive) to financial metrics that are strongly rank correlated to 1yr and 3 yr total returns for that company's peer group"} +{'id': 140753, 'data_set_name': '可以使用:fnd72_s_pit_or_bs_q_1_bs_sh_cap_and_apic', 'description': '不可使用,仅供参考:Share Capital & APIC'} +{'id': 62474, 'data_set_name': '可以使用:anl69_best_net_hi', 'description': '不可使用,仅供参考:Net Income High'} +{'id': 373571, 'data_set_name': '可以使用:pv37_high_global1h', 'description': '不可使用,仅供参考:Global Highest Price 1H'} +{'id': 138995, 'data_set_name': '可以使用:other_off_balance_liabilities', 'description': '不可使用,仅供参考:Other off-balance sheet liabilities for the annual period.'} +{'id': 60835, 'data_set_name': '可以使用:anl14_high_ntp_fp4', 'description': '不可使用,仅供参考:The Highest Estimation of Net Profit - upcoming 4 quarters'} +{'id': 139175, 'data_set_name': '可以使用:fnd25_metricvalue', 'description': '不可使用,仅供参考:Numeric field representing value of operating metric'} +{'id': 137315, 'data_set_name': '可以使用:net_income_after_tax_2', 'description': '不可使用,仅供参考:Net income after all expenses and taxes for the period.'} +{'id': 137475, 'data_set_name': '可以使用:bank_borrowings_total', 'description': '不可使用,仅供参考:Total bank borrowings outstanding.'} +{'id': 62133, 'data_set_name': '可以使用:quarterly_price_to_cash_flow_per_share_2', 'description': '不可使用,仅供参考:Ratio of price to cash flow per share for the quarter.'} +{'id': 60953, 'data_set_name': '可以使用:anl14_low_eps_fp4', 'description': '不可使用,仅供参考:The lowest estimation of earnings per share - upcoming 4 quarters'} +{'id': 373567, 'data_set_name': '可以使用:pv37_high_13', 'description': '不可使用,仅供参考:High price'} +{'id': 140469, 'data_set_name': '可以使用:fnd72_pit_or_bs_a_bs_total_capital_leases', 'description': '不可使用,仅供参考:Total of short- and long-term amounts payable by a lessee under capital leases'} +{'id': 139013, 'data_set_name': '可以使用:quarterly_total_equity_liabilities', 'description': '不可使用,仅供参考:Total equity and liabilities at the end of the quarter.'} +{'id': 139102, 'data_set_name': '可以使用:short_term_payables_other', 'description': '不可使用,仅供参考:[Quarterly] Total Plan Obligations'} +{'id': 137438, 'data_set_name': '可以使用:accounts_total_current_assets_2', 'description': '不可使用,仅供参考:Total current assets as reported in accounts.'} +{'id': 137373, 'data_set_name': '可以使用:quarterly_total_revenue_3', 'description': '不可使用,仅供参考:Total revenue reported for the most recent quarter.'} +{'id': 138951, 'data_set_name': '可以使用:long_term_trade_debt', 'description': '不可使用,仅供参考:Long-term trade debt reported for the annual period.'} +{'id': 373575, 'data_set_name': '可以使用:pv37_high_global3h', 'description': '不可使用,仅供参考:Global Highest Price 3H'} +{'id': 140489, 'data_set_name': '可以使用:fnd72_pit_or_bs_q_bs_allow_doubtful_acc_rec', 'description': '不可使用,仅供参考:A contra asset account that is subtracted from trade and notes receivables on the balance sheet'} +{'id': 62137, 'data_set_name': '可以使用:quarterly_quick_ratio', 'description': '不可使用,仅供参考:Ratio of liquid assets to current liabilities for the latest quarter.'} +{'id': 61010, 'data_set_name': '可以使用:anl14_low_ptp_fy2', 'description': '不可使用,仅供参考:The lowest estimation of Pretax Profit - upcoming 2 years'} +{'id': 60860, 'data_set_name': '可以使用:anl14_high_ptp_fy2', 'description': '不可使用,仅供参考:The Highest Estimation of Pretax Profit - upcoming 2 years'} +{'id': 60899, 'data_set_name': '可以使用:anl14_low_bvps_fy3', 'description': '不可使用,仅供参考:The Lowest Estimation of Book Value Per Share - upcoming 3 years'} +{'id': 60924, 'data_set_name': '可以使用:anl14_low_div_fp5', 'description': '不可使用,仅供参考:The lowest estimation of dividend - upcoming 5 quarters'} +{'id': 140581, 'data_set_name': '可以使用:fnd72_pit_or_cf_q_cf_cash_from_oper', 'description': '不可使用,仅供参考:Total amount of cash a company generates from its operation'} +{'id': 140397, 'data_set_name': '可以使用:fnd72_1_bs_gross_fix_asset', 'description': '不可使用,仅供参考:Gross Fixed Assets'} +{'id': 60752, 'data_set_name': '可以使用:anl14_high_capex_fp1', 'description': '不可使用,仅供参考:The highest estimation of capital expenditures - upcoming quarter'} +{'id': 139122, 'data_set_name': '可以使用:stockholders_other_comprehensive_assets', 'description': '不可使用,仅供参考:Other comprehensive assets attributable to stockholders.'} +{'id': 140567, 'data_set_name': '可以使用:fnd72_pit_or_cf_a_cf_net_inc', 'description': '不可使用,仅供参考:Net Income'} +{'id': 62209, 'data_set_name': '可以使用:quarterly_total_shareholder_equity', 'description': '不可使用,仅供参考:Total shareholder equity for the most recent quarter.'} +{'id': 136646, 'data_set_name': '可以使用:fnd17_alldelay1_a2netmrgn', 'description': '不可使用,仅供参考:Net Profit Margin % - 2nd historical fiscal year'} +{'id': 139134, 'data_set_name': '可以使用:total_rental_expense', 'description': '不可使用,仅供参考:Total rental expenses incurred during the period.'} +{'id': 60532, 'data_set_name': '可以使用:anl11_cit_totalcorregsectrnk', 'description': '不可使用,仅供参考:Sector Rank on CIT Hybrid Score with 1 being the highest rank'} +{'id': 140867, 'data_set_name': '可以使用:fnd72_s_pit_or_cf_q_cf_other_non_cash_adj_less', 'description': '不可使用,仅供参考:Other Non-Cash Adjustments'} +{'id': 62206, 'data_set_name': '可以使用:quarterly_total_liabilities_3', 'description': '不可使用,仅供参考:Sum of all liabilities for the most recent quarter.'} +{'id': 140494, 'data_set_name': '可以使用:fnd72_pit_or_bs_q_bs_cur_asset_report', 'description': '不可使用,仅供参考:Current Assets Reported'} +{'id': 60970, 'data_set_name': '可以使用:anl14_low_nav_fy1', 'description': '不可使用,仅供参考:The lowest estimation of net asset value - upcoming year'} +{'id': 60995, 'data_set_name': '可以使用:anl14_low_ntprep_fp4', 'description': '不可使用,仅供参考:The lowest estimation of reported net profit - upcoming 4 quarters'} +{'id': 140645, 'data_set_name': '可以使用:fnd72_pit_or_is_a_is_net_interest_expense', 'description': '不可使用,仅供参考:Interest expense minus interest income'} +{'id': 60983, 'data_set_name': '可以使用:anl14_low_ntp_fp2', 'description': '不可使用,仅供参考:The lowest estimation of net profit - upcoming 2 quarters'} +{'id': 60834, 'data_set_name': '可以使用:anl14_high_ntp_fp3', 'description': '不可使用,仅供参考:The Highest Estimation of Net Profit - upcoming 3 quarters'} +{'id': 62068, 'data_set_name': '可以使用:quarterly_interest_coverage_ratio', 'description': '不可使用,仅供参考:Earnings before interest and taxes divided by interest expense for the quarter.'} +{'id': 60746, 'data_set_name': '可以使用:anl14_high_bvps_fp5', 'description': '不可使用,仅供参考:The Highest Estimation of Book Value Per Share - Upcoming 5 Quarters'} +{'id': 137414, 'data_set_name': '可以使用:twelve_month_high_price_2', 'description': '不可使用,仅供参考:The highest price at which the security traded in the last twelve months.'} +{'id': 373569, 'data_set_name': '可以使用:pv37_high_15', 'description': '不可使用,仅供参考:High price'} +{'id': 61979, 'data_set_name': '可以使用:quarterly_asset_turnover', 'description': '不可使用,仅供参考:Annualized ratio of sales to average assets for the quarter.'} +{'id': 62058, 'data_set_name': '可以使用:quarterly_free_cash_flow_per_share_alt_2', 'description': '不可使用,仅供参考:Alternate calculation of free cash flow per share for the quarter.'} +{'id': 139140, 'data_set_name': '可以使用:unrealized_currency_net_balancesheet', 'description': '不可使用,仅供参考:[Quarterly] Right-of-Use Assets - Capital Lease, Net - Supplemental'} +{'id': 140757, 'data_set_name': '可以使用:fnd72_s_pit_or_bs_q_1_bs_tot_asset', 'description': '不可使用,仅供参考:Total Assets'} +{'id': 62667, 'data_set_name': '可以使用:anl69_pe_expected_report_dt', 'description': '不可使用,仅供参考:Expected Earnings Report Date'} +{'id': 140432, 'data_set_name': '可以使用:fnd72_pit_or_bs_a_bs_future_min_oper_lease_oblig', 'description': '不可使用,仅供参考:Total of the Future Minimum Operating Lease Commitments'} +{'id': 60748, 'data_set_name': '可以使用:anl14_high_bvps_fy2', 'description': '不可使用,仅供参考:The highest estimation of Book Value Per Share - upcoming 2 years'} +{'id': 136530, 'data_set_name': '可以使用:annual_debt_to_total_capital', 'description': '不可使用,仅供参考:Ratio of total debt to total capital for the most recent fiscal year.'} +{'id': 61885, 'data_set_name': '可以使用:anl15_s_cal_fy1_total', 'description': '不可使用,仅供参考:The total of earnings estimation for the calendarized 1 fiscal year aggregated within GICS sector grouping.'} +{'id': 138970, 'data_set_name': '可以使用:operating_revenue_other', 'description': '不可使用,仅供参考:[Quarterly] Other Revenue, Total'} +{'id': 60827, 'data_set_name': '可以使用:anl14_high_ndebt_fy1', 'description': '不可使用,仅供参考:The Highest Estimation of Net Debt - upcoming year'} +{'id': 62147, 'data_set_name': '可以使用:quarterly_receivables_turnover_2', 'description': '不可使用,仅供参考:Receivables turnover ratio for the most recent quarter.'} +{'id': 137226, 'data_set_name': '可以使用:fnd17_siratio4', 'description': '不可使用,仅供参考:Short Interest ratio - 3 months ago'} +{'id': 137415, 'data_set_name': '可以使用:twelve_month_low_price', 'description': '不可使用,仅供参考:The lowest price at which the security traded in the last twelve months.'} +{'id': 62033, 'data_set_name': '可以使用:quarterly_dividend_per_share', 'description': '不可使用,仅供参考:Dividend paid per share for the most recent quarter.'} +{'id': 60884, 'data_set_name': '可以使用:anl14_high_roe_fp2', 'description': '不可使用,仅供参考:The highest estimation of returns on equity - upcoming 2 quarters'} +{'id': 137712, 'data_set_name': '可以使用:fnd23_headlinefull', 'description': '不可使用,仅供参考:full story headline index'} +{'id': 60867, 'data_set_name': '可以使用:anl14_high_ptprep_fy4', 'description': '不可使用,仅供参考:The Highest Estimation of Reported Pretax Profit - Upcoming 4 Years'} +{'id': 60820, 'data_set_name': '可以使用:anl14_high_nav_fy1', 'description': '不可使用,仅供参考:The Highest Estimation of Net Asset Value - upcoming year'} +{'id': 62119, 'data_set_name': '可以使用:quarterly_operating_margin_percent', 'description': '不可使用,仅供参考:Operating income divided by revenue for the quarter, expressed as a percentage.'} +{'id': 140130, 'data_set_name': '可以使用:fnd31_devnorthamericaadditionalfactor4_ps_wt', 'description': '不可使用,仅供参考:Time Weighted Sales Yield. It is defined as the time-weighted sales per share for FY1 and FY2 divided by its price.'} +{'id': 140736, 'data_set_name': '可以使用:fnd72_s_pit_or_bs_q_1_bs_cash_near_cash_item', 'description': '不可使用,仅供参考:Cash & Near Cash Items'} +{'id': 137001, 'data_set_name': '可以使用:fnd17_arevperemp', 'description': '不可使用,仅供参考:Revenue/employee - most recent fiscal year'} +{'id': 62470, 'data_set_name': '可以使用:anl69_best_net_gaap_lo', 'description': '不可使用,仅供参考:Net GAAP Low'} +{'id': 60581, 'data_set_name': '可以使用:anl11_e_totalcor', 'description': "不可使用,仅供参考:The E Hybrid Score weights the three E scores based on which are strongly rank correlated (to the positive) to financial metrics related to 1yr and 3 yr total returns for that company's peer group."} +{'id': 139271, 'data_set_name': '可以使用:fnd28_bsasseta_value_02647a', 'description': '不可使用,仅供参考:value of annual field: Deferred Charges'} +{'id': 139086, 'data_set_name': '可以使用:short_term_loans_drawn', 'description': '不可使用,仅供参考:Short-term loans drawn and outstanding at period end.'} +{'id': 139107, 'data_set_name': '可以使用:short_term_securities_gain_adjustment', 'description': '不可使用,仅供参考:Adjustment for gains on short-term securities.'} +{'id': 60977, 'data_set_name': '可以使用:anl14_low_ndebt_fy1', 'description': '不可使用,仅供参考:The lowest estimation of net debt - upcoming year'} +{'id': 140632, 'data_set_name': '可以使用:fnd72_pit_or_is_a_is_expected_return_plan_assets', 'description': '不可使用,仅供参考:The estimated expected long-term rate of return on pension plan assets expressed as a percent'} +{'id': 139269, 'data_set_name': '可以使用:fnd28_bsasseta_value_02401a', 'description': '不可使用,仅供参考:value of annual field: Accumulated Depreciation'} +{'id': 138985, 'data_set_name': '可以使用:other_long_term_obligations_2', 'description': '不可使用,仅供参考:Other long-term obligations not classified elsewhere.'} +{'id': 62392, 'data_set_name': '可以使用:anl45_net_market_exposure', 'description': '不可使用,仅供参考:Difference in fractional total value between long and short ideas'} +{'id': 61030, 'data_set_name': '可以使用:anl14_low_roa_fy2', 'description': '不可使用,仅供参考:The lowest estimation of Returns on Assets - upcoming 2 years'} +{'id': 62587, 'data_set_name': '可以使用:anl69_ebit_expected_report_dt', 'description': '不可使用,仅供参考:Expected Earnings Report Date'} +{'id': 139280, 'data_set_name': '可以使用:fnd28_bsassetq_value_02101q', 'description': '不可使用,仅供参考:value of quarterly field: Inventories Total'} +{'id': 136448, 'data_set_name': '可以使用:share_award_quantity_ltip', 'description': '不可使用,仅供参考:Number of shares awarded in a given incentive plan for LTIP compensation.'} +{'id': 137281, 'data_set_name': '可以使用:fnd17_ttmpehigh', 'description': '不可使用,仅供参考:P/E excluding extraordinary items high, trailing 12 months'} +{'id': 140198, 'data_set_name': '可以使用:fnd44_working_capital_accruals', 'description': '不可使用,仅供参考:Working Capital Accruals'} +{'id': 140935, 'data_set_name': '可以使用:srp_earnings_score', 'description': '不可使用,仅供参考:Earnings score'} +{'id': 62052, 'data_set_name': '可以使用:quarterly_free_cash_flow', 'description': '不可使用,仅供参考:Free cash flow for the most recent quarter.'} +{'id': 60896, 'data_set_name': '可以使用:anl14_low_bvps_fp5', 'description': '不可使用,仅供参考:The lowest estimation of Book Value Per Share - upcoming 5 quarters'} +{'id': 136457, 'data_set_name': '可以使用:total_shareholding_value_2', 'description': '不可使用,仅供参考:Value of shares held by the director at the end of the period (alternate).'} +{'id': 140815, 'data_set_name': '可以使用:fnd72_s_pit_or_bs_q_bs_options_outstanding', 'description': '不可使用,仅供参考:Options Outstanding End of Period'} +{'id': 60811, 'data_set_name': '可以使用:anl14_high_epsrep_fp2', 'description': '不可使用,仅供参考:The Highest Estimation of Reported Earnings Per Share - upcoming 2 quarters'} +{'id': 139061, 'data_set_name': '可以使用:short_term_cash_investment_proceeds', 'description': '不可使用,仅供参考:cash interest paid represents interest paid in cash during the period Cash Interest.'} +{'id': 140117, 'data_set_name': '可以使用:fnd31_devnorthamericaadditionalfactor4_apg', 'description': '不可使用,仅供参考:TTM Gross Profit to Assets. It is defined as the TTM (Sales - COGS)/Total Assets.'} +{'id': 140630, 'data_set_name': '可以使用:fnd72_pit_or_is_a_is_eqy_earn_from_invest_assoc', 'description': '不可使用,仅供参考:Earnings from investments in associated companies, joint ventures, unconsolidated subsidiaries that are valued according to the equity method'} +{'id': 140137, 'data_set_name': '可以使用:fnd31_devnorthamericaadditionalfactor4_y5speq4vc', 'description': '不可使用,仅供参考:Stability of 5-yr TTM Earnings per Share. It is defined as the standard deviation of the last 20-Month trailing 12-month earnings per share (EPS) divided by the mean of these EPSs.'} +{'id': 373436, 'data_set_name': '可以使用:resampled_pca_factor_6', 'description': '不可使用,仅供参考:Sixth principal component factor derived from robust resampling of returns for top 2000 securities.'} +{'id': 373535, 'data_set_name': '可以使用:top2000_factor2_group50_score', 'description': '不可使用,仅供参考:Second factor score for top 2000 securities, grouped into 50 clusters.'} +{'id': 137000, 'data_set_name': '可以使用:fnd17_arevchg', 'description': '不可使用,仅供参考:Revenue Change %, year over year'} +{'id': 60921, 'data_set_name': '可以使用:anl14_low_div_fp2', 'description': '不可使用,仅供参考:The Lowest Estimation of Dividend - Upcoming 2 Quarters'} +{'id': 62473, 'data_set_name': '可以使用:anl69_best_net_gaap_stddev', 'description': '不可使用,仅供参考:Net GAAP Std Dev'} +{'id': 137426, 'data_set_name': '可以使用:accounts_current_assets_equity', 'description': '不可使用,仅供参考:Current assets related to equity accounts.'} +{'id': 140175, 'data_set_name': '可以使用:fnd31_qsg5additionalfactor3_monthly_y5speq4rqsr', 'description': '不可使用,仅供参考:R Sqr of 5-yr TTM EPS Trend Line. It is defined as the slope coefficient between monthly dates and the corresponding trailing 12-month earnings per share before extra items in the prior 20 quarters.'} +{'id': 62026, 'data_set_name': '可以使用:quarterly_current_ratio_prior_year', 'description': '不可使用,仅供参考:Current assets divided by current liabilities for the same quarter last year.'} +{'id': 61002, 'data_set_name': '可以使用:anl14_low_opp_fp1', 'description': '不可使用,仅供参考:The Lowest estimation of Operating Profit - upcoming quarter'} +{'id': 139071, 'data_set_name': '可以使用:short_term_debt_issued', 'description': '不可使用,仅供参考:Depreciation/Depletion represents the sum of Depreciation and Depletion.'} +{'id': 137488, 'data_set_name': '可以使用:cumulative_short_term_creditors_since_q1', 'description': '不可使用,仅供参考:Cumulative short-term creditors value since the first quarter.'} +{'id': 139060, 'data_set_name': '可以使用:short_term_borrowings_value_2', 'description': '不可使用,仅供参考:Value of short-term borrowings outstanding.'} +{'id': 140631, 'data_set_name': '可以使用:fnd72_pit_or_is_a_is_expected_return_pension', 'description': '不可使用,仅供参考:The component of net pension expense that pertains to the expected return on pension plan assets'} +{'id': 62093, 'data_set_name': '可以使用:quarterly_long_term_debt_to_equity_ratio_prior_year', 'description': '不可使用,仅供参考:Long-term debt divided by equity for the same quarter last year.'} +{'id': 60802, 'data_set_name': '可以使用:anl14_high_eps_fp3', 'description': '不可使用,仅供参考:The Highest estimation of Earnings Per Share - upcoming 3 quarters'} +{'id': 140506, 'data_set_name': '可以使用:fnd72_pit_or_bs_q_bs_options_outstanding', 'description': '不可使用,仅供参考:Options Outstanding End of Period'} +{'id': 62472, 'data_set_name': '可以使用:anl69_best_net_gaap_numest', 'description': '不可使用,仅供参考:Net GAAP # Ests'} +{'id': 61986, 'data_set_name': '可以使用:quarterly_book_value_per_share_2', 'description': '不可使用,仅供参考:Book value per share for the most recent quarter.'} +{'id': 62214, 'data_set_name': '可以使用:receivables_turnover_quarterly', 'description': '不可使用,仅供参考:Annualized ratio of total revenue to average receivables for the most recent quarter.'} +{'id': 137325, 'data_set_name': '可以使用:normalized_net_income', 'description': '不可使用,仅供参考:Net income after taxes excluding unusual or one-time items.'} +{'id': 139041, 'data_set_name': '可以使用:share_capital_ordinary', 'description': '不可使用,仅供参考:Ordinary share capital reported for the annual period.'} +{'id': 140550, 'data_set_name': '可以使用:fnd72_pit_or_cf_a_cf_cash_paid_for_tax', 'description': '不可使用,仅供参考:Cash Paid for Taxes'} +{'id': 137466, 'data_set_name': '可以使用:annual_net_income_value', 'description': '不可使用,仅供参考:Net income reported for the annual period.'} +{'id': 60832, 'data_set_name': '可以使用:anl14_high_ntp_fp1', 'description': '不可使用,仅供参考:The Highest Estimation of Net Profit - Upcoming Quarter'} +{'id': 61041, 'data_set_name': '可以使用:anl14_low_roe_fy5', 'description': '不可使用,仅供参考:The lowest estimation of Returns on Equity - upcoming 5 years'} +{'id': 138928, 'data_set_name': '可以使用:long_term_current_liabilities_3', 'description': '不可使用,仅供参考:Current portion of long-term liabilities due within one year.'} +{'id': 60910, 'data_set_name': '可以使用:anl14_low_capex_fy4', 'description': '不可使用,仅供参考:The Lowest Estimation of Capital Expenditures - upcoming 4 years'} +{'id': 373517, 'data_set_name': '可以使用:top1500_pca_factor1_grouping20', 'description': '不可使用,仅供参考:First principal component grouping for top 1500 securities with 20 clusters.'} +{'id': 139092, 'data_set_name': '可以使用:short_term_non_current_cash', 'description': '不可使用,仅供参考:Short-term cash classified as non-current.'} +{'id': 61013, 'data_set_name': '可以使用:anl14_low_ptp_fy5', 'description': '不可使用,仅供参考:The lowest estimation of Pretax Profit - upcoming 5 years'} +{'id': 61972, 'data_set_name': '可以使用:income_taxes_paid_quarterly', 'description': '不可使用,仅供参考:Total income taxes paid during the most recent quarter.'} +{'id': 60956, 'data_set_name': '可以使用:anl14_low_eps_fy2', 'description': '不可使用,仅供参考:The lowest estimation of earnings per share - upcoming 2 years'} +{'id': 140402, 'data_set_name': '可以使用:fnd72_bs_net_fix_asset', 'description': '不可使用,仅供参考:Net Fixed Assets'} +{'id': 62054, 'data_set_name': '可以使用:quarterly_free_cash_flow_per_share', 'description': '不可使用,仅供参考:Free cash flow per share for the latest quarter.'} +{'id': 138983, 'data_set_name': '可以使用:other_interest_liabilities_2', 'description': '不可使用,仅供参考:represents changes in inventories during the period. An increase in inventories has a negative impact on cash flow from operating activities, and vice versa.'} +{'id': 139281, 'data_set_name': '可以使用:fnd28_bsassetq_value_02140q', 'description': '不可使用,仅供参考:value of quarterly field: Prepaid Expenses'} +{'id': 60912, 'data_set_name': '可以使用:anl14_low_cfps_fp1', 'description': '不可使用,仅供参考:The lowest estimation of cash flow per share - upcoming quarter'} +{'id': 138924, 'data_set_name': '可以使用:long_term_capital_commitments', 'description': '不可使用,仅供参考:[Quarterly] Current Portion of Long-Term Capital Leases, Supplemental'} +{'id': 60602, 'data_set_name': '可以使用:anl11_emp_totalcorregsectrnk', 'description': '不可使用,仅供参考:Sector Rank on EMP Hybrid Score with 1 being the highest rank'} +{'id': 62024, 'data_set_name': '可以使用:quarterly_current_ratio_alt', 'description': '不可使用,仅供参考:Alternate calculation of current ratio for the most recent quarter.'} +{'id': 140774, 'data_set_name': '可以使用:fnd72_s_pit_or_bs_q_2_bs_future_min_oper_lease_oblig', 'description': '不可使用,仅供参考:Total of the Future Minimum Operating Lease Commitments'} +{'id': 60952, 'data_set_name': '可以使用:anl14_low_eps_fp3', 'description': '不可使用,仅供参考:The Lowest Estimation of Earnings Per Share - upcoming 3 quarters'} +{'id': 138950, 'data_set_name': '可以使用:long_term_tax_payable_4', 'description': '不可使用,仅供参考:Long-term tax payable reported in interim financials.'} +{'id': 136804, 'data_set_name': '可以使用:fnd17_alldelay1_pelow', 'description': '不可使用,仅供参考:P/E excluding extraordinary items, Low'} +{'id': 140747, 'data_set_name': '可以使用:fnd72_s_pit_or_bs_q_1_bs_other_cur_asset', 'description': '不可使用,仅供参考:Other Current Assets'} +{'id': 373587, 'data_set_name': '可以使用:pv37_low_global2h', 'description': '不可使用,仅供参考:Global Lowest Price 2H'} +{'id': 136545, 'data_set_name': '可以使用:annual_total_revenue', 'description': '不可使用,仅供参考:Total revenue for the most recent fiscal year.'} +{'id': 140807, 'data_set_name': '可以使用:fnd72_s_pit_or_bs_q_bs_future_min_oper_lease_oblig', 'description': '不可使用,仅供参考:Total of the Future Minimum Operating Lease Commitments'} +{'id': 137551, 'data_set_name': '可以使用:fixed_production_assets', 'description': '不可使用,仅供参考:Value of fixed assets used in production.'} +{'id': 139253, 'data_set_name': '可以使用:fnd28_bsasseta_value_02001a', 'description': '不可使用,仅供参考:Value of annual field: Cash & Short-Term Investments'} +{'id': 136454, 'data_set_name': '可以使用:total_equity_linked_wealth_2', 'description': '不可使用,仅供参考:Total value of equity-linked wealth held by the director at period end (alternate).'} +{'id': 60765, 'data_set_name': '可以使用:anl14_high_cfps_fp4', 'description': '不可使用,仅供参考:The Highest Estimation of Cash Flow Per Share - upcoming 4 quarters'} +{'id': 60909, 'data_set_name': '可以使用:anl14_low_capex_fy3', 'description': '不可使用,仅供参考:The Lowest estimation of Capital Expenditures - upcoming 3 years'} +{'id': 138964, 'data_set_name': '可以使用:net_investments', 'description': '不可使用,仅供参考:Net investments reported in interim financials.'} +{'id': 140569, 'data_set_name': '可以使用:fnd72_pit_or_cf_a_cf_other_inv_act', 'description': '不可使用,仅供参考:Other Investing Activities'} +{'id': 139085, 'data_set_name': '可以使用:short_term_loans_debt_2', 'description': '不可使用,仅供参考:Short-term loans and debt obligations due within one year.'} +{'id': 136382, 'data_set_name': '可以使用:current_other_listed_boards_count_2', 'description': '不可使用,仅供参考:Number of other listed boards currently served on by directors (alternate).'} +{'id': 137411, 'data_set_name': '可以使用:ttm_total_assets_value', 'description': '不可使用,仅供参考:Total assets for the trailing twelve months.'} +{'id': 139040, 'data_set_name': '可以使用:share_capital_in_preference', 'description': '不可使用,仅供参考:Share capital in preference reported for the interim period.'} +{'id': 140901, 'data_set_name': '可以使用:fnd72_s_pit_or_is_q_is_net_non_oper_loss', 'description': '不可使用,仅供参考:Net Non-Operating Losses'} +{'id': 60950, 'data_set_name': '可以使用:anl14_low_eps_fp1', 'description': '不可使用,仅供参考:The lowest estimation of earnings per share - upcoming quarter'} +{'id': 136439, 'data_set_name': '可以使用:matched_share_award_value_ltip', 'description': '不可使用,仅供参考:Value of shares awarded plus matched percentage for a given plan for LTIP compensation.'} +{'id': 62188, 'data_set_name': '可以使用:quarterly_total_assets_change_yoy', 'description': '不可使用,仅供参考:Year-over-year percent change in total assets for the quarter.'} +{'id': 61998, 'data_set_name': '可以使用:quarterly_cash_flow_per_share_alt_2', 'description': '不可使用,仅供参考:Alternate calculation of cash flow per share for the quarter.'} +{'id': 61966, 'data_set_name': '可以使用:cash_flow_per_share_quarterly_prev_year', 'description': '不可使用,仅供参考:Cash flow per share for the same quarter in the previous year.'} +{'id': 137437, 'data_set_name': '可以使用:accounts_total_current_assets', 'description': '不可使用,仅供参考:Total current assets as reported in accounts.'} +{'id': 60780, 'data_set_name': '可以使用:anl14_high_ebit_fp1', 'description': '不可使用,仅供参考:The highest estimation of earnings before interest & taxes - upcoming quarter'} +{'id': 139163, 'data_set_name': '可以使用:voting_common_shares_total', 'description': '不可使用,仅供参考:Total number of voting common shares outstanding.'} +{'id': 138933, 'data_set_name': '可以使用:long_term_debt_total', 'description': '不可使用,仅供参考:Total long-term debt reported for the annual period.'} +{'id': 61974, 'data_set_name': '可以使用:price_to_cash_flow_per_share_quarterly', 'description': '不可使用,仅供参考:Ratio of share price to cash flow per share for the most recent quarter.'} +{'id': 61987, 'data_set_name': '可以使用:quarterly_book_value_per_share_3', 'description': '不可使用,仅供参考:Common equity divided by shares outstanding for the most recent quarter.'} +{'id': 60764, 'data_set_name': '可以使用:anl14_high_cfps_fp3', 'description': '不可使用,仅供参考:The highest estimation of cash flow per share - upcoming 3 quarters'} +{'id': 62533, 'data_set_name': '可以使用:anl69_cpss_expected_report_dt', 'description': '不可使用,仅供参考:Expected Earnings Report Date'} +{'id': 62645, 'data_set_name': '可以使用:anl69_ndebt_expected_report_time', 'description': '不可使用,仅供参考:Expected Earnings Report Time'} +{'id': 139066, 'data_set_name': '可以使用:short_term_creditors', 'description': '不可使用,仅供参考:Amount of short-term creditors at the reporting date.'} +{'id': 140834, 'data_set_name': '可以使用:fnd72_s_pit_or_bs_q_bs_total_avail_line_of_credit', 'description': '不可使用,仅供参考:Total remaining amount of the lines of credit'} +{'id': 137150, 'data_set_name': '可以使用:fnd17_qpehigh', 'description': '不可使用,仅供参考:P/E excluding extraordinary items high, most recent quarter'} +{'id': 140171, 'data_set_name': '可以使用:fnd31_qsg5additionalfactor3_monthly_rev6fy2', 'description': "不可使用,仅供参考:Averaged Last 6-M EPS Revisions for FY2. It is defined as the average of prior 6-month monthly changes in a stock's consensus analysts earnings forecasts for fiscal year 2, scaled by previous month-end trading price."} +{'id': 137406, 'data_set_name': '可以使用:ttm_earnings_before_tax_value', 'description': '不可使用,仅供参考:Earnings before taxes for the trailing twelve months.'} +{'id': 140791, 'data_set_name': '可以使用:fnd72_s_pit_or_bs_q_bs_accrued_income_taxes', 'description': "不可使用,仅供参考:This field includes all taxes payable as disclosed under the company's current liabilities"} +{'id': 140709, 'data_set_name': '可以使用:fnd72_pit_or_is_q_is_net_non_oper_loss', 'description': '不可使用,仅供参考:Net Non-Operating Losses'} +{'id': 139020, 'data_set_name': '可以使用:quick_assets_total', 'description': '不可使用,仅供参考:Total value of quick assets, typically cash and equivalents.'} +{'id': 60915, 'data_set_name': '可以使用:anl14_low_cfps_fp4', 'description': '不可使用,仅供参考:The Lowest Estimation of Cash Flow Per Share - upcoming 4 quarters'} +{'id': 60799, 'data_set_name': '可以使用:anl14_high_ebitda_fy5', 'description': '不可使用,仅供参考:The Highest Estimation of Earnings Before Interest, Taxes, Depreciation & Amortization - upcoming 5 years'} +{'id': 137453, 'data_set_name': '可以使用:accumulated_provision_depreciation_assets', 'description': '不可使用,仅供参考:[Quarterly] Provision for Doubtful Accounts'} +{'id': 139031, 'data_set_name': '可以使用:sales_revenue_total', 'description': '不可使用,仅供参考:Total revenue from sales for the period.'} +{'id': 373346, 'data_set_name': '可以使用:pv173_rawratiostilt60dema60d', 'description': '不可使用,仅供参考:It is defined as the 60-day exponential average of average factor loading of the second principal component for the z-spread between 6 months and 40 years'} +{'id': 62138, 'data_set_name': '可以使用:quarterly_quick_ratio_2', 'description': '不可使用,仅供参考:Quick ratio for the most recent quarter.'} +{'id': 60603, 'data_set_name': '可以使用:anl11_emp_totalcorregsubsecperc', 'description': '不可使用,仅供参考:SubSector Percentile on EMP Hybrid Score with 100% being the best'} +{'id': 60798, 'data_set_name': '可以使用:anl14_high_ebitda_fy4', 'description': '不可使用,仅供参考:The highest estimation of Earnings Before Interest, Taxes, Depreciation & Amortization - upcoming 4 years'} +{'id': 140898, 'data_set_name': '可以使用:fnd72_s_pit_or_is_q_is_invest_income', 'description': '不可使用,仅供参考:Investment Income'} +{'id': 137106, 'data_set_name': '可以使用:fnd17_pehigh', 'description': '不可使用,仅供参考:P/E excluding extraordinary items, High'} +{'id': 60826, 'data_set_name': '可以使用:anl14_high_ndebt_fp4', 'description': '不可使用,仅供参考:The Highest Estimation of Net Debt - upcoming 4 quarters'} +{'id': 137224, 'data_set_name': '可以使用:fnd17_siratio2', 'description': '不可使用,仅供参考:Short Interest Ratio - 1 month ago'} +{'id': 137472, 'data_set_name': '可以使用:annual_statement_of_cash_flows', 'description': '不可使用,仅供参考:Annual statement of cash flows for the period.'} +{'id': 60795, 'data_set_name': '可以使用:anl14_high_ebitda_fy1', 'description': '不可使用,仅供参考:The Highest Estimation of Earnings Before Interest, Taxes, Depreciation & Amortization - upcoming year'} +{'id': 62656, 'data_set_name': '可以使用:anl69_net_expected_report_dt', 'description': '不可使用,仅供参考:Expected Earnings Report Date'} +{'id': 62083, 'data_set_name': '可以使用:quarterly_long_term_debt_per_share_3', 'description': '不可使用,仅供参考:Long-term debt divided by shares outstanding for the quarter.'} +{'id': 137316, 'data_set_name': '可以使用:net_income_annual', 'description': '不可使用,仅供参考:Net income for the most recent fiscal year after all expenses and taxes.'} +{'id': 140851, 'data_set_name': '可以使用:fnd72_s_pit_or_cf_q_cf_cash_paid_for_tax', 'description': '不可使用,仅供参考:Cash Paid for Taxes'} +{'id': 140644, 'data_set_name': '可以使用:fnd72_pit_or_is_a_is_net_inc_avail_com_shrhldrs', 'description': '不可使用,仅供参考:Net Income Available To Common Shareholders'} +{'id': 139123, 'data_set_name': '可以使用:stockholders_other_paid_in_capital', 'description': '不可使用,仅供参考:Other paid-in capital attributable to stockholders.'} +{'id': 140407, 'data_set_name': '可以使用:fnd72_cf_disp_fix_asset', 'description': '不可使用,仅供参考:Disposal of Fixed Assets'} +{'id': 139137, 'data_set_name': '可以使用:trade_payables_short_term', 'description': '不可使用,仅供参考:Short-term trade payables owed by the company.'} +{'id': 140132, 'data_set_name': '可以使用:fnd31_devnorthamericaadditionalfactor4_rev6fy2', 'description': "不可使用,仅供参考:Averaged Last 6-M EPS Revisions for FY2. It is defined as the average of prior 6-month monthly changes in a stock's consensus analysts' earnings forecasts for fiscal year 2, scaled by previous month-end trading price."} +{'id': 136539, 'data_set_name': '可以使用:annual_normalized_net_income_to_common', 'description': '不可使用,仅供参考:Normalized net income available to common shareholders for the year, excluding unusual items.'} +{'id': 62136, 'data_set_name': '可以使用:quarterly_price_to_sales_ratio_3', 'description': '不可使用,仅供参考:Price divided by sales per share for the quarter.'} +{'id': 140866, 'data_set_name': '可以使用:fnd72_s_pit_or_cf_q_cf_other_inv_act', 'description': '不可使用,仅供参考:Other Investing Activities'} +{'id': 60932, 'data_set_name': '可以使用:anl14_low_ebit_fp3', 'description': '不可使用,仅供参考:The Lowest Estimation of Earnings Before Interest & Taxes - Upcoming 3 Quarters'} +{'id': 138943, 'data_set_name': '可以使用:long_term_minority_interest', 'description': '不可使用,仅供参考:Long-term minority interest in consolidated subsidiaries.'} +{'id': 140565, 'data_set_name': '可以使用:fnd72_pit_or_cf_a_cf_net_chg_st_invest', 'description': '不可使用,仅供参考:The net change in marketable securities and other short-term investments'} +{'id': 137474, 'data_set_name': '可以使用:assets_total_3', 'description': '不可使用,仅供参考:Total assets held by the company at period end.'} +{'id': 139289, 'data_set_name': '可以使用:fnd28_bsassetq_value_02649q', 'description': '不可使用,仅供参考:value of quarterly field: Total Intangible Other Assets - Net'} +{'id': 62084, 'data_set_name': '可以使用:quarterly_long_term_debt_to_assets', 'description': '不可使用,仅供参考:Ratio of long-term debt to total assets for the quarter.'} +{'id': 62534, 'data_set_name': '可以使用:anl69_cpss_expected_report_time', 'description': '不可使用,仅供参考:Expected Earnings Report Time'} +{'id': 137107, 'data_set_name': '可以使用:fnd17_pelow', 'description': '不可使用,仅供参考:P/E excluding extraordinary items, Low'} +{'id': 139084, 'data_set_name': '可以使用:short_term_loans_debt', 'description': '不可使用,仅供参考:Short-term loans and debt reported for the interim period.'} +{'id': 136706, 'data_set_name': '可以使用:fnd17_alldelay1_arevstrt', 'description': '不可使用,仅供参考:Reinvestment Rate - most recent fiscal year'} +{'id': 62204, 'data_set_name': '可以使用:quarterly_total_liabilities', 'description': '不可使用,仅供参考:Total liabilities reported for the most recent quarter.'} +{'id': 137698, 'data_set_name': '可以使用:fnd23_curr_assets', 'description': '不可使用,仅供参考:current assets'} +{'id': 139067, 'data_set_name': '可以使用:short_term_currency_instruments', 'description': '不可使用,仅供参考:Short-term currency instruments held by the company.'} +{'id': 60754, 'data_set_name': '可以使用:anl14_high_capex_fp3', 'description': '不可使用,仅供参考:The Highest Estimation of Capital Expenditures - upcoming 3 quarters'} +{'id': 60990, 'data_set_name': '可以使用:anl14_low_ntp_fy4', 'description': '不可使用,仅供参考:The lowest estimation of net profit - upcoming 4 years'} +{'id': 62305, 'data_set_name': '可以使用:anl44_2_netprofit_rep_coveredby', 'description': '不可使用,仅供参考:netprofit rep coveredby'} +{'id': 140646, 'data_set_name': '可以使用:fnd72_pit_or_is_a_is_net_non_oper_loss', 'description': '不可使用,仅供参考:Net Non-Operating Losses'} +{'id': 139058, 'data_set_name': '可以使用:short_term_bank_investment_total', 'description': '不可使用,仅供参考:Total short-term bank investments at period end.'} +{'id': 137326, 'data_set_name': '可以使用:normalized_net_income_annual', 'description': '不可使用,仅供参考:Net income for the most recent fiscal year, excluding unusual or one-time items.'} +{'id': 61988, 'data_set_name': '可以使用:quarterly_capital_spending_per_share', 'description': '不可使用,仅供参考:Capital spending divided by average shares for the quarter.'} +{'id': 62482, 'data_set_name': '可以使用:anl69_best_px_cps_ratio', 'description': '不可使用,仅供参考:P/CF'} +{'id': 140166, 'data_set_name': '可以使用:fnd31_qsg5additionalfactor3_monthly_pbwt', 'description': '不可使用,仅供参考:Time Weighted Book Yield. It is defined as the time-weighted average of book value per share estimates in fiscal year 1 and fiscal year 2 divided by close price.'} +{'id': 61991, 'data_set_name': '可以使用:quarterly_cash', 'description': '不可使用,仅供参考:Total cash held by the company for the most recent quarter.'} +{'id': 140447, 'data_set_name': '可以使用:fnd72_pit_or_bs_a_bs_other_lt_liabilities', 'description': '不可使用,仅供参考:Other LT Liabilities'} +{'id': 61997, 'data_set_name': '可以使用:quarterly_cash_flow_per_share_alt', 'description': '不可使用,仅供参考:Alternative calculation of cash flow per share for the quarter.'} +{'id': 373671, 'data_set_name': '可以使用:pv96_eqy_split_ratio', 'description': '不可使用,仅供参考:This number describes the rate at which the company will be dividing their current shares outstanding'} +{'id': 137528, 'data_set_name': '可以使用:equity_total_reported', 'description': '不可使用,仅供参考:Total equity as reported in the financial statements.'} +{'id': 60911, 'data_set_name': '可以使用:anl14_low_capex_fy5', 'description': '不可使用,仅供参考:The Lowest Estimation of Capital Expenditures - upcoming 5 years'} +{'id': 62175, 'data_set_name': '可以使用:quarterly_shareholder_equity', 'description': '不可使用,仅供参考:Sum of all equity line items for the most recent quarter.'} +{'id': 137517, 'data_set_name': '可以使用:earnings_from_associates', 'description': '不可使用,仅供参考:[Quarterly] Research & Development'} +{'id': 139015, 'data_set_name': '可以使用:quarterly_total_liabilities_equity_2', 'description': '不可使用,仅供参考:Total liabilities and equity at the end of the quarter.'} +{'id': 140498, 'data_set_name': '可以使用:fnd72_pit_or_bs_q_bs_future_min_oper_lease_oblig', 'description': '不可使用,仅供参考:Total of the Future Minimum Operating Lease Commitments'} +{'id': 62554, 'data_set_name': '可以使用:anl69_dps_expected_report_dt', 'description': '不可使用,仅供参考:Expected Earnings Report Date'} +{'id': 373524, 'data_set_name': '可以使用:top1500_pca_factor2_grouping50', 'description': '不可使用,仅供参考:Second principal component grouping for top 1500 securities with 50 clusters.'} +{'id': 136378, 'data_set_name': '可以使用:cash_incentive_plan_value_ltip', 'description': '不可使用,仅供参考:Value of any cash-based incentive plan held by the executive for LTIP compensation.'} +{'id': 137442, 'data_set_name': '可以使用:accrued_assets_total', 'description': '不可使用,仅供参考:Total accrued assets as of the reporting date.'} +{'id': 62106, 'data_set_name': '可以使用:quarterly_net_loans_2', 'description': '不可使用,仅供参考:Net loans for the most recent quarter.'} +{'id': 137327, 'data_set_name': '可以使用:normalized_net_income_available_to_common_annual', 'description': '不可使用,仅供参考:Net income available to common shareholders for the most recent fiscal year, excluding unusual or one-time items.'} +{'id': 60890, 'data_set_name': '可以使用:anl14_high_roe_fy4', 'description': '不可使用,仅供参考:The Highest Estimation of Returns on Equity - upcoming 4 years'} +{'id': 140868, 'data_set_name': '可以使用:fnd72_s_pit_or_cf_q_cf_other_non_cash_adjust', 'description': '不可使用,仅供参考:Other Non-Cash Adjustments'} +{'id': 140173, 'data_set_name': '可以使用:fnd31_qsg5additionalfactor3_monthly_tw_ebitdaev', 'description': '不可使用,仅供参考:Time Weighted EBITDA/EV. It is defined as the time-weighted EBITDA/Enterprise Value for FY1 and FY2.'} +{'id': 140865, 'data_set_name': '可以使用:fnd72_s_pit_or_cf_q_cf_other_fnc_act', 'description': '不可使用,仅供参考:Other Financing Activities'} +{'id': 137413, 'data_set_name': '可以使用:twelve_month_high_price', 'description': '不可使用,仅供参考:Highest price traded in the last twelve months.'} +{'id': 60583, 'data_set_name': '可以使用:anl11_e_totalcorregsectrnk', 'description': '不可使用,仅供参考:Sector Rank on E Hybrid Score with 1 being the highest rank'} +{'id': 62002, 'data_set_name': '可以使用:quarterly_cash_flow_per_share_nonannualized_prior', 'description': '不可使用,仅供参考:Cash flow per share for the quarter, not annualized, for the same quarter last year.'} +{'id': 138896, 'data_set_name': '可以使用:income_from_discontinued_operations', 'description': '不可使用,仅供参考:[Quarterly] Interest Cost - Domestic'} +{'id': 137002, 'data_set_name': '可以使用:fnd17_arevps', 'description': '不可使用,仅供参考:Revenue/share - most recent fiscal year'} +{'id': 60819, 'data_set_name': '可以使用:anl14_high_epsrep_fy5', 'description': '不可使用,仅供参考:The highest estimation of reported earnings per share - upcoming 5 years'} +{'id': 62105, 'data_set_name': '可以使用:quarterly_net_loans', 'description': '不可使用,仅供参考:Net loans reported for the most recent quarter.'} +{'id': 60840, 'data_set_name': '可以使用:anl14_high_ntp_fy4', 'description': '不可使用,仅供参考:The highest estimation of net profit - upcoming 4 years'} +{'id': 136831, 'data_set_name': '可以使用:fnd17_alldelay1_qcurratio2', 'description': '不可使用,仅供参考:Current ratio - most recent quarter, 1 year ago'} +{'id': 140452, 'data_set_name': '可以使用:fnd72_pit_or_bs_a_bs_pure_retained_earnings', 'description': '不可使用,仅供参考:Accumulated earnings, earned surplus, or unappropriated profit that has been retained by the company'} +{'id': 137483, 'data_set_name': '可以使用:cash_interest_accrued', 'description': '不可使用,仅供参考:Interest accrued but not yet paid in cash.'} +{'id': 62300, 'data_set_name': '可以使用:anl44_2_netdebt_value', 'description': '不可使用,仅供参考:netdebt value'} +{'id': 62103, 'data_set_name': '可以使用:quarterly_net_income_per_employee_2', 'description': '不可使用,仅供参考:Annualized net income per employee for the most recent quarter.'} +{'id': 139295, 'data_set_name': '可以使用:fnd28_bsassetq_value_18390q', 'description': '不可使用,仅供参考:value of quarterly field: Construction Work in Progress'} +{'id': 62134, 'data_set_name': '可以使用:quarterly_price_to_sales_ratio', 'description': '不可使用,仅供参考:Price divided by sales per share for the quarter.'} +{'id': 62036, 'data_set_name': '可以使用:quarterly_earnings_before_tax', 'description': '不可使用,仅供参考:Pretax income for the most recent quarter.'} +{'id': 139263, 'data_set_name': '可以使用:fnd28_bsasseta_value_02149a', 'description': '不可使用,仅供参考:value of annual field: Other Current Assets'} +{'id': 140510, 'data_set_name': '可以使用:fnd72_pit_or_bs_q_bs_other_cur_liab', 'description': '不可使用,仅供参考:Includes customer deposits, short-term deferred income tax liabilities, refunds due to customers, and other current liabilities'} +{'id': 140159, 'data_set_name': '可以使用:fnd31_qsg5additionalfactor3_monthly_divgp', 'description': '不可使用,仅供参考:Shareholder Payout to Gross Profit. It is defined as total dividends paid divided by (revenue - cost of goods sold).'} +{'id': 138961, 'data_set_name': '可以使用:net_income_current_period', 'description': '不可使用,仅供参考:Net income for the current reporting period.'} +{'id': 137375, 'data_set_name': '可以使用:quarterly_total_shareholders_equity', 'description': "不可使用,仅供参考:Total shareholders' equity reported for the most recent quarter."} +{'id': 60936, 'data_set_name': '可以使用:anl14_low_ebit_fy2', 'description': '不可使用,仅供参考:The lowest estimation of earnings before interest & taxes - upcoming 2 years'} +{'id': 60785, 'data_set_name': '可以使用:anl14_high_ebit_fy1', 'description': '不可使用,仅供参考:The Highest Estimation of Earnings Before Interest & Taxes - Upcoming Year'} +{'id': 62200, 'data_set_name': '可以使用:quarterly_total_debt_to_equity_prior_year', 'description': '不可使用,仅供参考:Total debt divided by shareholder equity for the same quarter last year.'} +{'id': 140913, 'data_set_name': '可以使用:fnd72_s_pit_or_is_q_net_income', 'description': '不可使用,仅供参考:Net Income'} +{'id': 60833, 'data_set_name': '可以使用:anl14_high_ntp_fp2', 'description': '不可使用,仅供参考:The highest estimation of Net Profit - upcoming 2 quarters'} +{'id': 60823, 'data_set_name': '可以使用:anl14_high_ndebt_fp1', 'description': '不可使用,仅供参考:The Highest Estimation of Net Debt - upcoming quarter'} +{'id': 62308, 'data_set_name': '可以使用:anl44_2_netprofit_rep_prevalue', 'description': '不可使用,仅供参考:netprofit rep prevalue'} +{'id': 138897, 'data_set_name': '可以使用:income_tax_expense_3', 'description': '不可使用,仅供参考:[Quarterly] Total Extraordinary Items'} +{'id': 62461, 'data_set_name': '可以使用:anl69_best_net_4wk_dn', 'description': '不可使用,仅供参考:Net Inc 4 Wk Down'} +{'id': 373431, 'data_set_name': '可以使用:resampled_pca_factor_14', 'description': '不可使用,仅供参考:Fourteenth principal component factor derived from robust resampling of returns for top 2000 securities.'} +{'id': 140163, 'data_set_name': '可以使用:fnd31_qsg5additionalfactor3_monthly_mpgghcy3', 'description': '不可使用,仅供参考:3 Year Change in Gross Profit Margin. It is defined as the 3-year-on-year change in gross profit margin.'} +{'id': 140118, 'data_set_name': '可以使用:fnd31_devnorthamericaadditionalfactor4_apgghcy1', 'description': '不可使用,仅供参考:1-Year Change in Gross Profit to Assets. It is defined as the Year on Year Change in Gross Profit to Assets.'} +{'id': 60771, 'data_set_name': '可以使用:anl14_high_div_fp2', 'description': '不可使用,仅供参考:The highest estimation of dividend - upcoming 2 quarters'} +{'id': 373570, 'data_set_name': '可以使用:pv37_high_global', 'description': '不可使用,仅供参考:Global Highest Price'} +{'id': 62141, 'data_set_name': '可以使用:quarterly_quick_ratio_prior', 'description': '不可使用,仅供参考:Quick ratio for the same quarter last year.'} +{'id': 138889, 'data_set_name': '可以使用:goat_accumulated_other_comprehensive_income', 'description': '不可使用,仅供参考:Accumulated other comprehensive income for GOAT at the reporting date.'} +{'id': 373432, 'data_set_name': '可以使用:resampled_pca_factor_2', 'description': '不可使用,仅供参考:Second principal component factor derived from robust resampling of returns for top 2000 securities.'} +{'id': 139057, 'data_set_name': '可以使用:short_term_bank_debt_amount', 'description': '不可使用,仅供参考:Amount of short-term bank debt outstanding.'} +{'id': 60963, 'data_set_name': '可以使用:anl14_low_epsrep_fp4', 'description': '不可使用,仅供参考:The Lowest Estimation of Reported Earnings Per Share - upcoming 4 quarters'} +{'id': 140123, 'data_set_name': '可以使用:fnd31_devnorthamericaadditionalfactor4_mpg', 'description': '不可使用,仅供参考:TTM Gross Profit Margin. It is defined as the TTM (Sales-COGS)/Sales.'} +{'id': 140120, 'data_set_name': '可以使用:fnd31_devnorthamericaadditionalfactor4_divgp', 'description': '不可使用,仅供参考:Shareholder Payout to Gross Profit. It is defined as total dividends paid divided by (revenue - cost of goods sold).'} +{'id': 140850, 'data_set_name': '可以使用:fnd72_s_pit_or_cf_q_cf_cash_from_oper', 'description': '不可使用,仅供参考:Total amount of cash a company generates from its operation'} +{'id': 136535, 'data_set_name': '可以使用:annual_net_income_incl_extraordinary', 'description': '不可使用,仅供参考:Net income for the year, including extraordinary items.'} +{'id': 62155, 'data_set_name': '可以使用:quarterly_return_on_assets_percent_2', 'description': '不可使用,仅供参考:Annualized net income divided by average assets for the quarter, as a percentage.'} +{'id': 373539, 'data_set_name': '可以使用:top2000_factor3_group2_score', 'description': '不可使用,仅供参考:Third factor score for top 2000 securities, grouped into 2 clusters.'} +{'id': 140178, 'data_set_name': '可以使用:fnd31_qsg5additionalfactor3_monthly_yoychgroepct', 'description': '不可使用,仅供参考:Yearly Change in ROE - Percentage Method. It is defined as the yearly percentage change in the ratio of trailing-twelve-month earnings before extraordinary items divided by current total book equity.'} +{'id': 62034, 'data_set_name': '可以使用:quarterly_dividend_per_share_2', 'description': '不可使用,仅供参考:Dividend per share for the most recent quarter.'} +{'id': 140161, 'data_set_name': '可以使用:fnd31_qsg5additionalfactor3_monthly_fwdebitdaev', 'description': '不可使用,仅供参考:Leading 12-Month EBITDA/EV. It is defined as the FY1 estimate of EBITDA/Enterprise Value.'} +{'id': 136700, 'data_set_name': '可以使用:fnd17_alldelay1_arecturn', 'description': '不可使用,仅供参考:Receivables turnover - most recent fiscal year'} +{'id': 60976, 'data_set_name': '可以使用:anl14_low_ndebt_fp4', 'description': '不可使用,仅供参考:The lowest estimation of net debt - upcoming 4 quarters'} +{'id': 136803, 'data_set_name': '可以使用:fnd17_alldelay1_pehigh', 'description': '不可使用,仅供参考:P/E excluding extraordinary items, High'} +{'id': 136552, 'data_set_name': '可以使用:cash_per_share', 'description': '不可使用,仅供参考:Total cash and short-term investments divided by shares outstanding.'} +{'id': 140164, 'data_set_name': '可以使用:fnd31_qsg5additionalfactor3_monthly_numrevy2', 'description': '不可使用,仅供参考:Net # of Revisions for Fiscal Year 2. It is defined as the weighted average of the number of FY2 analyst earnings forecasts raised less the number lowered within a month, divided by the total number of analyst forecasts.'} +{'id': 60944, 'data_set_name': '可以使用:anl14_low_ebitda_fp5', 'description': '不可使用,仅供参考:The lowest estimation of Earnings Before Interest, Taxes, Depreciation & Amortization - upcoming 5 quarters'} +{'id': 140566, 'data_set_name': '可以使用:fnd72_pit_or_cf_a_cf_net_chng_cash', 'description': '不可使用,仅供参考:Net Changes in Cash'} +{'id': 62035, 'data_set_name': '可以使用:quarterly_dividend_per_share_3', 'description': '不可使用,仅供参考:Dividend paid per share for the most recent quarter.'} +{'id': 61896, 'data_set_name': '可以使用:anl15_s_cal_fy2_total', 'description': '不可使用,仅供参考:The total of earnings estimation for the calendarized 2 fiscal year aggregated within GICS sector grouping.'} +{'id': 136510, 'data_set_name': '可以使用:fnd13_statementq_dgcd', 'description': '不可使用,仅供参考:Gross Dividends - Common Stock'} +========================= 数据字段结束 ======================================= + +以上数据字段和操作符, 按照Description说明组合, 但是每一个 alpha 组合的使用的数据字段和操作符不要过于集中, 在符合语法的情况下, 多尝试不同的组合 + +你再检查一下, 如果你使用了 +Operator abs does not support event inputs +Operator ts_mean does not support event inputs +Operator ts_scale does not support event inputs +Operator add does not support event inputs +Operator sign does not support event inputs +Operator greater does not support event inputs +Operator ts_av_diff does not support event inputs +Operator ts_quantile does not support event inputs +Operator ts_arg_min does not support event inputs +Operator divide does not support event inputs +Operator ts_corr does not support event inputs +Operator ts_decay_linear does not support event inputs +Operator ts_sum does not support event inputs +Operator ts_delay does not support event inputs +Operator ts_arg_max does not support event inputs +Operator ts_std_dev does not support event inputs +Operator ts_regression does not support event inputs +Operator ts_backfill does not support event inputs +Operator signed_power does not support event inputs +Operator ts_product does not support event inputs +Operator ts_zscore does not support event inputs +Operator group_rank does not support event inputs +Operator subtract does not support event inputs +Operator ts_delta does not support event inputs +Operator ts_rank does not support event inputs +Operator ts_count_nans does not support event inputs +Operator ts_covariance does not support event inputs +Operator multiply does not support event inputs +Operator if_else does not support event inputs +Operator group_neutralize does not support event inputs +Operator group_zscore does not support event inputs +Operator winsorize does not support event inputs +注意, 以上操作符不能使用事件类型的数据集, 以上操作符禁止使用事件类型的数据集!! \ No newline at end of file diff --git a/planning_post_alpha.txt b/planning_post_alpha.txt index a029834..f82889a 100644 --- a/planning_post_alpha.txt +++ b/planning_post_alpha.txt @@ -1,500 +1,501 @@ -ts_covariance(fnd23_annfv1a_nimv, pv103_lasz_mean, 5) -ts_covariance(earning_fiscal_year_end, pv64_out_stal_fund_creation_unit_size, 5) -ts_covariance(mdl77_2mqf_niper, pv64_dif_fund_creation_unit_size, 20) -ts_covariance(quarterly_retained_earnings_total, pv87_interval_asksize_median, 5) -ts_covariance(pv87_weightedavg60_group_ess_earnings, pv81_stlh, 20) -ts_covariance(mdl77_2gdna_yoychgaa, mdl177_2_liquidityriskfactor_ohlsonscore, 250) -ts_covariance(fnd72_pit_or_is_a_is_interest_inc, pv98_daily_mean_taks_taks_mean, 20) -ts_covariance(fnd28_wsbshtq_value_01551q, pv64_trr_stal_cur_mkt_cap, 20) -ts_covariance(mdl177_v1_400_sue, pv64_dif_stal_fund_creation_unit_size, 5) -ts_covariance(earning_time_type, pv104_lasz_mean, 5) -ts_covariance(anl10_ebismun_2qf_2231, pv52_yse_national_order_size_code, 250) -ts_covariance(mdl264_2l_qic, pv104_tasz_mean, 5) -ts_covariance(mdl262_rasv2splitprofitabilityebitd_q_profitability9, pv87_interval_asksize_median, 60) -ts_covariance(anl14_stddev_opp_fy1, oth551_resret_size, 250) -ts_covariance(mdl116_epsyld_fy2_trendpredict_model_mada, fnd72_s_pit_or_cr_q_debt_to_mkt_cap, 20) -ts_covariance(fnd65_us5000_cusip_fqsurstd60dlag, pv81_sasl, 250) -ts_covariance(mdl262_deltapredictni_a_mae, pv87_interval_asksizeinterval_bidsize_correlation, 20) -ts_covariance(pv20_ebt_indicator_n_feature4, pv87_interval_bidsize_std, 5) -ts_covariance(anl10_ebspast_det_excflag, pv104_masz_mean, 5) -ts_covariance(pv20_eps_indicator_4_feature2, pv104_hasz_mean, 20) -ts_covariance(fnd65_us5000_cusip_pfcmtt, pv103_masz_mean, 5) -ts_covariance(mdl262_trkdpitpredictiveebitda_prediction, oth551_r2_size, 250) -ts_covariance(fnd28_ishta_value_01751a, pv64_dif_fund_creation_unit_size, 20) -ts_covariance(est_12m_pre_mean_3mth_ago, pv64_dif_stal_fund_creation_unit_size, 5) -ts_covariance(anl10_ebsfq1_consensus, oth551_r2_size, 250) -ts_covariance(fnd23_icsm_mvb_fbbs, pv103_sasz_mean, 5) -ts_covariance(max_ebit_guidance, mdl230_allcap_sedol_ohlsonscore, 20) -ts_covariance(anl44_orig_en_pretaxprofit_rep_prevalue, pv81_zsbt, 250) -ts_covariance(mdl77_2liquidityriskfactor_impduration, pv103_tlrt_topofbook_tlrt_mean, 60) -ts_covariance(annual_retained_earnings_total_fast_d1, pv103_maus_mean, 60) -ts_covariance(mdl264_usa_compustat_q1_dl8_miiq_l2, pv103_maus_mean, 250) -ts_covariance(anl10_gpspast_det_analyst_1321, pv81_zsbl, 5) -ts_covariance(mdl230_us5000_cusip_pspeghc, pv52_yse_arca_order_size_code, 60) -ts_covariance(anl82_opry_profitability_profitability12, pv87_interval_bidsizeinterval_slippage_rankcorrel, 250) -ts_covariance(anl10_ebsnormal_decrease_fy2, pv64_dif_fund_creation_unit_size, 60) -ts_covariance(mdl264_epspi_class, fnd72_s_pit_or_cr_q_debt_to_mkt_cap, 5) -ts_covariance(anl14_stddev_ntp_fp1, pv87_interval_asksize_std, 20) -ts_covariance(earnings_announcement_time_of_day_fast_d1, pv52_asdaq_bx_order_size_code, 20) -ts_covariance(pv87_epsyld_gro_mean, pv103_1m_mbds_mean, 20) -ts_covariance(fnd65_totalcap_cusip_mad3yttmni, pv87_interval_asksize_skewness, 60) -ts_covariance(anl15_ebtgics_ind_12_m_1m_chg, pv81_trlm, 20) -ts_covariance(fnd72_pit_or_cr_a_net_inc_growth, pv52_yse_national_order_size_code, 20) -ts_covariance(anl69_eps_best_eps_lo, pv103_1m_mlrt_mean, 5) -ts_covariance(pv87_2_epsr_qf_matrix_all_high, pv64_trr_stal_cur_mkt_cap, 60) -ts_covariance(mdl230_totalcap_cusip_rev6fy2, pv81_zsbl, 5) -ts_covariance(fnd28_wcfsq1nbf_value_01401q, mdl248_avg_txn_val, 20) -ts_covariance(mdl177_2_globaldevnorthamerica_v502_fc_epsrm, pv52_asdaq_order_size_code, 250) -ts_covariance(fnd65_totalcap_cusip_mpgghcy3_, pv81_zsbt, 5) -ts_covariance(mdl26_p_yld_pct_stm_fy1, pv104_hbsz_mean, 60) -ts_covariance(mdl230_totalcap_cusip_epschgetr, oth551_resret_size, 20) -ts_covariance(anl69_sales_best_eeps_nxt_yr, pv103_1m_maks_mean, 60) -ts_covariance(anl82_ebty_deltaprofitability_profitability11, fnd72_pit_or_cr_q_debt_to_mkt_cap, 20) -ts_covariance(anl15_cpsgics_s_18_m_pe, pv64_trr_cur_mkt_cap, 5) -ts_covariance(mdl109_d_mtl_dlyspe, pv104_tbsz_mean, 5) -ts_covariance(fnd3_q_ebitda, pv87_interval_bidsizeinterval_volume_correlation, 250) -ts_covariance(anl10_nerpast_det_indicator_913, pv64_dif_fund_creation_unit_size, 5) -ts_covariance(anl4_epsr_value, pv103_mbus_mean, 20) -ts_covariance(anl15_ebt_ind_cal_fy3_6m_chg, fnd65_us5000_cusip_ohlsonscore, 250) -ts_covariance(anl10_epsinnovation_score_fy1, pv103_tbsz_mean, 5) -ts_covariance(mdl264_usa_compustat_q1_dl8_nopiq_l4, pv64_trr_stal_cur_mkt_cap, 250) -ts_covariance(anl49_1stfiscalquarterearningspershare, pv87_interval_bidsize_mean, 250) -ts_covariance(pv87_2_pretaxprofit_rep_af_matrix_p1_chngratio_number, pv87_interval_bidsize_skewness, 250) -ts_covariance(pv87_daily_qtr_matrix_net_income_gaap_consensus_mean_numdown, pv87_interval_asksizeinterval_bidsize_correlation, 20) -ts_covariance(anl10_entfy1_pred_surps_v1_2442, pv87_interval_bidsize_median, 20) -ts_covariance(count_negative_profitability_question, pv104_masz_mean, 60) -ts_covariance(fnd13_cinx, fund_total_size, 20) -ts_covariance(fnd28_annualgrowth_value_08601a, pv81_zsbl, 5) -ts_covariance(mdl26_v14_srprs_pct_lst_y_rnngs, pv87_interval_asksizeinterval_slippage_correlation, 250) -ts_covariance(anl10_gpssmun_1qf_2624, pv103_mbsz_mean, 5) -ts_covariance(fnd72_s_pit_or_cr_q_trail_12m_inc_bef_xo_item, pv103_tbds_mean, 20) -ts_covariance(fnd7_ointfund_qipspe, pv52_yse_arca_order_size_code, 20) -ts_covariance(mdl262_eps_oeps12_mae, pv104_masz_mean, 5) -ts_covariance(pv87_net_income_normalized_of_estimates_scale, pv87_interval_asksize_mean, 5) -ts_covariance(pv20_indicator_4_feature8, mdl77_liquidityriskfactor_ohlsonscore, 20) -ts_covariance(anl10_nersmun_1yf_2417, pv87_interval_bidsize_std, 60) -ts_covariance(anl15_dps_gr_cal_fy1_pe, oth551_r2_size, 5) -ts_covariance(mdl262_rasv2splitprofitabilityebitd_q_profitability10, pv103_1m_maks_mean, 250) -ts_covariance(mdl77_2valuemomemtummodel_reportedearningsmomentummodule, company_size_sentiment_score, 250) -ts_covariance(star_eq_ope_rank, pv98_daily_mean_taks_taks_mean, 5) -ts_covariance(anl10_nerrevise_ratio_to_consensus_fq2_2016, pv103_sbsz_mean, 20) -ts_covariance(earning_time_type_fast_d1, pv103_hbsz_mean, 20) -ts_covariance(pv87_2_pretaxprofit_qf_matrix_all_chngratio_high, pv104_tbsz_mean, 250) -ts_covariance(oth460_usa_compustat_q1_dl8_nopiq_l4, pv103_mlrt_topofbook_mlrt_mean, 250) -ts_covariance(mdl262_che_profitability_profitability9, pv104_mlrt_mean, 5) -ts_covariance(pv87_net_income_normalized_actual, pv103_tlrt_topofbook_tlrt_mean, 20) -ts_covariance(est_q_eps_std_3mth_ago, pv87_interval_bidsize_skewness, 250) -ts_covariance(anl10_ebsfy2_pred_surps_v0, pv87_interval_asksizeinterval_bidsize_rankcorrel, 60) -ts_covariance(anl10_epsinnovate_decrease_fq2, pv52_yse_national_order_size_code, 20) -ts_covariance(mdl26_trailing_4_quarter_pe, pv103_mbsz_mean, 20) -ts_covariance(anl44_netprofit_gaap_best_cur_fiscal_qtr_period, company_offer_total_size, 60) -ts_covariance(est_12m_opr_std, pv87_interval_asksizeinterval_slippage_rankcorrel, 20) -ts_covariance(anl15_ebtgics_gr_cal_fy2_gro, oth551_r2_size, 60) -ts_covariance(mdl262_cheq_profitability_profitability2, pv52_yse_national_order_size_code, 5) -ts_covariance(long_term_dividend_payout_ratio, company_offer_total_size, 60) -ts_covariance(anl49_netprofit, pv81_trlm, 20) -ts_covariance(anl14_high_eps_fp5, pv103_laks_mean, 60) -ts_covariance(rsk62_beta_5_100_spe, pv87_interval_bidsize_median, 5) -ts_covariance(pv87_pos_earnings_matrix_bee_sum, mdl77_2liquidityriskfactor_ohlsonscore, 20) -ts_covariance(oth432_rasv2splitprofitabilityebt_ttm_profitability5, mdl230_totalcap_cusip_ohlsonscore, 20) -ts_covariance(pv20_gps_indicator_q_feature8, oth551_beta_size, 5) -ts_covariance(mdl262_rasv2splitprofitabilityfcf_q_profitability9, nws31_d1_bodysize, 60) -ts_covariance(fnd2_itxreexftfedstyitxrt, mdl230_allcap_sedol_ohlsonscore, 60) -ts_covariance(pv20_pre_indicator_n_feature10, pv104_tlrt_mean, 250) -ts_covariance(pv87_2_operatingprofit_qf_matrix_all_chngratio_mean, pv81_zsbm, 250) -ts_covariance(fnd6_cptmfmq_opepsq, fnd72_s_pit_or_cr_q_debt_to_mkt_cap, 60) -ts_covariance(pv87_epsyld_ltm_d_mean, pv103_htsz_mean, 60) -ts_covariance(fnd3_Q_interestincome, pv103_sbsz_mean, 5) -ts_covariance(pv20_ebt_indicator_7_feature2, pv104_masz_mean, 60) -ts_covariance(pv87_prv2_expavg60_group_event_sentiment_score_earnings, pv103_hasz_mean, 250) -ts_covariance(mdl26_v14_nnlyst_rvsng_dwn_fq1_rnngs_7, pv64_trr_stal_cur_mkt_cap, 5) -ts_covariance(pv20_ebt_indicator_7_feature6, pv87_interval_asksize_skewness, 60) -ts_covariance(mdl211_saly_deltaprofitability_profitability9, fnd31_ohlsonscore, 250) -ts_covariance(mdl230_us5000_cusip_ebitdadebtchg, fnd31_ohlsonscore, 20) -ts_covariance(pv87_2_eps_af_matrix_p1_chngratio_high, rsk70_mfm2_usfast_size, 20) -ts_covariance(anl14_median_ebit_fy4, pv87_interval_asksize_numintervalsincehigh, 250) -ts_covariance(fnd28_bdea_value_18854a, pv81_sbsl, 60) -ts_covariance(mdl211_preq_profitability_profitability7, company_size_sentiment_score, 5) -ts_covariance(anl10_ebiinnovation_score_fy2_2589, fnd72_pit_or_cr_q_debt_to_mkt_cap, 60) -ts_covariance(pv87_2_netprofit_af_matrix_all_dts, pv103_1m_tlrt_mean, 250) -ts_covariance(fnd65_us5000_cusip_divcov, pv87_interval_bidsizeinterval_slippage_rankcorrel, 5) -ts_covariance(pv87_daily_ann_matrix_r1_net_income_normalized_consensus_mean_numup, pv52_yse_arca_order_size_code, 5) -ts_covariance(anl10_nersmun_2yf_2416, pv87_interval_bidsize_skewness, 60) -ts_covariance(anl4_qfv4_eps_number, pv81_stlh, 5) -ts_covariance(pv20_ebt_indicator_2_feature9, pv104_tasz_mean, 60) -ts_covariance(anl82_preq_q4_madp, pv103_lasz_mean, 250) -ts_covariance(mdl26_rvsnclstr_vg_rvsn_pr_fq1_rnngs, mdl77_liquidityriskfactor_ohlsonscore, 5) -ts_covariance(pe_ratio_relative_component_rank_v2, pv87_interval_asksizeinterval_bidsize_correlation, 20) -ts_covariance(fnd72_s_pit_or_cr_q_com_eqy_to_tot_asset, pv103_tlrt_topofbook_tlrt_mean, 250) -ts_covariance(mdl262_rasv2splitprofitabilityebit_ttm_profitability12, pv52_yse_arca_order_size_code, 250) -ts_covariance(anl15_ebt_gr_cal_fy3_cos, im_shares_transacted, 60) -ts_covariance(pv87_2_pretaxprofit_qf_matrix_all_chngratio_median, pv87_interval_bidsizeinterval_slippage_rankcorrel, 60) -ts_covariance(mdl26_7dy_bld_stmt_flg_fq4_rnngs, pv104_lsbs_mean, 250) -ts_covariance(oth460_ttax_l3, pv87_interval_bidsizeinterval_volume_correlation, 60) -ts_covariance(mdl26_nm_stm_nlysts_fy2_rnngs, mdl230_allcap_sedol_ohlsonscore, 250) -ts_covariance(anl15_ebtgics_ind_18_m_cos_dn, pv103_maus_mean, 60) -ts_covariance(mdl177_2_earningsqualityfactor_salegpm, pv104_lsbs_mean, 5) -ts_covariance(act_12m_prr_value, pv87_interval_bidsize_mean, 60) -ts_covariance(fnd3_A_nic, pv87_interval_asksize_numintervalsincehigh, 60) -ts_covariance(fnd31_devnorthamericaadditionalfactor4_divgp, pv81_zsbt, 250) -ts_covariance(fnd3_aacctadj__earbeforetaxes, pv103_tasz_mean, 250) -ts_covariance(anl49_backfill_estdcurrentperatio, fnd72_s_pit_or_cr_q_debt_to_mkt_cap, 60) -ts_covariance(anl10_nerrevise_value_fq1_2012, pv87_interval_asksizeinterval_slippage_correlation, 60) -ts_covariance(anl15_ebt_gr_18_m_cos_dn, pv104_hbsz_mean, 60) -ts_covariance(anl82_delta_preq_q4_predict, pv81_zsbt, 250) -ts_covariance(anl14_stddev_ebit_fp1, pv81_sasl, 60) -ts_covariance(mdl77_historicalgrowthfactor_pspeghc, oth567_deltascore_company_size_432, 20) -ts_covariance(anl14_high_ntprep_fy3, mdl230_us5000_cusip_ohlsonscore, 5) -ts_covariance(pv20_sal_indicator_2_feature12, mdl77_2liquidityriskfactor_ohlsonscore, 60) -ts_covariance(mdl177_liquidityriskfactor_mad3yttmni_alt, company_offer_total_size, 60) -ts_covariance(oth432_ebit_compustatdeltapredict_annual_funda_predict, pv103_mbsz_mean, 5) -ts_covariance(quarterly_earnings_before_tax_total, pv103_htsz_mean, 20) -ts_covariance(pv87_2_operatingprofit_af_matrix_p1_dts, pv98_tbds_mean, 60) -ts_covariance(fnd28_newa1_value_08385a, pv103_1m_mbds_mean, 250) -ts_covariance(mdl26_lw_stmt_chng_fy1_rnngs_30, pv87_interval_bidsize_mean, 5) -ts_covariance(pv20_eps_indicator_8_feature10, pv81_trlt, 250) -ts_covariance(pv20_opr_indicator_o_feature11, pv87_interval_asksize_std, 60) -ts_covariance(act_q_gps_surprisestd, fund_total_size, 5) -ts_covariance(pv20_net_indicator_q_feature7, pv103_laks_mean, 250) -ts_covariance(mdl230_allcap_sedol_aor, pv81_trlt, 250) -ts_covariance(mdl177_2_globaldevnorthamerica_v502_wcacc, pv87_interval_asksize_mean, 5) -ts_covariance(fnd65_allcap_sedol_bmpo, pv103_masz_mean, 250) -ts_covariance(fnd7_ointfund_qiim, pv103_sbps_mean, 250) -ts_covariance(fnd65_totalcap_cusip_rev3my1std, mdl248_avg_txn_val, 5) -ts_covariance(fnd28_wcfsa1_value_01503a, mdl230_allcap_sedol_ohlsonscore, 250) -ts_covariance(anl15_ebtgics_ind_cal_fy1_6m_chg, company_offer_total_size, 250) -ts_covariance(anl14_median_ntp_fy5, pv81_zsah, 250) -ts_covariance(mdl177_valueanalystmodel_qva_earnval_alt, oth137_avg_txn_val, 250) -ts_covariance(pv87_webv2_expavg20_group_css_earnings, mdl177_2_liquidityriskfactor_ohlsonscore, 60) -ts_covariance(fnd72_s_pit_or_bs_q_2_bs_other_cur_asset_less_prepay, oth567score_company_size, 60) -ts_covariance(mdl77_fa_pge_cf, pv52_yse_order_size_code, 250) -ts_covariance(mdl230_allcap_sedol_perg, fnd14_zs_lf_nlf_drp_ncr, 250) -ts_covariance(anl14_numofests_ntp_fy2, pv87_interval_bidsize_numintervalsincelow, 250) -ts_covariance(oth432_rasv2splitprofitabilityni_ttm_profitability1, pv81_stlh, 250) -ts_covariance(mdl264_usa_compustat_q1_dl8_epspiq_l1, mdl230_us5000_cusip_ohlsonscore, 5) -ts_covariance(mdl77_2mqf_yoychggpm, fnd65_totalcap_cusip_ohlsonscore, 60) -ts_covariance(anl82_delta_netq_q1_predict, pv81_lasz, 250) -ts_covariance(est_12m_gps_std_28d, pv104_lbsz_mean, 20) -ts_covariance(pv20_eps_indicator_1_feature2, pv87_interval_asksize_median, 5) -ts_covariance(fnd72_pit_or_is_q_is_tot_cash_com_dvd, pv87_interval_bidsize_median, 60) -ts_covariance(mdl262_che_profitability_profitability5, pv98_quote_size_mean, 5) -ts_covariance(fnd28_newa2_value_09216a, nws31_d1_bodysize, 60) -ts_covariance(mdl230_us5000_cusip_y5speq4rqsr, pv104_lbsz_mean, 20) -ts_covariance(mdl264_1l_qaeclg, oth567score_company_size_358, 250) -ts_covariance(mdl77_2ccacw, pv98_quote_size_mean, 5) -ts_covariance(mdl264_sani_l3, pv87_interval_bidsize_kurtosis, 60) -ts_covariance(mdl262_cheq_profitability_profitability3, pv103_lbsz_mean, 5) -ts_covariance(anl14_numofests_opp_fp1, fnd14_zs_lf_nlf_drp_ncr, 5) -ts_covariance(pv87_ann_matrix_net_income_gaap_estimate_high, pv52_yse_national_order_size_code, 250) -ts_covariance(mdl109_b_mtl_dlyspe, pv103_1m_mlrt_mean, 60) -ts_covariance(anl4_ptpr_number, pv52_asdaq_psx_order_size_code, 250) -ts_covariance(oth432_ninc_trkdpitdeltapredict_funda_mae, pv81_zsbm, 60) -ts_covariance(fnd17_ttmnichg, pv103_saps_mean, 5) -ts_covariance(est_q_opr_mean_28d, pv103_maks_topofbook_maks_mean, 20) -ts_covariance(anl10_ebiinnovation_score_fy2_2589, fnd65_allcap_sedol_ohlsonscore, 20) -ts_covariance(anl14_low_ntprep_fy2, pv103_laks_mean, 60) -ts_covariance(oth432_che_profitability_profitability2, pv103_sbps_mean, 20) -ts_covariance(fnd72_pit_or_cr_a_trail_12m_oper_inc, pv87_interval_asksizeinterval_bidsize_correlation, 5) -ts_covariance(oth460_usa_compustat_q1_dl8_epsfiq_l5, mdl177_2_liquidityriskfactor_ohlsonscore, 5) -ts_covariance(mdl264_1l_declg, fund_total_size, 5) -ts_covariance(mdl262_compustatpredictivetxtq_prediction, pv104_tbsz_mean, 250) -ts_covariance(pv20_opr_indicator_1_feature1, pv103_sbsz_mean, 60) -ts_covariance(anl44_second_en_netprofit_rep_coveredby, pv103_lasz_mean, 250) -ts_covariance(mdl26_rm02fy_prsprs_fy2_rnngs, pv98_daily_mean_taks_taks_mean, 20) -ts_covariance(pv87_daily_qtr_matrix_net_income_gaap_consensus_mean_numup, pv87_interval_bidsize_std, 20) -ts_covariance(mdl26_rm02fy_prsprs_fy2_rnngs, pv52_asdaq_order_size_code, 250) -ts_covariance(mdl262_deltapredictebitd_q_predict, pv103_mbds_topofbook_mbds_mean, 60) -ts_covariance(anl69_roe_expected_report_time, pv103_tbds_mean, 5) -ts_covariance(fnd72_s_pit_or_is_q_earn_for_common, pv87_interval_asksizeinterval_volume_correlation, 20) -ts_covariance(anl49_vector_depreciationdepletionamortization, pv103_tbsz_mean, 250) -ts_covariance(anl69_eps_best_eps, fnd72_pit_or_cr_a_debt_to_mkt_cap, 60) -ts_covariance(fnd89_major_11_31, pv81_sasl, 5) -ts_covariance(oth401_game_earning_smooth, oth567score_company_size, 60) -ts_covariance(anl49_valugaugeestimatingdifficultyrank, pv87_interval_asksize_std, 60) -ts_covariance(fnd65_us5000_cusip_min1yopmargin, pv81_zsbl, 60) -ts_covariance(anl14_mean_ntp_fy1, mdl177_2_liquidityriskfactor_ohlsonscore, 250) -ts_covariance(mdl230_us5000_cusip_ebitdadebt, pv64_dif_stal_fund_creation_unit_size, 60) -ts_covariance(pv87_2_operatingprofit_qf_matrix_all_low, mdl230_totalcap_cusip_ohlsonscore, 250) -ts_covariance(anl14_mean_ntprep_fp5, pv52_yse_national_order_size_code, 250) -ts_covariance(mdl177_2_earningsqualityfactor_saleeps, nws31_d1_bodysize, 20) -ts_covariance(mdl211_saly_deltaprofitability_profitability9, pv87_interval_bidsizeinterval_slippage_rankcorrel, 5) -ts_covariance(mdl264_viat_l3, mdl230_allcap_sedol_ohlsonscore, 250) -ts_covariance(pv87_2_eps_qf_matrix_all_high, pv87_interval_asksizeinterval_bidsize_rankcorrel, 60) -ts_covariance(fnd23_intfvm_ipos, nws31_bodysize, 5) -ts_covariance(oth460_usa_compustat_q1_dl8_epspxq_l4, pv104_hbsz_mean, 60) -ts_covariance(pv87_2_operatingprofit_af_matrix_p1_chngratio_mean, pv103_sbps_mean, 5) -ts_covariance(pv87_simpleavg20_group_css_earnings, pv87_interval_asksize_std, 60) -ts_covariance(pv20_gps_indicator_n_feature5, mdl77_liquidityriskfactor_ohlsonscore, 60) -ts_covariance(mdl354_sector_pt2net_margin, pv87_interval_asksize_median, 60) -ts_covariance(pv87_2_operatingprofit_qf_matrix_p1_dts, pv81_zsbm, 5) -ts_covariance(mdl77_2spe1yfvc_cf, pv87_interval_bidsizeinterval_slippage_correlation, 250) -ts_covariance(anl15_ebt_ind_cal_fy1_1m_chg, rsk70_mfm2_usfast_size, 5) -ts_covariance(pv20_net_indicator_n_feature3, pv64_dif_fund_creation_unit_size, 250) -ts_covariance(pv87_2_pretaxprofit_rep_af_matrix_all_chngratio_high, pv81_zsbh, 250) -ts_covariance(anl82_oprq_profitability_profitability1, pv52_yse_national_order_size_code, 20) -ts_covariance(oth432_acae_q_profitability_profitability9, pv87_interval_bidsizeinterval_slippage_rankcorrel, 5) -ts_covariance(anl15_ebt_gr_18_m_mean, oth567score_company_size, 250) -ts_covariance(fnd28_wcfsa1_value_01266a, pv103_tbds_mean, 5) -ts_covariance(pv20_net_indicator_q_feature0, company_offer_total_size, 60) -ts_covariance(fnd72_pit_or_cr_q_prof_margin, mdl230_totalcap_cusip_ohlsonscore, 250) -ts_covariance(mdl264_onet_l1, oth137_avg_txn_val, 20) -ts_covariance(mdl211_oprq_deltaprofitability_profitability5, pv87_interval_bidsize_numintervalsincelow, 5) -ts_covariance(est_q_pre_std_4wks_ago, pv52_yse_american_order_size_code, 60) -ts_covariance(anl69_roa_expected_report_time, pv64_dif_stal_fund_creation_unit_size, 5) -ts_covariance(mdl77_ohistoricalgrowthfactor_speghc, pv87_interval_asksizeinterval_slippage_correlation, 20) -ts_covariance(fnd17_margin5yr, rsk70_mfm2_usfast_size, 20) -ts_covariance(fnd89_jones_accrual_pct_major_13, oth567score_company_size, 20) -ts_covariance(oth460_net_margin_l2, mdl177_2_liquidityriskfactor_ohlsonscore, 250) -ts_covariance(anl10_ebtrevise_ratio_to_consensus_fq2_1011, pv81_zsbl, 5) -ts_covariance(anl15_ebtgics_ind_cal_fy3_total, fund_total_size, 250) -ts_covariance(fnd72_pit_or_cr_a_prof_margin, pv81_trlt, 20) -ts_covariance(anl14_stddev_ntp_fy1, pv103_sbsz_mean, 5) -ts_covariance(fnd31_qsg5additionalfactor3_backfilled_apg, pv103_sbps_mean, 250) -ts_covariance(mdl26_v14_mstchg_pct_f12m_rnngs_90, pv103_maks_topofbook_maks_mean, 20) -ts_covariance(mdl230_totalcap_cusip_pfgmtt, pv103_htsz_mean, 20) -ts_covariance(mdl138_in_3idp, pv52_yse_chicago_order_size_code, 20) -ts_covariance(fnd65_totalcap_cusip_pe_wt, pv87_interval_bidsize_kurtosis, 250) -ts_covariance(oth553_ptg_wordcnt, pv52_yse_arca_order_size_code, 250) -ts_covariance(anl82_oprq_profitability_profitability10, pv103_mbds_topofbook_mbds_mean, 60) -ts_covariance(anl14_numofests_ebitda_fp4, pv104_masz_mean, 5) -ts_covariance(mdl262_txtq_compustatdeltapredict_funda_madp, fnd65_allcap_sedol_ohlsonscore, 250) -ts_covariance(anl44_netprofit_gaap_best_eeps_nxt_yr, pv104_lsas_mean, 60) -ts_covariance(mdl264_opepsq_l2, pv104_tasz_mean, 250) -ts_covariance(pv20_eps_indicator_2_feature0, nws31_bodysize, 5) -ts_covariance(fnd28_value_18841a, pv104_lbsz_mean, 20) -ts_covariance(mdl26_v14_nnlyst_rvsng_dwn_fy1_rnngs_14, pv104_tasz_mean, 60) -ts_covariance(fnd28_wcfsq1nbf_value_01706q, pv98_daily_mean_taks_taks_mean, 20) -ts_covariance(est_12m_opr_mean_3mth_ago, pv81_zsbm, 60) -ts_covariance(oth460_berry_l2, pv87_interval_asksizeinterval_bidsize_correlation, 20) -ts_covariance(mdl211_salq_deltaprofitability_profitability7, pv87_interval_asksize_std, 60) -ts_covariance(est_q_ner_median_3mth_ago, pv103_masz_mean, 250) -ts_covariance(est_q_opr_num_28d, fund_total_size, 250) -ts_covariance(fnd72_s_pit_or_cr_q_trail_12m_eps, pv52_asdaq_order_size_code, 5) -ts_covariance(anl82_nety_profitability_profitability10, pv64_out_stal_fund_creation_unit_size, 5) -ts_covariance(anl15_ebtgics_s_fy3_cos_dn, pv87_interval_asksizeinterval_volume_correlation, 60) -ts_covariance(pv87_2_netprofit_rep_af_matrix_all_low, pv103_mbsz_mean, 60) -ts_covariance(anl14_high_ntprep_fy5, fnd65_totalcap_cusip_ohlsonscore, 5) -ts_covariance(fnd89_jones_accrual_major_11, pv87_interval_asksize_mean, 60) -ts_covariance(est_ptpr, pv52_yse_chicago_order_size_code, 60) -ts_covariance(mdl262_compustat_models_profitability7, pv103_mlrt_topofbook_mlrt_mean, 250) -ts_covariance(mdl211_ebtq_profitability_profitability5, pv103_mbds_topofbook_mbds_mean, 20) -ts_covariance(mdl262_compustatpredictiveoiadpq_mad_pred, pv104_tbsz_mean, 60) -ts_covariance(fnd72_q1_geo_grow_ebitda_cap_exp_to_int, pv81_trlm, 250) -ts_covariance(mdl26_v14_prsprise_pct_fq2_earnings, rsk70_mfm2_usfast_size, 250) -ts_covariance(mdl77_omomemtumanalystmodel_qma_repearnmom, pv87_interval_asksizeinterval_slippage_correlation, 20) -ts_covariance(mdl262_sbda_a_profitability_profitability2, pv103_laks_mean, 20) -ts_covariance(fnd3_a__earbeforetaxes, pv52_yse_order_size_code, 250) -ts_covariance(fnd6_newa2v1300_ni, pv104_mlrt_mean, 60) -ts_covariance(pv87_2_epsr_af_matrix_all_chngratio_low, pv81_zsbl, 5) -ts_covariance(pv87_2_epsr_af_matrix_p1_dts, pv103_tasz_mean, 5) -ts_covariance(mdl262_oiadpq_profitability_profitability3, pv103_1m_tlrt_mean, 5) -ts_covariance(anl10_nerinnovate_increase_fy1_1995, mdl230_us5000_cusip_ohlsonscore, 5) -ts_covariance(oth460_ciac_class, mdl77_2liquidityriskfactor_ohlsonscore, 20) -ts_covariance(fnd72_pit_or_cr_a_ev_to_t12m_ebitda, pv103_laks_mean, 250) -ts_covariance(oth460_unopincq_l3, fund_total_size, 250) -ts_covariance(eps_fiscal_quarter, nws31_d1_bodysize, 60) -ts_covariance(anl14_numofests_ntprep_fp1, pv87_interval_asksize_mean, 250) -ts_covariance(anl15_ebtgics_gr_cal_fy1_3m_chg, pv104_masz_mean, 250) -ts_covariance(fnd72_s_pit_or_is_q_is_pension_expense_income, pv87_interval_asksizeinterval_volume_correlation, 5) -ts_covariance(anl14_low_ntprep_fy2, pv87_interval_asksizeinterval_bidsize_correlation, 250) -ts_covariance(anl15_ebtgics_s_cal_fy3_pe, pv104_insm_mean, 20) -ts_covariance(fnd6_cibegni, pv52_yse_national_order_size_code, 250) -ts_covariance(rsk62_id_spe, pv104_tasz_mean, 250) -ts_covariance(pv20_indicator_0_feature0_121, pv103_lbsz_mean, 5) -ts_covariance(pv20_ebt_indicator_q_feature11, oth567_deltascore_company_size_432, 60) -ts_covariance(mdl264_uopiq_class, fnd72_pit_or_cr_q_debt_to_mkt_cap, 250) -ts_covariance(pv87_web_weightedavg60_group_v2_0_1_css_earnings, mdl230_totalcap_cusip_ohlsonscore, 20) -ts_covariance(pv87_neg_earnings_matrix_css_sum, pv103_1m_maks_mean, 60) -ts_covariance(oth432_accruals_oeps12_predict, pv52_yse_american_order_size_code, 60) -ts_covariance(anl69_dps_expected_report_time, pv64_out_stal_fund_creation_unit_size, 5) -ts_covariance(research_development_expense_reported_value, pv103_laks_mean, 250) -ts_covariance(fnd23_annfvmfm_rltr, pv103_lask_topofbook_lask_mean, 250) -ts_covariance(mdl77_historicalgrowthfactor_chg3yepsast, pv103_1m_mlrt_mean, 250) -ts_covariance(count_neutral_profitability_summary_fast_d1, pv87_interval_asksizeinterval_slippage_rankcorrel, 20) -ts_covariance(fnd6_txs, pv52_yse_arca_order_size_code, 250) -ts_covariance(fnd3_aacctadj_ebit, pv52_yse_national_order_size_code, 20) -ts_covariance(pv20_gps_indicator_7_feature11, pv87_interval_asksizeinterval_bidsize_correlation, 250) -ts_covariance(pv20_pre_indicator_6_feature10, fund_total_size, 20) -ts_covariance(anl82_oprq_q4_madp, fnd31_ohlsonscore, 20) -ts_covariance(pv87_2_netprofit_af_matrix_p1_chngratio_std, pv104_hbsz_mean, 60) -ts_covariance(mdl262_txtq_compustatdeltapredict_funda_predict, pv98_tbds_mean, 5) -ts_covariance(anl82_salq_profitability_profitability1, pv103_maus_mean, 5) -ts_covariance(pretax_income_total, pv104_tasz_mean, 250) -ts_covariance(oth460_uniamiq_l3, company_offer_total_size, 60) -ts_covariance(pv87_daily_ann_matrix_r1_net_income_normalized_consensus_mean_numnochangeunfiltered, oth567score_company_size, 250) -ts_covariance(oth432_oibdpq_profitability_profitability4, pv103_tbds_mean, 250) -ts_covariance(pv20_gps_indicator_9_feature8, oth551_beta_size, 60) -ts_covariance(mdl211_ebtq_deltaprofitability_profitability12, pv103_hasz_mean, 5) -ts_covariance(est_q_gps_mean_3mth_ago, nws31_bodysize, 60) -ts_covariance(fnd65_allcap_sedol_fqsurstd60dlag, pv98_daily_mean_taks_taks_mean, 5) -ts_covariance(fnd65_allcap_sedol_pspeghc, pv87_interval_asksize_mean, 20) -ts_covariance(pv20_eps_indicator_1_feature7, fund_total_size, 250) -ts_covariance(mdl77_earningsmomemtummodel_fc_fqsurstd, oth551_beta_size, 250) -ts_covariance(anl14_high_ebit_fp4, pv87_interval_bidsizeinterval_slippage_rankcorrel, 20) -ts_covariance(anl15_ebt_gr_cal_fy3_gro, pv103_masz_mean, 5) -ts_covariance(anl15_cpsgics_gr_cal_fy3_pe, pv81_msni, 20) -ts_covariance(gross_profit_total, pv103_tasz_mean, 250) -ts_covariance(mdl262_ninc_trkdpitdeltapredict_funda_mae, pv87_interval_asksizeinterval_bidsize_correlation, 5) -ts_covariance(pv20_pre_indicator_1_feature11, pv103_tbsz_mean, 60) -ts_covariance(pv20_opr_indicator_n_feature2, pv52_yse_order_size_code, 250) -ts_covariance(est_12m_ner_std_28d, pv81_lasz, 250) -ts_covariance(anl82_preq_profitability_profitability12, pv103_tbsz_mean, 250) -ts_covariance(pv20_gps_indicator_9_feature11, pv104_lsbs_mean, 20) -ts_covariance(mdl264_3l_21fspe, pv103_tbsz_mean, 250) -ts_covariance(pv20_net_indicator_n_feature6, pv52_yse_american_order_size_code, 20) -ts_covariance(mdl262_rasv2splitprofitabilityni_q_profitability3, pv64_trr_cur_mkt_cap, 250) -ts_covariance(mdl77_2deepvaluefactor_vesspem21f, pv103_sasz_mean, 5) -ts_covariance(anl15_ebtgics_ind_cal_fy3_3m_chg, pv103_1m_mlrt_mean, 20) -ts_covariance(pv20_opr_indicator_o_feature7, pv104_insm_mean, 5) -ts_covariance(anl14_high_ntp_fp2, pv87_interval_bidsizeinterval_volume_correlation, 20) -ts_covariance(oth432_deltapredictni_a_predict, mdl177_2_liquidityriskfactor_ohlsonscore, 20) -ts_covariance(anl4_epsr_high, nws31_d1_bodysize, 20) -ts_covariance(fnd72_s_pit_or_cr_q_ev_to_t12m_ebit, pv104_tlrt_mean, 20) -ts_covariance(mdl77_earningmomentumfactor_stdevfy1epsp, pv64_trr_stal_cur_mkt_cap, 60) -ts_covariance(mdl262_predictiveebt_a_predict, mdl177_liquidityriskfactor_ohlsonscore_alt, 5) -ts_covariance(mdl177_valanalystmodel_qva_earnquality, pv52_asdaq_psx_order_size_code, 60) -ts_covariance(anl10_ebtfq1_smart_ests_v1_936, pv87_interval_bidsize_std, 60) -ts_covariance(fnd14_hiwater_incmst_net_income_fy, pv104_lsts_mean, 5) -ts_covariance(mdl211_ebty_deltaprofitability_profitability5, pv103_1m_mlrt_mean, 20) -ts_covariance(gross_profit_to_assets_ratio, pv98_tbds_mean, 250) -ts_covariance(ern6_actual_period, pv103_hasz_mean, 20) -ts_covariance(mdl77_liquidityriskfactor_impduration, pv52_asdaq_psx_order_size_code, 60) -ts_covariance(mdl262_deltapredictebt_q_predict, pv104_hasz_mean, 5) -ts_covariance(anl14_mean_eps_fp5, pv64_trr_cur_mkt_cap, 20) -ts_covariance(pv87_ann_matrix_net_income_normalized_estimate_low, pv87_interval_bidsizeinterval_slippage_rankcorrel, 5) -ts_covariance(pv20_ebt_indicator_4_feature7, pv52_asdaq_order_size_code, 250) -ts_covariance(mdl264_net_margin_l2, pv52_yse_arca_order_size_code, 60) -ts_covariance(fnd72_q1_inc_tax_exp_to_net_sales, pv103_lbsz_mean, 60) -ts_covariance(oth460_3l_qacf, pv104_tbsz_mean, 20) -ts_covariance(anl14_high_ebitda_fp5, pv104_lasz_mean, 250) -ts_covariance(pv20_ebt_indicator_q_feature5, pv64_trr_stal_cur_mkt_cap, 60) -ts_covariance(mdl138_qpdi3_net_margin, mdl248_avg_txn_val, 5) -ts_covariance(mdl211_netq_profitability_profitability7, fund_total_size, 250) -ts_covariance(anl15_dps_gr_12_m_pe, nws31_d1_bodysize, 60) -ts_covariance(anl14_high_eps_fy2, pv87_interval_bidsize_skewness, 250) -ts_covariance(est_12m_ner_median, pv103_1m_tlrt_mean, 5) -ts_covariance(mdl262_sbda_a_profitability_profitability4, pv87_interval_asksize_numintervalsincehigh, 5) -ts_covariance(oth460_2l_eiuv, pv103_maus_mean, 5) -ts_covariance(anl82_opry_y1_madp, pv64_trr_cur_mkt_cap, 60) -ts_covariance(anl10_ebiinnovate_decrease_fq2_2606, pv81_zsah, 250) -ts_covariance(fnd7_ointfund_rq21hsc, company_offer_total_size, 5) -ts_covariance(fnd89_csjones_industry_major_14, pv87_interval_bidsize_skewness, 20) -ts_covariance(anl15_bps_ind_12_m_pe, oth551_resret_size, 60) -ts_covariance(fnd65_allcap_sedol_lagegp, pv104_lasz_mean, 20) -ts_covariance(mdl262_txtq_compustatdeltapredict_funda_mada, im_shares_transacted, 5) -ts_covariance(mdl230_us5000_cusip_altmanz, pv103_htsz_mean, 20) -ts_covariance(anl49_backfill_typeofearningspershare, pv104_tbsz_mean, 20) -ts_covariance(anl49_earningspershare, pv103_mbus_mean, 250) -ts_covariance(oth432_txtq_mae, oth551_resret_size, 60) -ts_covariance(fnd28_wsfsq_value_01451q, company_offer_size_value, 5) -ts_covariance(pv20_ebt_indicator_4_feature7, fnd72_pit_or_cr_q_debt_to_mkt_cap, 60) -ts_covariance(pv20_ebt_indicator_7_feature2, pv87_interval_bidsize_kurtosis, 5) -ts_covariance(anl82_saly_deltaprofitability_profitability8, fnd72_pit_or_cr_a_debt_to_mkt_cap, 60) -ts_covariance(pe_ratio_relative_component_rank, company_size_sentiment_score, 250) -ts_covariance(est_q_eps_std_4wks_ago, pv87_interval_bidsize_skewness, 5) -ts_covariance(anl82_netq_deltaprofitability_profitability2, mdl230_totalcap_cusip_ohlsonscore, 20) -ts_covariance(pv20_pre_indicator_8_feature11, pv103_tbsz_mean, 250) -ts_covariance(mdl77_ooearningsmomemtummodel_fc_y2repsg, pv87_interval_asksizeinterval_slippage_correlation, 60) -ts_covariance(pv87_2_operatingprofit_qf_matrix_p1_median, pv52_asdaq_bx_order_size_code, 5) -ts_covariance(anl14_high_eps_fy4, company_size_sentiment_score, 20) -ts_covariance(mdl26_mstchg_pct_f12m_rnngs_14, pv52_asdaq_order_size_code, 5) -ts_covariance(pv20_ebt_indicator_6_feature6, pv103_lbsz_mean, 5) -ts_covariance(mdl77_omomemtumanalystmodel_qma_mktresponse, pv64_dif_stal_fund_creation_unit_size, 250) -ts_covariance(mdl211_saly_profitability_profitability5, mdl177_liquidityriskfactor_ohlsonscore_alt, 60) -ts_covariance(oth432_rasv2splitprofitabilityebt_ttm_profitability4, pv87_interval_bidsize_numintervalsincelow, 20) -ts_covariance(pv20_gps_indicator_1_feature8, pv103_tbsz_mean, 250) -ts_covariance(anl15_ebt_gr_18_m_6m_chg, rsk70_mfm2_usfast_size, 60) -ts_covariance(mdl230_totalcap_cusip_spe2yfvc_cf, fnd31_ohlsonscore, 20) -ts_covariance(mdl230_allcap_sedol_saleeps, fund_total_size, 20) -ts_covariance(pv20_sal_indicator_p_feature1, pv87_interval_asksizeinterval_bidsize_rankcorrel, 5) -ts_covariance(anl14_median_ntp_fy5, nws31_d1_bodysize, 5) -ts_covariance(oth432_saleq_profitability_profitability9, pv98_tbds_mean, 20) -ts_covariance(fnd65_us5000_cusip_chgroepct, oth137_avg_txn_val, 250) -ts_covariance(pv87_2_netprofit_rep_qf_matrix_p1_low, pv104_masz_mean, 250) -ts_covariance(mdl262_che_profitability_profitability2, fnd72_s_pit_or_cr_q_debt_to_mkt_cap, 5) -ts_covariance(mdl211_preq_deltaprofitability_profitability1, pv87_interval_asksize_kurtosis, 250) -ts_covariance(anl82_opry_deltaprofitability_profitability3, pv104_mbsz_mean, 5) -ts_covariance(mdl177_2_historicalgrowthfactor_chg3yepsp, oth567_deltascore_company_size_432, 250) -ts_covariance(fnd72_s_pit_or_is_a_is_rd_expend, mdl77_2liquidityriskfactor_ohlsonscore, 20) -ts_covariance(mdl262_deltapredictebit_q_predict, pv81_zsat, 5) -ts_covariance(fnd6_newa1v1300_ibadj, oth567score_company_size_358, 20) -ts_covariance(anl82_preq_deltaprofitability_profitability11, company_offer_total_size, 20) -ts_covariance(anl14_high_epsrep_fy3, pv103_mbus_mean, 60) -ts_covariance(fnd31_qsg5additionalfactor3_backfilled_rev6fy2, pv64_out_stal_fund_creation_unit_size, 5) -ts_covariance(anl82_delta_preq_q2_predict, fnd72_s_pit_or_cr_q_debt_to_mkt_cap, 20) -ts_covariance(fnd7_ointfund_qipon, pv103_lbsz_mean, 5) -ts_covariance(anl10_ebtfq2_pred_surps_v0_964, pv103_1m_mlrt_mean, 5) -ts_covariance(act_12m_opr_value, pv98_quote_size_mean, 5) -ts_covariance(pv20_gps_indicator_6_feature10, pv103_maus_mean, 60) -ts_covariance(pv87_ann_matrix_net_income_normalized_estimate_median, pv81_lasz, 5) -ts_covariance(anl69_roe_expected_report_dt, pv52_yse_american_order_size_code, 60) -ts_covariance(mdl354_group_pt12yf_dlyspe, pv98_daily_mean_taks_taks_mean, 20) -ts_covariance(fnd28_wcishta_value_18189a, pv103_taks_mean, 20) -ts_covariance(pv20_pre_indicator_3_feature0, pv87_interval_bidsize_median, 250) -ts_covariance(fnd7_ointhstfundfn_hfqspepo, fnd65_totalcap_cusip_ohlsonscore, 60) -ts_covariance(mdl211_nety_y1_mada, mdl177_2_liquidityriskfactor_ohlsonscore, 20) -ts_covariance(oth432_deltapredictebitd_a_mae, pv103_lasz_mean, 5) -ts_covariance(fnd72_a2_retention_ratio, fund_total_size, 250) -ts_covariance(mdl77_ooearningsmomemtummodel_fc_numrevy1, pv81_stlh, 250) -ts_covariance(fnd65_us5000_cusip_aoer, pv103_mlrt_topofbook_mlrt_mean, 250) -ts_covariance(mdl77_put_put_indepsp, pv104_tasz_mean, 5) -ts_covariance(mdl77_momemtumanalystmodel_qma_composite, fnd65_allcap_sedol_ohlsonscore, 20) -ts_covariance(mdl262_acae_q_profitability_profitability8, pv103_tbsz_mean, 60) -ts_covariance(mdl262_niq_compustatdeltapredict_funda_mada, pv87_interval_asksizeinterval_slippage_correlation, 5) -ts_covariance(oth460_ibadjq_l3, pv81_stsl, 20) -ts_covariance(oth432_rasv2splitprofitabilityfcf_ttm_profitability3, mdl77_liquidityriskfactor_ohlsonscore, 60) -ts_covariance(mdl26_bld_stmt_flg_fq2_rnngs, fnd65_totalcap_cusip_ohlsonscore, 60) -ts_covariance(mdl26_mn_f_rvsnclstr_nlysts_fy2_rnngs, oth567_deltascore_company_size_432, 20) -ts_covariance(anl4_netprofit_mean, pv103_maks_topofbook_maks_mean, 250) -ts_covariance(anl82_ebtq_deltaprofitability_profitability5, oth567score_company_size_358, 20) -ts_covariance(oth432_compustatpredictiveepsfiq_mad_pred, pv87_interval_asksize_mean, 5) -ts_covariance(mdl262_oibdpq_profitability_profitability8, pv103_tbds_mean, 250) -ts_covariance(mdl77_2min2ygrossmargin, oth137_avg_txn_val, 20) -ts_covariance(mdl177_2_liquidityriskfactor_oplev, pv87_interval_asksize_kurtosis, 250) -ts_covariance(fnd72_pit_or_cr_a_com_eqy_to_tot_asset, pv64_out_stal_fund_creation_unit_size, 5) -ts_covariance(mdl177_historicalgrowthfactor_pctchg3yeps, pv81_trlt, 20) -ts_covariance(investment_income_nonoperating, mdl77_2liquidityriskfactor_ohlsonscore, 20) -ts_covariance(mdl264_viat_class, pv81_zsbh, 60) -ts_covariance(ebit_reported_value, pv87_interval_asksizeinterval_bidsize_correlation, 20) -ts_covariance(mdl77_oindustryrrelativevaluefactor_curindcoreepsp_, mdl77_2liquidityriskfactor_ohlsonscore, 5) -ts_covariance(fnd28_pftlta_value_08365a, pv103_1m_maks_mean, 250) -ts_covariance(fnd65_totalcap_cusip_mpoghc, pv104_tasz_mean, 5) -ts_covariance(fnd28_statisticsa_value_05290a, pv103_masz_mean, 60) -ts_covariance(oth432_deltapredictebt_a_predict, oth551_r2_size, 20) -ts_covariance(mdl77_valuemomemtummodel_earningsqualitymodule, pv52_yse_arca_order_size_code, 5) -ts_covariance(pv20_net_indicator_8_feature12, pv104_lbsz_mean, 20) -ts_covariance(gross_profit_current, pv81_stsl, 20) -ts_covariance(mdl26_nnlyst_rvsng_p_fy1_rnngs_7, mdl248_avg_txn_val, 60) -ts_covariance(mdl26_stm_f12m_rnngs, pv103_1m_mbds_mean, 5) -ts_covariance(fnd65_us5000_cusip_ebitdap, oth137_avg_txn_val, 60) -ts_covariance(mdl211_ebtq_deltaprofitability_profitability11, pv103_sbps_mean, 250) -ts_covariance(mdl26_dsnc_prd_srprs_flg_chg_fq4_rnngs, pv104_insm_mean, 60) -ts_covariance(fnd23_icsm_m_caic, pv103_tlrt_topofbook_tlrt_mean, 250) -ts_covariance(oth395_major_12_13, nws31_bodysize, 60) -ts_covariance(mdl264_epsfx_class, pv104_hlts_mean, 60) -ts_covariance(mdl264_einn_l1, pv87_interval_asksize_std, 60) -ts_covariance(anl69_rec_expected_report_time, mdl230_allcap_sedol_ohlsonscore, 5) -ts_covariance(anl49_backfill_earningspershareindicator, pv81_zsbl, 250) -ts_covariance(mdl230_allcap_sedol_yoychgroa, pv103_masz_mean, 60) -ts_covariance(mdl77_earningsmomemtummodel_fc_y2repsg, pv64_dif_stal_fund_creation_unit_size, 250) -ts_covariance(mdl230_us5000_cusip_spe2yfvc_cf, pv64_trr_stal_cur_mkt_cap, 60) -ts_covariance(anl15_ebt_s_cal_fy3_mean, pv52_asdaq_order_size_code, 5) -ts_covariance(fnd72_pit_or_cr_q_announcement_dt, pv87_interval_asksize_numintervalsincehigh, 250) -ts_covariance(fnd65_totalcap_cusip_fc_numrevq1, rsk70_mfm2_usfast_size, 5) -ts_covariance(pv87_2_netprofit_rep_qf_matrix_p1_median, fnd31_ohlsonscore, 5) -ts_covariance(mdl230_allcap_sedol_pedwf_cf, pv87_interval_asksizeinterval_slippage_rankcorrel, 20) -ts_covariance(oth432_compustatpredictiveniq_prediction, pv103_tasz_mean, 5) -ts_covariance(mdl26_v14_mstchg_pct_fy1_rnngs_14, pv98_tbds_mean, 20) -ts_covariance(oth460_3l_ifpv, oth551_beta_size, 60) -ts_covariance(mdl26_mstchg_pct_fq2_rnngs_60, pv52_yse_order_size_code, 20) -ts_covariance(earnings_news_mention_count, pv104_lsbs_mean, 250) -ts_covariance(anl10_ebsfy2_smart_ests_v1, pv87_interval_bidsizeinterval_slippage_correlation, 60) -ts_covariance(oth460_usa_compustat_q1_dl8_ibadjq_l2, pv87_interval_asksizeinterval_bidsize_correlation, 250) -ts_covariance(mdl264_usa_compustat_q1_dl8_ibadjq_l3, pv64_dif_fund_creation_unit_size, 250) -ts_covariance(fnd90_us_qes_gamef_grossprofit_asset, pv103_tbsz_mean, 60) -ts_covariance(fnd28_dvfq1_value_18264q, fnd65_allcap_sedol_ohlsonscore, 250) -ts_covariance(mdl262_compustatpredictiveepspiq_prediction, pv103_tbds_mean, 20) -ts_covariance(oth432_oiadpq_profitability_profitability1, pv87_interval_asksizeinterval_bidsize_rankcorrel, 250) -ts_covariance(mdl262_rasv2splitprofitabilityebitd_ttm_profitability9, oth567score_company_size_358, 250) -ts_covariance(mdl77_oearningmomentumfactor_rev6, pv103_tasz_mean, 250) -ts_covariance(mdl177_historicalgrowthfactor_chgeps, pv103_sbsz_mean, 20) +ts_sum(returns, 20) +rank(subtract(ts_count_nans(anl14_high_ebit_fy2, 20), ts_count_nans(anl14_low_eps_fy5, 20))) +zscore(ts_corr(returns, resampled_pca_factor_1, 20)) +subtract(ts_product(ts_zscore(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 5), 5), group_mean(ts_product(ts_zscore(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 5), 5), 1, industry_grouping_level2_top2000)) +normalize(quarterly_working_capital_per_share_to_price_2) +ts_decay_linear(returns, 10) +subtract(divide(ts_sum(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 5), ts_sum(returns, 5)), group_mean(divide(ts_sum(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 5), ts_sum(returns, 5)), 1, industry_grouping_level2_top2000)) +zscore(divide(quarterly_free_cash_flow_per_share_non_annualized_2, multiply(net_income_annual, ts_rank(fnd44_working_capital_accruals, 12)))) +zscore(fnd31_devnorthamericaadditionalfactor4_pcwt) +zscore(multiply(divide(quarterly_cash_flow_per_share_non_annualized_prior, quarterly_earnings_before_tax), inverse(ts_std_dev(quarterly_operating_margin_percent, 36)))) +ts_corr(returns, pv173_rawratiosbondreturn20deqwt, 20) +multiply(ts_delta(rank(daily_volume_to_shares_outstanding), 3), ts_delta(rank(ts_std_dev(returns, 20)), 3)) +ts_av_diff(returns, 20) +ts_zscore(multiply(group_rank(anl14_high_ntp_fy1, industry), sign(ts_delta(daily_volume_percent_shares_out, 10))), 20) +zscore(add(ts_sum(short_term_net_cash_balance, 20), reverse(ts_sum(other_off_balance_liabilities, 20)))) +ts_regression(ts_std_dev(returns, 20), ts_mean(returns, 20), 20) +zscore(fnd44_working_capital_accruals) +ts_zscore(multiply(ts_delay(returns, 5), ts_rank(daily_volume_percent_shares_out, 20)), 20) +normalize(subtract(divide(quarterly_cash_flow_per_share, net_income_quarterly_4), ts_rank(fnd44_perc_accruals, 24))) +zscore(fnd44_perc_accruals) +subtract(ts_rank(quarterly_book_value_per_share_5, 30), ts_rank(quarterly_tangible_book_value_per_share_3, 30)) +rank(divide(multiply(quarterly_cash_flow_per_share_2, quarterly_asset_turnover), add(net_income_annual, fnd44_probability_restatement))) +last_diff_value(ts_std_dev(returns, 20), 20) +group_neutralize(ts_zscore(daily_volume_to_shares_outstanding, 60), industry_grouping_level2_top2000) +group_zscore(subtract(ts_rank(anl14_high_ntp_fy1, 20), ts_rank(anl14_low_eps_fy5, 20)), industry) +group_neutralize(ts_delta(pv37_high_global2h, 5), industry_grouping_level2_top2000) +group_neutralize(ts_decay_linear(ts_zscore(pv37_volume_global, 20), 10), industry_grouping_level2_top2000) +zscore(anl14_high_revenue_fy2) +ts_delta(ts_std_dev(returns, 60), 10) +multiply(ts_std_dev(returns, 20), sign(ts_delta(returns, 1))) +subtract(if_else(ts_delta(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 1) > 0, ts_zscore(returns, 20), ts_zscore(returns, 20) * -1), group_mean(if_else(ts_delta(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 1) > 0, ts_zscore(returns, 20), ts_zscore(returns, 20) * -1), 1, industry_grouping_level2_top2000)) +ts_decay_linear(ts_std_dev(returns, 20), 20) +subtract(ts_av_diff(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 20), group_mean(ts_av_diff(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 20), 1, industry_grouping_level2_top2000)) +normalize(quarterly_asset_turnover) +group_scale(add(ts_quantile(daily_volume_percent_shares_out, 20), ts_quantile(anl14_low_ndebt_fy1, 20)), industry) +zscore(divide(quarterly_pe_ratio_high, 1)) +group_neutralize(ts_av_diff(pv37_volume_global, 20), industry_grouping_level2_top2000) +normalize(anl14_low_eps_fy2) +zscore(multiply(ts_regression(returns, pv173_zscoresbondreturn20deqwt, 20), sign(ts_delta(anl14_high_revenue_fy2, 5)))) +subtract(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), group_mean(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 1, industry_grouping_level2_top2000)) +subtract(ts_corr(pv37_volume_global, returns, 20), group_mean(ts_corr(pv37_volume_global, returns, 20), 1, industry_grouping_level2_top2000)) +subtract(ts_backfill(pv37_volume_global, 20, 1, "NAN"), group_mean(ts_backfill(pv37_volume_global, 20, 1, "NAN"), 1, industry_grouping_level2_top2000)) +rank(divide(ts_std_dev(anl14_high_ntp_fy1, 20), ts_std_dev(resampled_pca_factor_1, 20))) +subtract(rank(ts_std_dev(ttm_net_income_value, 252)), rank(ts_std_dev(quarterly_asset_turnover, 252))) +group_neutralize(ts_rank(pv37_low_global, 20), industry_grouping_level2_top2000) +rank(subtract(quarterly_working_capital_per_share_to_price_2, fnd44_working_capital_accruals)) +quantile(ts_std_dev(returns, 20)) +subtract(ts_zscore(ts_std_dev(returns, 20), 60), group_mean(ts_zscore(ts_std_dev(returns, 20), 60), 1, industry_grouping_level2_top2000)) +subtract(ts_zscore(daily_volume_to_shares_outstanding, 60), group_mean(ts_zscore(daily_volume_to_shares_outstanding, 60), 1, industry_grouping_level2_top2000)) +group_neutralize(ts_corr(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), returns, 20), industry_grouping_level2_top2000) +normalize(subtract(divide(quarterly_free_cash_flow, quarterly_total_revenue_3), ts_rank(fnd44_probability_restatement, 12))) +multiply(ts_std_dev(returns, 20), ts_mean(returns, 20), false) +rank(divide(anl69_best_px_cps_ratio, 1)) +rank(ts_corr(ts_mean(fnd31_qsg5additionalfactor3_monthly_y5speq4rqsr, 20), ts_mean(fnd31_qsg5additionalfactor3_monthly_yoychgroepct, 20), 50)) +normalize(divide(quarterly_cash_flow_per_share, net_income_quarterly_4)) +ts_decay_linear(rank(ts_delta(fnd72_s_pit_or_is_q_is_net_non_oper_loss, 10)), 5) +rank(anl14_high_roe_fy2) +group_mean(multiply(ts_std_dev(returns, 20), daily_volume_percent_shares_out), 1, industry_grouping_level2_top1500) +zscore(multiply(divide(quarterly_free_cash_flow_per_share_alt_2, quarterly_earnings_before_tax_5), sign(subtract(ts_mean(quarterly_receivables_turnover_2, 12), ts_mean(quarterly_receivables_turnover_2, 24))))) +ts_arg_min(ts_std_dev(returns, 20), 20) +subtract(ts_corr(returns, daily_volume_to_shares_outstanding, 20), group_mean(ts_corr(returns, daily_volume_to_shares_outstanding, 20), 1, industry_grouping_level2_top2000)) +multiply(rank(ts_delta(fnd31_qsg5additionalfactor3_monthly_fwdebitdaev, 5)), ts_rank(fnd31_qsg5additionalfactor3_monthly_pbwt, 10)) +subtract(ts_rank(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 20), group_mean(ts_rank(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 20), 1, industry_grouping_level2_top2000)) +subtract(ts_rank(long_term_trade_debt, 20), ts_rank(long_term_other_current_liabilities, 20)) +group_neutralize(ts_backfill(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 20, 1, "NAN"), industry_grouping_level2_top2000) +normalize(multiply(divide(quarterly_cash_flow_per_share_alt, normalized_net_income_available_to_common_annual), sign(ts_delta(quarterly_asset_turnover, 6)))) +rank(divide(quarterly_earnings_before_tax_5, annual_total_revenue)) +ts_decay_linear(rank(ts_std_dev(returns, 20)), 20) +group_neutralize(ts_regression(returns, group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 20, 0, 0), industry_grouping_level2_top2000) +group_neutralize(divide(ts_delay(pv37_volume_global, 5), ts_delay(pv37_volume_global, 10)), industry_grouping_level2_top2000) +group_neutralize(ts_quantile(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 20, "gaussian"), industry_grouping_level2_top2000) +group_mean(ts_std_dev(returns, 20), sharesout, industry) +multiply(group_rank(ts_std_dev(returns, 20), industry_grouping_level2_top1500), rank(ts_std_dev(returns, 20))) +rank(subtract(divide(quarterly_cash_per_share_3, normalized_net_income), fnd44_perc_accruals)) +rank(divide(quarterly_cash_flow_per_share_2, add(fnd13_statementq_sopi, 0.01))) +rank(multiply(inverse(ts_std_dev(quarterly_net_income_3, 24)), divide(quarterly_cash, net_income_annual))) +zscore(multiply(divide(quarterly_cash_flow_per_share_nonannualized, net_income_quarterly_4), inverse(ts_std_dev(normalized_net_income, 24)))) +subtract(ts_decay_linear(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 10), group_mean(ts_decay_linear(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 10), 1, industry_grouping_level2_top2000)) +rank(fnd44_perc_accruals) +ts_decay_linear(subtract(ts_mean(anl14_high_ntp_fy1, 20), group_mean(anl14_high_ntp_fy1, industry, industry)), 20) +group_neutralize(group_rank(pv37_volume_global, industry_grouping_level2_top2000), industry_grouping_level2_top2000) +subtract(rank(ts_std_dev(returns, 5)), rank(ts_std_dev(returns, 60))) +subtract(subtract(ts_mean(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 10), ts_mean(returns, 10)), group_mean(subtract(ts_mean(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 10), ts_mean(returns, 10)), 1, industry_grouping_level2_top2000)) +ts_corr(ts_std_dev(returns, 20), resampled_pca_factor_1, 20) +divide(rank(ts_std_dev(returns, 20)), group_mean(rank(ts_std_dev(returns, 20)), 1, industry_grouping_level2_top1500)) +subtract(multiply(ts_std_dev(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 20), ts_std_dev(returns, 20)), group_mean(multiply(ts_std_dev(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 20), ts_std_dev(returns, 20)), 1, industry_grouping_level2_top2000)) +group_neutralize(ts_delta(pv37_low_global1h, 5), industry_grouping_level2_top2000) +subtract(multiply(ts_rank(pv37_volume_global, 20), ts_rank(returns, 20)), group_mean(multiply(ts_rank(pv37_volume_global, 20), ts_rank(returns, 20)), 1, industry_grouping_level2_top2000)) +winsorize(divide(quarterly_cash_flow_per_share_2, add(quarterly_net_income_3, 0.01)), std=3) +ts_arg_max(ts_std_dev(returns, 20), 20) +ts_zscore(divide(group_mean(anl14_high_ntp_fy1, industry, industry), ts_mean(anl14_high_ntp_fy1, 20)), 20) +group_scale(multiply(ts_av_diff(anl14_high_ebit_fy2, 20), sign(ts_delta(pv173_zscoresbondreturn20deqwt, 5))), industry) +ts_std_dev(returns, 60) +rank(multiply(divide(quarterly_cash_flow_per_share_2, quarterly_earnings_before_tax_5), inverse(ts_std_dev(quarterly_operating_margin_percent, 12)))) +divide(ts_std_dev(returns, 20), group_mean(ts_std_dev(returns, 20), 1, industry_grouping_level2_top1500)) +log(add(abs(fnd44_perc_accruals), 0.01)) +normalize(divide(ts_mean(anl14_high_ntp_fy1, 20), group_mean(anl14_high_ntp_fy1, industry, industry))) +ts_corr(returns, ts_backfill(anl14_high_revenue_fy2, 250), 20) +rank(multiply(ts_arg_max(anl14_high_revenue_fy1, 20), inverse(ts_arg_min(anl14_low_ebit_fy4, 20)))) +group_neutralize(ts_zscore(ts_std_dev(returns, 20), 60), industry_grouping_level2_top2000) +group_backfill(ts_std_dev(returns, 20), industry, 20) +normalize(divide(quarterly_free_cash_flow_per_share, net_income_quarterly_4)) +rank(fnd31_devnorthamericaadditionalfactor4_ps_wt) +rank(divide(quarterly_long_term_debt_to_assets, quarterly_total_assets)) +normalize(ts_std_dev(returns, 20)) +ts_target_tvr_decay(ts_std_dev(returns, 20), 0, 1, 0.1) +subtract(ts_rank(fnd31_devnorthamericaadditionalfactor4_y5speq4vc, 20), ts_rank(fnd31_devnorthamericaadditionalfactor4_divgp, 20)) +group_neutralize(ts_arg_min(pv37_volume_global, 10), industry_grouping_level2_top2000) +inverse(add(fnd44_working_capital_accruals, 0.01)) +subtract(ts_rank(share_based_awards, 30), ts_rank(share_based_deferred_tax, 30)) +group_neutralize(add(ts_zscore(pv37_volume_global, 20), ts_zscore(returns, 20)), industry_grouping_level2_top2000) +divide(rank(ts_std_dev(returns, 20)), add(ts_std_dev(returns, 60), 0.001)) +group_neutralize(subtract(ts_mean(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 10), ts_mean(returns, 10)), industry_grouping_level2_top2000) +add(add(rank(divide(quarterly_free_cash_flow_per_share_2, add(net_income_quarterly_4, 0.01))), rank(quarterly_asset_turnover)), reverse(rank(fnd44_perc_accruals))) +subtract(ts_delta(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 5), group_mean(ts_delta(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 5), 1, industry_grouping_level2_top2000)) +ts_arg_max(ts_sum(fnd31_qsg5additionalfactor3_monthly_rev6fy2, 30), 60) +rank(fnd44_probability_restatement) +group_neutralize(add(ts_product(anl14_high_eps_fp1, 10), reverse(ts_product(anl14_low_roe_fy1, 10))), industry) +ts_arg_min(ts_sum(long_term_secured_debt, 30), 60) +if_else(ts_std_dev(returns, 60) > ts_mean(ts_std_dev(returns, 60), 120), reverse(rank(ts_std_dev(returns, 20))), rank(ts_std_dev(returns, 20))) +zscore(pv173_rawratiosbondreturn20deqwt) +ts_zscore(returns, 20) +multiply(ts_delta(daily_volume_percent_shares_out, 3), ts_std_dev(returns, 20)) +subtract(ts_quantile(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 20, "gaussian"), group_mean(ts_quantile(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 20, "gaussian"), 1, industry_grouping_level2_top2000)) +divide(rank(daily_volume_to_shares_outstanding), group_mean(rank(daily_volume_to_shares_outstanding), 1, industry_grouping_level2_top1500)) +subtract(ts_delta(pv37_high_global2h, 5), group_mean(ts_delta(pv37_high_global2h, 5), 1, industry_grouping_level2_top2000)) +normalize(divide(quarterly_free_cash_flow_per_share_alt_2, multiply(net_income_after_tax_2, ts_std_dev(quarterly_net_income_3, 12)))) +ts_scale(ts_std_dev(returns, 20), 20) +normalize(multiply(inverse(ts_std_dev(quarterly_net_income_3, 18)), divide(quarterly_free_cash_flow, quarterly_total_revenue_3))) +ts_av_diff(ts_std_dev(returns, 20), 20) +normalize(multiply(divide(quarterly_free_cash_flow_per_share, quarterly_pe_ratio_maximum), inverse(ts_std_dev(quarterly_asset_turnover, 24)))) +ts_sum(ts_delta(ts_std_dev(returns, 20), 1), 20) +rank(subtract(divide(quarterly_cash_per_share_3, net_income_available_to_common_incl_extraordinary_annual), fnd44_probability_restatement)) +zscore(divide(quarterly_free_cash_flow_per_share_alt_2, net_income_quarterly_4)) +multiply(sign(ts_sum(shareholders_other_equity_2, 20)), ts_rank(short_term_bank_debt_amount, 30)) +ts_arg_max(returns, 20) +multiply(ts_std_dev(returns, 20), ts_rank(pv37_volume_14, 20)) +subtract(ts_backfill(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 20, 1, "NAN"), group_mean(ts_backfill(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 20, 1, "NAN"), 1, industry_grouping_level2_top2000)) +subtract(rank(ts_mean(fnd72_pit_or_cf_q_cf_cash_from_oper, 20)), rank(ts_mean(fnd72_pit_or_cf_q_cf_other_fnc_act, 20))) +rank(add(divide(fnd72_s_pit_or_cf_q_cf_cash_from_oper, fnd72_s_pit_or_is_q_net_income), inverse(fnd44_perc_accruals))) +rank(divide(quarterly_total_shareholder_equity, add(quarterly_total_debt_4, 0.01))) +rank(divide(net_income_quarterly_4, annual_total_revenue)) +subtract(pv37_high_global, pv37_low_global) +group_scale(multiply(ts_arg_min(anl14_high_revenue_fy1, 20), sign(ts_delta(anl14_low_ebit_fy4, 5))), industry) +group_mean(ts_std_dev(returns, 20), ts_mean(returns, 20), industry) +not(ts_std_dev(returns, 20) > 0) +group_neutralize(rank(ts_std_dev(returns, 20)), industry_grouping_level2_top1500) +rank(ts_covariance(ts_mean(fnd72_pit_or_bs_q_bs_future_min_oper_lease_oblig, 30), ts_mean(fnd72_pit_or_is_q_is_eqy_earn_from_invest_assoc, 30), 60)) +zscore(subtract(divide(quarterly_free_cash_flow_per_share_alt_2, net_income_after_tax_2), multiply(fnd44_probability_restatement, 0.1))) +and(ts_std_dev(returns, 20) > 0, ts_mean(returns, 20) > 0) +ts_arg_max(ts_sum(fnd72_s_pit_or_bs_q_bs_accrued_income_taxes, 30), 60) +subtract(signed_power(ts_delta(log(pv37_volume_global), 5), 2), group_mean(signed_power(ts_delta(log(pv37_volume_global), 5), 2), 1, industry_grouping_level2_top2000)) +zscore(divide(quarterly_total_shareholder_equity, quarterly_total_assets)) +rank(divide(quarterly_operating_margin_percent, 1)) +group_neutralize(multiply(ts_rank(pv37_volume_global, 20), ts_rank(returns, 20)), industry_grouping_level2_top2000) +vector_neut(rank(fnd44_perc_accruals), rank(book_value_per_share_quarterly)) +rank(divide(fnd72_pit_or_cf_q_cf_cash_from_oper, net_income_quarterly_4)) +divide(ts_std_dev(returns, 20), ts_mean(returns, 20)) +group_neutralize(ts_corr(returns, daily_volume_to_shares_outstanding, 20), industry_grouping_level2_top2000) +ts_backfill(anl14_high_revenue_fy2, 250) +ts_corr(ts_std_dev(returns, 20), ts_delay(ts_std_dev(returns, 20), 1), 20) +ts_arg_max(ts_sum(quarterly_quick_ratio_2, 30), 60) +ts_corr(ts_std_dev(returns, 20), ts_mean(returns, 20), 20) +rank(fnd44_score) +group_neutralize(ts_sum(if_else(ts_delta(pv37_volume_global, 1) > 0, 1, -1), 10), industry_grouping_level2_top2000) +hump(rank(subtract(divide(quarterly_free_cash_flow_per_share_2, add(quarterly_net_income_3, 0.01)), fnd44_perc_accruals)), hump=0.05) +sign(ts_std_dev(returns, 20)) +subtract(ts_av_diff(pv37_volume_global, 20), group_mean(ts_av_diff(pv37_volume_global, 20), 1, industry_grouping_level2_top2000)) +group_neutralize(ts_scale(pv37_volume_global, 20), industry_grouping_level2_top2000) +subtract(rank(ts_std_dev(returns, 20)), rank(ts_mean(daily_volume_to_shares_outstanding, 20))) +rank(divide(anl14_low_ebitda_fy2, annual_total_revenue)) +normalize(add(ts_product(anl14_high_eps_fp1, 10), ts_product(anl14_low_roe_fy1, 10))) +jump_decay(rank(subtract(divide(quarterly_cash_flow_per_share_2, add(net_income_quarterly_4, 0.01)), fnd44_perc_accruals)), 21, sensitivity=0.5, force=0.1) +zscore(pv37_low_14) +subtract(ts_count_nans(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 20), group_mean(ts_count_nans(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 20), 1, industry_grouping_level2_top2000)) +zscore(quarterly_receivables_turnover_2) +normalize(multiply(ts_quantile(anl14_low_eps_fy5, 20), inverse(ts_mean(twelve_month_high_price, 20)))) +subtract(ts_max(ts_std_dev(returns, 20), 20), ts_std_dev(returns, 20)) +multiply(ts_delta(rank(ts_std_dev(returns, 20)), 5), ts_delta(rank(daily_volume_to_shares_outstanding), 5)) +rank(ts_corr(quarterly_cash_flow_per_share_2, quarterly_net_income_3, 252)) +ts_decay_linear(rank(ts_delta(annual_net_income_incl_extraordinary, 10)), 5) +subtract(ts_rank(fnd31_qsg5additionalfactor3_monthly_mpgghcy3, 30), ts_rank(fnd31_qsg5additionalfactor3_monthly_numrevy2, 30)) +ts_av_diff(rank(ts_std_dev(returns, 20)), 60) +days_from_last_change(ts_std_dev(returns, 20)) +subtract(rank(divide(quarterly_free_cash_flow_per_share_2, add(net_income_quarterly_4, 0.01))), rank(fnd44_perc_accruals)) +multiply(ts_std_dev(returns, 20), rank(fnd44_score)) +zscore(divide(operating_income_2, annual_total_revenue)) +normalize(divide(anl14_high_ebitda_fy2, annual_total_revenue)) +ts_arg_min(ts_sum(quarterly_receivables_turnover_2, 30), 60) +subtract(rank(divide(quarterly_cash_flow_per_share_2, add(fnd13_statementq_sopi, 0.01))), rank(fnd44_working_capital_accruals)) +normalize(divide(quarterly_research_and_development_expense_2, annual_total_revenue)) +rank(divide(anl14_high_ebit_fy2, annual_total_revenue)) +zscore(divide(fnd72_pit_or_cf_q_cf_cash_from_oper, add(fnd72_pit_or_is_q_net_income, abs(fnd44_perc_accruals)))) +abs(ts_av_diff(ts_std_dev(returns, 20), 20)) +normalize(add(ts_rank(anl14_high_revenue_fp1, 20), reverse(ts_rank(anl14_low_ntprep_fp4, 20)))) +if_else(ts_std_dev(returns, 20) > ts_mean(returns, 20), 1, 0) +multiply(rank(ts_delta(fnd17_arev, 5)), ts_rank(fnd17_arevps, 10)) +subtract(ts_arg_min(pv37_volume_global, 10), group_mean(ts_arg_min(pv37_volume_global, 10), 1, industry_grouping_level2_top2000)) +group_neutralize(ts_regression(returns, pv37_volume_global, 20, 0, 1), industry_grouping_level2_top2000) +zscore(add(divide(quarterly_cash_flow_per_share_non_annualized, book_value_per_share_quarterly), inverse(ts_rank(fnd44_perc_accruals, 24)))) +sign(subtract(ts_sum(fnd72_pit_or_cf_a_cf_net_inc, 250), ts_sum(fnd72_pit_or_is_a_is_net_inc_avail_com_shrhldrs, 250))) +subtract(ts_delta(pv37_low_global1h, 5), group_mean(ts_delta(pv37_low_global1h, 5), 1, industry_grouping_level2_top2000)) +ts_rank(subtract(rank(divide(quarterly_free_cash_flow_per_share_2, add(net_income_quarterly_4, 0.01))), rank(fnd44_perc_accruals)), 252) +group_neutralize(abs(ts_delta(log(pv37_volume_global), 5)), industry_grouping_level2_top2000) +multiply(sign(ts_sum(fnd31_devnorthamericaadditionalfactor4_apg, 20)), ts_rank(fnd31_devnorthamericaadditionalfactor4_mpg, 30)) +subtract(ts_rank(fnd72_pit_or_cf_a_cf_cash_from_fnc_act, 20), ts_rank(fnd72_pit_or_cf_a_cf_other_fnc_act, 20)) +subtract(signed_power(ts_delta(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 3), 2), group_mean(signed_power(ts_delta(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 3), 2), 1, industry_grouping_level2_top2000)) +rank(add(divide(fnd72_pit_or_cf_a_cf_cash_from_fnc_act, fnd72_pit_or_is_a_is_net_interest_expense), inverse(fnd44_probability_restatement))) +rank(subtract(ts_quantile(anl14_high_ebit_fp4, 20), ts_quantile(pv173_rawratiosmt5yzspreadchg60d, 20))) +multiply(sign(ts_sum(annual_total_revenue, 20)), ts_rank(annual_debt_to_total_capital, 30)) +zscore(add(divide(fnd72_s_pit_or_cf_q_cf_cash_from_oper, fnd72_s_pit_or_is_q_net_income), inverse(fnd44_perc_accruals))) +ts_std_dev(returns, 20) +rank(add(divide(fnd72_pit_or_cf_a_cf_net_inc, fnd72_pit_or_is_a_is_net_interest_expense), inverse(ts_std_dev(quarterly_net_income_available_to_common_2, 24)))) +group_neutralize(ts_av_diff(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 20), industry_grouping_level2_top2000) +group_neutralize(subtract(ts_max(pv37_volume_global, 5), ts_min(pv37_volume_global, 5)), industry_grouping_level2_top2000) +normalize(divide(quarterly_net_income_3, quarterly_total_assets)) +rank(multiply(divide(quarterly_free_cash_flow_per_share_non_annualized, quarterly_book_value_per_share_5), sign(ts_delta(quarterly_asset_turnover, 12)))) +normalize(add(divide(quarterly_cash_flow_per_share_alt, book_value_per_share_quarterly), inverse(ts_rank(fnd44_perc_accruals, 18)))) +multiply(group_rank(ts_std_dev(returns, 20), industry_grouping_level2_top1500), rank(daily_volume_percent_shares_out)) +zscore(divide(quarterly_cash_flow_per_share_2, add(normalized_net_income, abs(fnd44_working_capital_accruals)))) +zscore(divide(fnd17_arevps, 1)) +add(ts_std_dev(returns, 20), ts_std_dev(returns, 10)) +ts_quantile(ts_std_dev(returns, 20), 20) +ts_count_nans(ts_std_dev(returns, 20), 20) +reverse(rank(ts_std_dev(returns, 20))) +rank(quarterly_current_ratio_alt) +group_neutralize(ts_rank(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 20), industry_grouping_level2_top2000) +ts_rank(ts_std_dev(returns, 20), 20) +bucket(rank(subtract(divide(quarterly_free_cash_flow_per_share_2, add(quarterly_net_income_3, 0.01)), fnd44_perc_accruals)), range="0,1,0.2") +subtract(ts_scale(pv37_volume_global, 20), group_mean(ts_scale(pv37_volume_global, 20), 1, industry_grouping_level2_top2000)) +group_zscore(pv37_volume_14, industry) +ts_backfill(divide(group_zscore(anl14_high_nav_fy2, industry), ts_mean(anl14_low_ndebt_fy5, 20)), 20) +rank(add(rank(divide(quarterly_cash_flow_per_share_2, add(net_income_quarterly_4, 0.01))), rank(divide(quarterly_free_cash_flow_per_share_2, add(quarterly_net_income_3, 0.01))))) +ts_scale(subtract(group_mean(anl14_high_revenue_fp1, industry, industry), ts_mean(fnd31_qsg5additionalfactor3_monthly_pcdwf, 20)), 20) +rank(divide(quarterly_cash_flow_per_share_2, add(net_income_quarterly_4, 0.01))) +rank(ts_covariance(ts_mean(fnd31_devnorthamericaadditionalfactor4_rev6fy2, 30), ts_mean(fnd31_devnorthamericaadditionalfactor4_tw_ebitdaev, 30), 60)) +ts_decay_linear(rank(subtract(ts_sum(fnd72_pit_or_bs_q_bs_other_cur_asset, 60), ts_sum(fnd72_pit_or_bs_q_bs_other_cur_liab, 60))), 5) +subtract(ts_covariance(returns, pv37_volume_global, 20), group_mean(ts_covariance(returns, pv37_volume_global, 20), 1, industry_grouping_level2_top2000)) +rank(subtract(divide(quarterly_free_cash_flow_per_share, quarterly_pe_ratio_high), fnd44_probability_restatement)) +group_neutralize(ts_arg_min(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 10), industry_grouping_level2_top2000) +ts_delta(ts_backfill(anl14_high_revenue_fy2, 250), 5) +rank(ts_covariance(ts_mean(net_income_available_to_common_incl_extraordinary_annual, 30), ts_mean(normalized_net_income_annual, 30), 60)) +rank(divide(quarterly_free_cash_flow_per_share_2, add(quarterly_net_income_3, 0.01))) +divide(ts_std_dev(returns, 20), group_mean(ts_std_dev(returns, 20), sharesout, industry)) +divide(ts_std_dev(returns, 20), ts_max(ts_std_dev(returns, 60), 60)) +zscore(add(ts_rank(anl14_high_revenue_fy2, 20), reverse(ts_rank(anl14_low_ebit_fy5, 20)))) +ts_arg_max(ts_sum(fnd17_apayratio, 30), 60) +group_neutralize(ts_count_nans(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 20), industry_grouping_level2_top2000) +group_neutralize(fnd86_fundamental_score, industry) +subtract(ts_count_nans(pv37_volume_global, 20), group_mean(ts_count_nans(pv37_volume_global, 20), 1, industry_grouping_level2_top2000)) +group_neutralize(divide(ts_mean(pv37_volume_global, 5), ts_std_dev(pv37_volume_global, 5)), industry_grouping_level2_top2000) +rank(multiply(divide(quarterly_free_cash_flow_per_share, book_value_per_share_quarterly), sign(subtract(ts_mean(quarterly_asset_turnover, 12), ts_mean(quarterly_asset_turnover, 24))))) +subtract(ts_regression(returns, pv37_volume_global, 20, 0, 1), group_mean(ts_regression(returns, pv37_volume_global, 20, 0, 1), 1, industry_grouping_level2_top2000)) +ts_arg_min(rank(ts_std_dev(returns, 20)), 60) +subtract(ts_covariance(returns, group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 20), group_mean(ts_covariance(returns, group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 20), 1, industry_grouping_level2_top2000)) +normalize(multiply(inverse(ts_std_dev(quarterly_operating_margin_percent, 6)), divide(quarterly_free_cash_flow_per_share, quarterly_total_revenue_3))) +rank(divide(add(quarterly_cash_flow_per_share_alt, fnd72_pit_or_cf_q_cf_cash_from_oper), add(net_income_current_period, abs(fnd44_perc_accruals)))) +group_neutralize(ts_std_dev(pv37_volume_global, 20), industry_grouping_level2_top2000) +normalize(subtract(divide(fnd72_pit_or_cf_a_cf_cash_from_fnc_act, fnd72_pit_or_is_a_is_net_inc_avail_com_shrhldrs), ts_rank(fnd44_perc_accruals, 6))) +group_neutralize(ts_rank(pv37_high_global2, 20), industry_grouping_level2_top2000) +multiply(zscore(divide(ts_std_dev(returns, 20), group_mean(ts_std_dev(returns, 20), sharesout, industry))), ts_mean(resampled_pca_factor_1, 20)) +ts_decay_linear(rank(ts_delta(share_capital_ordinary_quarterly, 10)), 5) +add(rank(ts_std_dev(returns, 20)), multiply(rank(ts_mean(daily_volume_percent_shares_out, 20)), 0.5)) +multiply(sign(ts_sum(fnd44_probability_restatement, 20)), ts_rank(fnd44_working_capital_accruals, 30)) +ts_std_dev(ts_mean(returns, 5), 20) +subtract(ts_rank(ts_delta(pv37_volume_global, 3), 20), group_mean(ts_rank(ts_delta(pv37_volume_global, 3), 20), 1, industry_grouping_level2_top2000)) +quantile(subtract(rank(divide(quarterly_cash_flow_per_share_2, add(net_income_quarterly_4, 0.01))), rank(fnd44_perc_accruals))) +normalize(srp_earnings_score) +abs(ts_std_dev(returns, 20)) +add(multiply(rank(ts_std_dev(returns, 20)), 2), multiply(rank(daily_volume_percent_shares_out), 1.5)) +normalize(add(divide(fnd72_pit_or_cf_q_cf_net_inc, fnd72_pit_or_is_q_net_income), inverse(ts_std_dev(quarterly_net_income_3, 36)))) +ts_target_tvr_delta_limit(ts_std_dev(returns, 20), ts_mean(returns, 20), 0, 1, 0.1) +rank(add(ts_scale(anl14_high_revenue_fy2, 20), reverse(ts_scale(anl14_low_ebit_fy5, 20)))) +rank(multiply(ts_std_dev(anl14_high_revenue_fy2, 20), inverse(ts_std_dev(anl14_low_ebit_fy5, 20)))) +normalize(add(divide(fnd72_pit_or_cf_a_cf_cash_from_fnc_act, fnd72_pit_or_is_a_is_net_inc_avail_com_shrhldrs), inverse(ts_rank(fnd44_working_capital_accruals, 6)))) +subtract(group_rank(pv37_volume_global, industry_grouping_level2_top2000), group_mean(group_rank(pv37_volume_global, industry_grouping_level2_top2000), 1, industry_grouping_level2_top2000)) +ts_corr(ts_std_dev(returns, 20), fnd31_ohlsonscore, 20) +rank(ts_corr(ts_mean(investment_contracts_total, 20), ts_mean(value_investment_assets_total, 20), 50)) +rank(ts_corr(ts_mean(fnd72_s_pit_or_cf_q_cf_cash_from_oper, 20), ts_mean(fnd72_s_pit_or_cf_q_cf_other_non_cash_adjust, 20), 50)) +normalize(divide(fnd72_s_pit_or_cf_q_cf_cash_from_oper, multiply(fnd72_s_pit_or_is_q_net_income, ts_std_dev(normalized_net_income_annual, 12)))) +reverse(ts_delta(rank(ts_std_dev(returns, 20)), 10)) +if_else(rank(daily_volume_percent_shares_out) < 0.5, rank(ts_std_dev(returns, 20)), reverse(rank(ts_std_dev(returns, 20)))) +subtract(group_max(ts_std_dev(returns, 20), industry_grouping_level2_top1500), group_min(ts_std_dev(returns, 20), industry_grouping_level2_top1500)) +ts_arg_min(ts_sum(fnd72_s_pit_or_bs_q_bs_future_min_oper_lease_oblig, 30), 60) +multiply(ts_std_dev(returns, 20), ts_std_dev(returns, 60)) +or(ts_std_dev(returns, 20) > 0, ts_mean(returns, 20) > 0) +group_neutralize(ts_sum(divide(ts_delta(pv37_volume_global, 1), ts_delay(pv37_volume_global, 1)), 10), industry_grouping_level2_top2000) +group_neutralize(ts_decay_linear(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 10), industry_grouping_level2_top2000) +zscore(divide(group_rank(anl14_high_nav_fy2, industry), ts_std_dev(anl14_low_ndebt_fy5, 20))) +normalize(divide(quarterly_price_to_sales_ratio, 1)) +normalize(ts_regression(anl14_high_ntp_fy1, daily_volume_percent_shares_out, 20)) +divide(divide(quarterly_free_cash_flow_per_share_2, add(net_income_quarterly_4, 0.01)), add(fnd44_perc_accruals, 0.01)) +kth_element(ts_std_dev(returns, 20), 20, 1) +zscore(subtract(1, divide(fnd44_perc_accruals, ts_mean(quarterly_net_income_available_to_common_2, 12)))) +add(rank(fnd44_perc_accruals), rank(divide(quarterly_cash_flow_per_share_2, add(net_income_quarterly_4, 0.01)))) +ts_arg_min(returns, 20) +group_neutralize(signed_power(ts_delta(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 3), 2), industry_grouping_level2_top2000) +rank(ts_corr(anl14_high_ebit_fp4, pv173_rawratiosmt5yzspreadchg60d, 20)) +group_neutralize(signed_power(ts_delta(log(pv37_volume_global), 5), 2), industry_grouping_level2_top2000) +rank(fnd44_working_capital_accruals) +zscore(divide(ts_std_dev(returns, 20), group_mean(ts_std_dev(returns, 20), sharesout, industry))) +ts_corr(ts_mean(returns, 5), ts_std_dev(returns, 20), 20) +group_neutralize(ts_delta(returns, 20), industry_grouping_level2_top2000) +subtract(ts_decay_linear(ts_zscore(pv37_volume_global, 20), 10), group_mean(ts_decay_linear(ts_zscore(pv37_volume_global, 20), 10), 1, industry_grouping_level2_top2000)) +ts_regression(anl14_high_ebit_fp4, pv173_zscoresatlas_unit_name, 20, 0, 1) +group_neutralize(ts_quantile(pv37_volume_global, 20, "gaussian"), industry_grouping_level2_top2000) +ts_max(rank(fnd44_working_capital_accruals), 252) +subtract(ts_rank(pv37_low_global, 20), group_mean(ts_rank(pv37_low_global, 20), 1, industry_grouping_level2_top2000)) +normalize(multiply(inverse(ts_std_dev(quarterly_operating_margin_percent, 18)), divide(quarterly_cash, net_income_current_period))) +multiply(ts_std_dev(returns, 20), zscore(fnd31_ohlsonscore)) +ts_quantile(returns, 20) +subtract(ts_regression(returns, group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 20, 0, 0), group_mean(ts_regression(returns, group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 20, 0, 0), 1, industry_grouping_level2_top2000)) +rank(multiply(divide(quarterly_cash_per_share_value, book_value_per_share_quarterly), sign(ts_delta(quarterly_receivables_turnover_2, 6)))) +ts_delay(rank(fnd44_working_capital_accruals), 21) +rank(divide(add(fnd72_pit_or_cf_q_cf_cash_from_oper, fnd72_pit_or_cf_a_cf_cash_from_fnc_act), add(fnd72_pit_or_is_q_net_income, abs(fnd44_perc_accruals)))) +rank(subtract(divide(quarterly_cash_per_share_3, quarterly_book_value_per_share_2), multiply(fnd44_probability_restatement, 0.05))) +normalize(multiply(signed_power(ts_delta(returns, 5), 2), sign(ts_delta(pv173_zscoresbondreturn20deqwt, 5)))) +winsorize(fnd44_perc_accruals, std=3) +group_neutralize(anl14_low_ebit_fy5, industry) +subtract(ts_arg_max(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 10), group_mean(ts_arg_max(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 10), 1, industry_grouping_level2_top2000)) +subtract(ts_rank(quarterly_long_term_debt_to_equity_ratio, 20), ts_rank(quarterly_total_debt_to_equity_2, 20)) +trade_when(ts_std_dev(returns, 20), ts_mean(returns, 20), ts_delta(ts_std_dev(returns, 20), 1)) +normalize(subtract(1, divide(fnd44_perc_accruals, ts_mean(fnd72_pit_or_is_a_is_net_inc_avail_com_shrhldrs, 36)))) +log(ts_std_dev(returns, 20)) +group_neutralize(add(multiply(rank(ts_std_dev(returns, 20)), 3), multiply(rank(ts_mean(daily_volume_to_shares_outstanding, 20)), 2)), industry_grouping_level2_top1500) +normalize(fnd31_devnorthamericaadditionalfactor4_pbdwf) +group_neutralize(ts_arg_max(pv37_volume_global, 10), industry_grouping_level2_top2000) +multiply(ts_std_dev(returns, 20), group_mean(ts_std_dev(returns, 20), 1, industry_grouping_level2_top1500)) +rank(ts_mean(daily_volume_percent_shares_out, 20)) +normalize(rank(subtract(divide(quarterly_cash_flow_per_share_2, add(fnd13_statementq_sopi, 0.01)), fnd44_working_capital_accruals))) +group_zscore(ts_std_dev(returns, 20), industry) +group_neutralize(ts_delta(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 5), industry_grouping_level2_top2000) +zscore(anl14_high_eps_fy2) +normalize(fnd31_devnorthamericaadditionalfactor4_tw_ebitdaev) +rank(divide(fnd72_pit_or_cf_q_cf_cash_from_oper, fnd72_pit_or_is_q_net_income)) +group_rank(ts_std_dev(returns, 20), industry) +group_neutralize(rank(fnd44_score), industry) +reverse(ts_std_dev(returns, 20)) +subtract(divide(ts_sum(pv37_volume_global, 5), ts_sum(pv37_volume_global, 20)), group_mean(divide(ts_sum(pv37_volume_global, 5), ts_sum(pv37_volume_global, 20)), 1, industry_grouping_level2_top2000)) +group_neutralize(ts_count_nans(pv37_volume_global, 20), industry_grouping_level2_top2000) +zscore(group_zscore(add(ts_std_dev(anl14_high_ntp_fy1, 20), ts_std_dev(resampled_pca_factor_1, 20)), industry)) +bucket(rank(ts_std_dev(returns, 20)), "0,1,0.1") +ts_decay_linear(multiply(signed_power(ts_delta(returns, 5), 2), daily_volume_percent_shares_out), 20) +ts_arg_min(ts_sum(fnd17_qcurratio2, 30), 60) +zscore(divide(quarterly_free_cash_flow, add(fnd72_pit_or_is_q_net_income, abs(fnd44_working_capital_accruals)))) +subtract(ts_sum(if_else(ts_delta(pv37_volume_global, 1) > 0, 1, -1), 10), group_mean(ts_sum(if_else(ts_delta(pv37_volume_global, 1) > 0, 1, -1), 10), 1, industry_grouping_level2_top2000)) +group_neutralize(multiply(ts_std_dev(returns, 20), ts_std_dev(returns, 60)), industry_grouping_level2_top1500) +ts_corr(returns, resampled_pca_factor_1, 20) +rank(ts_std_dev(quarterly_pe_ratio_high, 126)) +normalize(divide(quarterly_cash_flow_per_share_non_annualized, multiply(net_income_available_to_common_incl_extraordinary_annual, ts_std_dev(quarterly_operating_margin_percent, 6)))) +subtract(ts_quantile(pv37_volume_global, 20, "gaussian"), group_mean(ts_quantile(pv37_volume_global, 20, "gaussian"), 1, industry_grouping_level2_top2000)) +ts_delta(group_mean(ts_std_dev(returns, 20), 1, industry_grouping_level2_top1500), 10) +rank(quarterly_sga_to_sales_ratio) +zscore(divide(ts_sum(anl14_high_ebit_fy2, 20), ts_sum(anl14_low_eps_fy5, 20))) +normalize(add(inverse(ts_std_dev(normalized_net_income_available_to_common_annual, 18)), divide(quarterly_free_cash_flow_per_share_non_annualized, net_income_annual))) +normalize(divide(fnd17_pehigh, 1)) +quantile(ts_std_dev(returns, 20), gaussian, 1.0) +rank(add(add(rank(divide(quarterly_free_cash_flow_per_share_2, add(net_income_quarterly_4, 0.01))), rank(quarterly_asset_turnover)), subtract(rank(reverse(fnd44_perc_accruals)), rank(fnd44_probability_restatement)))) +subtract(ts_std_dev(returns, 20), group_mean(ts_std_dev(returns, 20), 1, industry_grouping_level2_top2000)) +ts_sum(ts_std_dev(returns, 20), 20) +group_neutralize(ts_backfill(pv37_volume_global, 20, 1, "NAN"), industry_grouping_level2_top2000) +rank(ts_std_dev(quarterly_asset_turnover, 252)) +group_neutralize(ts_covariance(returns, pv37_volume_global, 20), industry_grouping_level2_top2000) +subtract(add(ts_zscore(pv37_volume_global, 20), ts_zscore(returns, 20)), group_mean(add(ts_zscore(pv37_volume_global, 20), ts_zscore(returns, 20)), 1, industry_grouping_level2_top2000)) +min(ts_std_dev(returns, 20), ts_mean(returns, 20)) +ts_std_dev(ts_std_dev(returns, 20), 20) +zscore(divide(quarterly_total_assets, quarterly_total_liabilities)) +subtract(ts_rank(fnd72_s_pit_or_bs_q_bs_sh_cap_and_apic, 30), ts_rank(fnd72_s_pit_or_bs_q_bs_total_avail_line_of_credit, 30)) +ts_count_nans(returns, 20) +multiply(rank(ts_delta(fnd72_s_pit_or_is_q_is_invest_income, 5)), ts_rank(fnd72_s_pit_or_is_q_net_income, 10)) +zscore(subtract(1, divide(fnd44_working_capital_accruals, ts_mean(fnd72_pit_or_is_a_is_net_inc_avail_com_shrhldrs, 24)))) +zscore(divide(anl14_low_ebit_fy2, annual_total_revenue)) +ts_decay_linear(rank(ts_delta(fnd44_perc_accruals, 10)), 5) +zscore(divide(quarterly_tangible_book_value_per_share_3, 1)) +zscore(multiply(ts_arg_max(anl14_high_revenue_fy1, 20), ts_arg_min(anl14_low_ebit_fy4, 20))) +signed_power(subtract(rank(divide(quarterly_free_cash_flow_per_share_2, add(quarterly_earnings_before_tax_5, 0.01))), rank(fnd44_perc_accruals)), 2) +multiply(ts_std_dev(returns, 20), ts_mean(pv37_volume_14, 20)) +multiply(ts_std_dev(ts_std_dev(returns, 20), 20), ts_std_dev(returns, 20)) +zscore(ts_std_dev(returns, 20)) +zscore(add(divide(fnd72_pit_or_cf_a_cf_net_inc, fnd72_pit_or_is_a_is_net_inc_avail_com_shrhldrs), inverse(ts_rank(fnd44_working_capital_accruals, 36)))) +ts_arg_min(rank(fnd44_working_capital_accruals), 252) +zscore(quarterly_interest_coverage_ratio) +rank(subtract(1, divide(fnd44_perc_accruals, ts_mean(fnd72_pit_or_is_q_net_income, 12)))) +subtract(ts_product(ts_zscore(pv37_volume_global, 5), 5), group_mean(ts_product(ts_zscore(pv37_volume_global, 5), 5), 1, industry_grouping_level2_top2000)) +subtract(rank(ts_std_dev(returns, 20)), group_zscore(ts_std_dev(returns, 20), industry_grouping_level2_top1500)) +group_neutralize(ts_covariance(returns, group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 20), industry_grouping_level2_top2000) +rank(multiply(ts_covariance(returns, pv173_rawratiosbondreturn20deqwt, 20), sign(ts_delta(anl14_high_revenue_fy2, 5)))) +zscore(ts_corr(anl14_high_ebit_fp4, daily_volume_percent_shares_out, 20)) +subtract(divide(quarterly_free_cash_flow_per_share_2, add(quarterly_net_income_3, 0.01)), fnd44_perc_accruals) +ts_step(1) +zscore(divide(quarterly_cash_flow_per_share_2, add(net_income_quarterly_4, 0.01))) +ts_backfill(ts_std_dev(returns, 20), 20) +power(ts_std_dev(returns, 20), 2) +ts_min(rank(fnd44_perc_accruals), 252) +multiply(rank(ts_delta(quarterly_pe_ratio_high, 5)), ts_rank(quarterly_price_to_sales_ratio_3, 10)) +vec_avg(ts_std_dev(returns, 20)) +rank(subtract(ts_sum(anl14_high_ebit_fp4, 20), ts_sum(pv173_rawratiosmt5yzspreadchg60d, 20))) +ts_arg_max(ts_sum(short_term_debt_issued, 30), 60) +rank(divide(quarterly_net_income_available_to_common_2, quarterly_total_revenue_3)) +normalize(divide(anl69_best_net_gaap, 1)) +is_nan(ts_std_dev(returns, 20)) +subtract(subtract(ts_max(pv37_volume_global, 5), ts_min(pv37_volume_global, 5)), group_mean(subtract(ts_max(pv37_volume_global, 5), ts_min(pv37_volume_global, 5)), 1, industry_grouping_level2_top2000)) +subtract(ts_rank(operating_income_2, 20), ts_rank(sales_revenue_total, 20)) +zscore(divide(quarterly_cash_flow_per_share_alt_2, add(net_income_available_to_common_incl_extraordinary_annual, abs(fnd44_perc_accruals)))) +ts_product(ts_std_dev(returns, 20), 20) +group_neutralize(subtract(ts_mean(pv37_volume_global, 5), ts_mean(pv37_volume_global, 20)), industry_grouping_level2_top2000) +group_neutralize(ts_corr(pv37_volume_global, returns, 20), industry_grouping_level2_top2000) +group_neutralize(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), industry_grouping_level2_top2000) +signed_power(ts_std_dev(returns, 20), 2) +subtract(ts_arg_max(pv37_volume_global, 10), group_mean(ts_arg_max(pv37_volume_global, 10), 1, industry_grouping_level2_top2000)) +max(ts_std_dev(returns, 20), ts_mean(returns, 20)) +vec_sum(ts_std_dev(returns, 20)) +normalize(divide(quarterly_dividend_per_share, quarterly_earnings_before_tax)) +ts_delta(pv37_volume_14, 5) +subtract(ts_mean(pv37_volume_global, 20), group_mean(ts_mean(pv37_volume_global, 20), 1, industry_grouping_level2_top2000)) +group_neutralize(ts_std_dev(returns, 20), industry) +group_neutralize(ts_product(ts_zscore(pv37_volume_global, 5), 5), industry_grouping_level2_top2000) +ts_delta(rank(fnd44_perc_accruals), 21) +group_neutralize(ts_arg_max(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 10), industry_grouping_level2_top2000) +rank(ts_corr(ts_delta(fnd72_pit_or_bs_q_bs_accrued_income_taxes, 60), ts_delta(fnd72_pit_or_is_q_is_tot_cash_com_dvd, 60), 250)) +zscore(add(divide(fnd72_s_pit_or_cf_q_cf_cash_from_oper, fnd72_s_pit_or_is_q_net_income), inverse(ts_rank(fnd44_working_capital_accruals, 18)))) +ts_corr(ts_std_dev(returns, 20), daily_volume_percent_shares_out, 20) +subtract(ts_std_dev(returns, 10), ts_std_dev(returns, 60)) +group_neutralize(ts_rank(ts_delta(pv37_volume_global, 3), 20), industry_grouping_level2_top2000) +ts_mean(returns, 20) +subtract(ts_arg_min(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 10), group_mean(ts_arg_min(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 10), 1, industry_grouping_level2_top2000)) +divide(ts_std_dev(returns, 20), ts_mean(daily_volume_percent_shares_out, 20)) +normalize(ts_covariance(anl14_high_ntp_fy1, daily_volume_percent_shares_out, 20)) +rank(ts_std_dev(returns, 20), 2) +normalize(subtract(ts_mean(fnd72_pit_or_cf_q_cf_other_non_cash_adj_less, 20), ts_mean(fnd72_s_pit_or_cf_q_cf_incr_cap_stock, 20))) +subtract(subtract(ts_mean(pv37_volume_global, 5), ts_mean(pv37_volume_global, 20)), group_mean(subtract(ts_mean(pv37_volume_global, 5), ts_mean(pv37_volume_global, 20)), 1, industry_grouping_level2_top2000)) +subtract(divide(ts_mean(pv37_volume_global, 5), ts_std_dev(pv37_volume_global, 5)), group_mean(divide(ts_mean(pv37_volume_global, 5), ts_std_dev(pv37_volume_global, 5)), 1, industry_grouping_level2_top2000)) +group_neutralize(sign(ts_delta(pv37_volume_global, 1)), industry_grouping_level2_top2000) +ts_covariance(ts_std_dev(returns, 20), ts_mean(returns, 20), 20) +zscore(divide(quarterly_price_to_cash_flow_per_share_2, 1)) +group_neutralize(ts_mean(pv37_volume_global, 20), industry_grouping_level2_top2000) +group_neutralize(divide(ts_sum(pv37_volume_global, 5), ts_sum(pv37_volume_global, 20)), industry_grouping_level2_top2000) +multiply(rank(divide(quarterly_free_cash_flow_per_share_2, add(net_income_quarterly_4, 0.01))), rank(quarterly_current_ratio_alt)) +subtract(abs(ts_delta(log(pv37_volume_global), 5)), group_mean(abs(ts_delta(log(pv37_volume_global), 5)), 1, industry_grouping_level2_top2000)) +rank(ts_covariance(ts_mean(quarterly_cash_flow_per_share_2, 30), ts_mean(quarterly_free_cash_flow_per_share_2, 30), 60)) +rank(quarterly_return_on_assets_percent_2) +ts_mean(rank(divide(quarterly_cash_flow_per_share_2, add(net_income_quarterly_4, 0.01))), 252) +hump(ts_std_dev(returns, 20), 0.01) +rank(divide(quarterly_book_value_per_share_5, 1)) +ts_rank(pv37_volume_global, 20) +ts_decay_linear(group_zscore(divide(anl14_high_ebit_fp4, anl14_low_ntprep_fp4), industry), 20) +subtract(ts_sum(divide(ts_delta(pv37_volume_global, 1), ts_delay(pv37_volume_global, 1)), 10), group_mean(ts_sum(divide(ts_delta(pv37_volume_global, 1), ts_delay(pv37_volume_global, 1)), 10), 1, industry_grouping_level2_top2000)) +rank(ts_corr(ts_mean(quarterly_sga_to_sales_ratio, 20), ts_mean(quarterly_working_capital_per_share_to_price_2, 20), 50)) +group_neutralize(multiply(ts_arg_min(anl14_high_revenue_fy1, 20), ts_delta(pv173_zscoresbondreturn20deqwt, 5)), industry) +group_scale(ts_std_dev(returns, 20), industry) +group_neutralize(ts_product(ts_zscore(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 5), 5), industry_grouping_level2_top2000) +group_neutralize(multiply(ts_std_dev(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 20), ts_std_dev(returns, 20)), industry_grouping_level2_top2000) +zscore(subtract(1, divide(fnd44_perc_accruals, ts_mean(quarterly_net_income_available_to_common_2, 24)))) +subtract(ts_std_dev(pv37_volume_global, 20), group_mean(ts_std_dev(pv37_volume_global, 20), 1, industry_grouping_level2_top2000)) +rank(ts_std_dev(quarterly_asset_turnover, 126)) +ts_delta(ts_std_dev(returns, 20), 1) +ts_corr(returns, ts_backfill(anl14_high_eps_fp1, 250), 20) +ts_decay_linear(ts_std_dev(returns, 20), 10) +zscore(fnd31_devnorthamericaadditionalfactor4_apg) +group_neutralize(ts_scale(multiply(anl14_high_revenue_fp1, fnd31_qsg5additionalfactor3_monthly_pcdwf), 20), industry) +group_neutralize(ts_av_diff(multiply(anl14_high_eps_fp1, fnd31_devnorthamericaadditionalfactor4_pbdwf), 20), industry) +subtract(ts_corr(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), returns, 20), group_mean(ts_corr(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), returns, 20), 1, industry_grouping_level2_top2000)) +ts_zscore(group_rank(multiply(ts_delay(returns, 5), daily_volume_percent_shares_out), industry), 20) +normalize(multiply(ts_count_nans(anl14_high_ebit_fy2, 20), sign(ts_delta(anl14_low_eps_fy5, 5)))) +normalize(quarterly_quick_ratio) +group_scale(subtract(ts_quantile(daily_volume_percent_shares_out, 20), ts_quantile(anl14_low_ndebt_fy1, 20)), industry) +ts_zscore(ts_std_dev(returns, 20), 20) +rank(ts_std_dev(returns, 20)) +group_neutralize(divide(ts_sum(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 5), ts_sum(returns, 5)), industry_grouping_level2_top2000) +zscore(ts_backfill(anl14_high_revenue_fy2, 250)) +group_neutralize(multiply(ts_rank(anl14_low_ndebt_fy1, 20), ts_rank(fnd31_devnorthamericaadditionalfactor4_pbdwf, 20)), industry) +inverse(ts_std_dev(returns, 20)) +ts_product(rank(ts_std_dev(returns, 20)), 10) +zscore(multiply(divide(quarterly_cash_per_share_value, net_income_quarterly_4), inverse(ts_std_dev(quarterly_receivables_turnover_2, 24)))) +group_neutralize(if_else(ts_delta(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 1) > 0, ts_zscore(returns, 20), ts_zscore(returns, 20) * -1), industry_grouping_level2_top2000) +rank(quarterly_long_term_debt_to_equity_ratio) +ts_covariance(returns, resampled_pca_factor_1, 20) +rank(ts_std_dev(ttm_net_income_value, 252)) +multiply(rank(ts_delta(accounts_receivable_current_assets, 5)), ts_rank(accounts_total_current_assets, 10)) +multiply(ts_std_dev(returns, 20), ts_corr(returns, ts_delay(returns, 1), 20)) +multiply(sign(ts_delta(fnd72_pit_or_is_q_net_income, 10)), ts_rank(fnd72_pit_or_is_q_is_invest_income, 30)) +multiply(rank(ts_std_dev(returns, 20)), rank(daily_volume_to_shares_outstanding)) +ts_arg_max(rank(divide(quarterly_cash_flow_per_share_2, add(quarterly_earnings_before_tax_5, 0.01))), 252) +subtract(divide(ts_delay(pv37_volume_global, 5), ts_delay(pv37_volume_global, 10)), group_mean(divide(ts_delay(pv37_volume_global, 5), ts_delay(pv37_volume_global, 10)), 1, industry_grouping_level2_top2000)) +zscore(anl14_low_roe_fy2) +ts_backfill(add(ts_product(anl14_high_eps_fp1, 10), ts_product(anl14_low_roe_fy1, 10)), 20) +subtract(sign(ts_delta(pv37_volume_global, 1)), group_mean(sign(ts_delta(pv37_volume_global, 1)), 1, industry_grouping_level2_top2000)) +add(rank(ts_std_dev(returns, 10)), add(rank(ts_std_dev(returns, 20)), rank(ts_std_dev(returns, 40)))) +jump_decay(ts_std_dev(returns, 20), 20, 0.5, 0.1) +subtract(ts_std_dev(returns, 20), ts_std_dev(returns, 10)) +power(ts_sum(returns, 20), 2) +group_scale(add(ts_av_diff(industry_grouping_level2_top1500, 20), ts_av_diff(anl14_low_ntprep_fp4, 20)), industry) +rank(ts_covariance(ts_mean(long_term_liabilities_total_3, 30), ts_mean(long_term_minority_interest, 30), 60)) +ts_backfill(group_neutralize(divide(anl14_high_nav_fy2, anl14_low_ndebt_fy5), industry), 20) +ts_delta(ts_std_dev(returns, 20), 5) +sqrt(ts_std_dev(returns, 20)) +rank(add(divide(quarterly_free_cash_flow, quarterly_total_assets_change_percent), inverse(fnd44_working_capital_accruals))) +ts_arg_min(ts_sum(fnd31_qsg5additionalfactor3_monthly_tw_ebitdaev, 30), 60) +subtract(ts_rank(pv37_high_global2, 20), group_mean(ts_rank(pv37_high_global2, 20), 1, industry_grouping_level2_top2000)) +zscore(multiply(ts_quantile(fnd31_qsg5additionalfactor3_monthly_pcdwf, 20, "uniform"), inverse(ts_mean(anl14_low_ndebt_fy1, 20)))) +add(zscore(anl14_high_revenue_fy2), zscore(anl14_high_ebit_fy2)) diff --git a/prepare_prompt/alpha_prompt.txt b/prepare_prompt/alpha_prompt.txt index d5afbd6..75a23af 100644 --- a/prepare_prompt/alpha_prompt.txt +++ b/prepare_prompt/alpha_prompt.txt @@ -1,6 +1,9 @@ -研发资本化激进程度因子 -企业将研发支出过度资本化可短期美化利润，但该会计选择往往反映盈余管理动机，市场未能及时识别其不可持续性，导致股价高估。多空逻辑：做空研发资本化比例高且后续ROA下滑的企业，做多费用化处理、盈利稳健的企业。 -核心指标为资本化研发支出占总研发支出比重，需行业与市值双中性化；使用过去三年均值平滑异常值；引入专利引用数或新申请量作为创新真实性的验证变量。 +研究因子名称 +盈利质量溢价因子 +假设 +高质量的盈利通常表现为持续性、可预测性和较低的应计成分，这类公司往往具备更强的经营现金流支撑和更低的财务操纵风险。根据行为金融学中的“代表性启发”偏差，市场投资者容易过度关注账面利润而忽视盈利构成的质量，导致高质量盈利公司被系统性低估，从而在中长期产生超额收益。 +实施方案 +基于财报数据，构建一个综合盈利质量指标：首先计算应计利润占总利润的比例（应计比率），再结合经营性现金流与净利润的比值、盈利波动率以及资产周转稳定性等维度进行标准化加权，最终形成横截面上的排序因子。该因子值越高，代表公司盈利质量越好，预期未来收益越高。 *=========================================================================================* @@ -29,7 +32,8 @@ 重申：请确保所有表达式都使用WorldQuant WebSim平台函数，不要使用pandas、numpy或其他Python库函数。输出必须是一行有效的WQ表达式。 以下是我的账号有权限使用的操作符, 请严格按照操作符, 以及我提供的数据集, 进行生成,组合 50 个 alpha： 不要自行假设, 你需要用到的操作符 和 数据集, 必须从我提供给你的里面查找, 并严格按照里面的使用方法进行组合 - +!! 数据集使用要求尽量分散, 不要重复使用 +!! 数据集使用要求尽量分散, 不要重复使用 ================================================================= **操作符汇总 diff --git a/prepare_prompt/keys_text.txt b/prepare_prompt/keys_text.txt index d4d99cc..f819e21 100644 --- a/prepare_prompt/keys_text.txt +++ b/prepare_prompt/keys_text.txt @@ -1 +1,4 @@ -['capitalization', 'expense', 'profit', 'patent', 'innovation', 'accounting'] \ No newline at end of file +["accrual", "cashflow", "earnings", "quality", "profitability", "stability", "volatility", "turnover", "ratio", "persistence"] +Earnings Quality Premium Factor +High-quality earnings are typically characterized by persistence, predictability, and low accrual components, reflecting stronger operating cash flow support and lower risk of financial manipulation. Behavioral finance suggests that investors often suffer from representativeness bias—overemphasizing headline earnings while neglecting the underlying quality—leading to systematic underpricing of firms with high earnings quality and thus generating long-term abnormal returns. +Construct a composite earnings quality metric using financial statement data: first compute the ratio of accruals to total earnings, then integrate metrics such as operating cash flow to net income, earnings volatility, and asset turnover stability. Standardize and weight these components to form a cross-sectional ranking factor. Higher factor values indicate better earnings quality and are expected to predict higher future returns. \ No newline at end of file diff --git a/template_output.txt b/template_output.txt index b3efa96..673e17a 100644 --- a/template_output.txt +++ b/template_output.txt @@ -1,500 +1,495 @@ -ts_covariance(anl10_nersmun_2yf_2416, pv87_interval_bidsize_skewness, 60) -ts_covariance(fnd65_us5000_cusip_ebitdap, oth137_avg_txn_val, 60) -ts_covariance(anl15_ebtgics_s_cal_fy3_pe, pv104_insm_mean, 20) -ts_covariance(anl10_entfy1_pred_surps_v1_2442, pv87_interval_bidsize_median, 20) -ts_covariance(fnd72_pit_or_is_q_is_tot_cash_com_dvd, pv87_interval_bidsize_median, 60) -ts_covariance(anl15_cpsgics_gr_cal_fy3_pe, pv81_msni, 20) -ts_covariance(mdl211_ebtq_profitability_profitability5, pv103_mbds_topofbook_mbds_mean, 20) -ts_covariance(anl82_preq_profitability_profitability12, pv103_tbsz_mean, 250) -ts_covariance(anl14_mean_eps_fp5, pv64_trr_cur_mkt_cap, 20) -ts_covariance(anl14_high_eps_fy2, pv87_interval_bidsize_skewness, 250) -ts_covariance(anl14_high_ntprep_fy5, fnd65_totalcap_cusip_ohlsonscore, 5) -ts_covariance(anl82_delta_preq_q4_predict, pv81_zsbt, 250) -ts_covariance(mdl138_qpdi3_net_margin, mdl248_avg_txn_val, 5) -ts_covariance(anl15_ebt_gr_cal_fy3_gro, pv103_masz_mean, 5) -ts_covariance(mdl230_us5000_cusip_y5speq4rqsr, pv104_lbsz_mean, 20) -ts_covariance(pv20_ebt_indicator_4_feature7, pv52_asdaq_order_size_code, 250) -ts_covariance(mdl264_1l_qaeclg, oth567score_company_size_358, 250) -ts_covariance(pv20_ebt_indicator_4_feature7, fnd72_pit_or_cr_q_debt_to_mkt_cap, 60) -ts_covariance(oth432_acae_q_profitability_profitability9, pv87_interval_bidsizeinterval_slippage_rankcorrel, 5) -ts_covariance(pv87_epsyld_ltm_d_mean, pv103_htsz_mean, 60) -ts_covariance(anl44_orig_en_pretaxprofit_rep_prevalue, pv81_zsbt, 250) -ts_covariance(anl82_ebty_deltaprofitability_profitability11, fnd72_pit_or_cr_q_debt_to_mkt_cap, 20) -ts_covariance(mdl77_2spe1yfvc_cf, pv87_interval_bidsizeinterval_slippage_correlation, 250) -ts_covariance(fnd13_cinx, fund_total_size, 20) -ts_covariance(anl15_ebt_gr_18_m_cos_dn, pv104_hbsz_mean, 60) -ts_covariance(pv20_gps_indicator_9_feature8, oth551_beta_size, 60) -ts_covariance(anl49_valugaugeestimatingdifficultyrank, pv87_interval_asksize_std, 60) -ts_covariance(pv20_opr_indicator_o_feature11, pv87_interval_asksize_std, 60) -ts_covariance(mdl77_2valuemomemtummodel_reportedearningsmomentummodule, company_size_sentiment_score, 250) -ts_covariance(mdl230_allcap_sedol_yoychgroa, pv103_masz_mean, 60) -ts_covariance(fnd72_s_pit_or_bs_q_2_bs_other_cur_asset_less_prepay, oth567score_company_size, 60) -ts_covariance(mdl264_einn_l1, pv87_interval_asksize_std, 60) -ts_covariance(fnd31_devnorthamericaadditionalfactor4_divgp, pv81_zsbt, 250) -ts_covariance(fnd28_dvfq1_value_18264q, fnd65_allcap_sedol_ohlsonscore, 250) -ts_covariance(anl15_ebtgics_ind_12_m_1m_chg, pv81_trlm, 20) -ts_covariance(mdl177_liquidityriskfactor_mad3yttmni_alt, company_offer_total_size, 60) -ts_covariance(mdl77_ooearningsmomemtummodel_fc_y2repsg, pv87_interval_asksizeinterval_slippage_correlation, 60) -ts_covariance(oth432_deltapredictebt_a_predict, oth551_r2_size, 20) -ts_covariance(pv87_daily_ann_matrix_r1_net_income_normalized_consensus_mean_numup, pv52_yse_arca_order_size_code, 5) -ts_covariance(anl69_rec_expected_report_time, mdl230_allcap_sedol_ohlsonscore, 5) -ts_covariance(fnd23_icsm_mvb_fbbs, pv103_sasz_mean, 5) -ts_covariance(pe_ratio_relative_component_rank_v2, pv87_interval_asksizeinterval_bidsize_correlation, 20) -ts_covariance(pv20_eps_indicator_4_feature2, pv104_hasz_mean, 20) -ts_covariance(mdl230_us5000_cusip_pspeghc, pv52_yse_arca_order_size_code, 60) -ts_covariance(fnd3_A_nic, pv87_interval_asksize_numintervalsincehigh, 60) -ts_covariance(anl14_median_ntp_fy5, pv81_zsah, 250) -ts_covariance(mdl211_oprq_deltaprofitability_profitability5, pv87_interval_bidsize_numintervalsincelow, 5) -ts_covariance(anl82_delta_preq_q2_predict, fnd72_s_pit_or_cr_q_debt_to_mkt_cap, 20) -ts_covariance(fnd28_wcishta_value_18189a, pv103_taks_mean, 20) -ts_covariance(mdl354_sector_pt2net_margin, pv87_interval_asksize_median, 60) -ts_covariance(anl14_median_ntp_fy5, nws31_d1_bodysize, 5) -ts_covariance(fnd65_us5000_cusip_chgroepct, oth137_avg_txn_val, 250) -ts_covariance(est_q_gps_mean_3mth_ago, nws31_bodysize, 60) -ts_covariance(pv87_2_operatingprofit_qf_matrix_all_chngratio_mean, pv81_zsbm, 250) -ts_covariance(anl49_1stfiscalquarterearningspershare, pv87_interval_bidsize_mean, 250) -ts_covariance(fnd6_cibegni, pv52_yse_national_order_size_code, 250) -ts_covariance(pv87_2_eps_af_matrix_p1_chngratio_high, rsk70_mfm2_usfast_size, 20) -ts_covariance(mdl211_preq_deltaprofitability_profitability1, pv87_interval_asksize_kurtosis, 250) -ts_covariance(mdl26_v14_srprs_pct_lst_y_rnngs, pv87_interval_asksizeinterval_slippage_correlation, 250) -ts_covariance(est_q_pre_std_4wks_ago, pv52_yse_american_order_size_code, 60) -ts_covariance(fnd31_qsg5additionalfactor3_backfilled_rev6fy2, pv64_out_stal_fund_creation_unit_size, 5) -ts_covariance(mdl230_totalcap_cusip_pfgmtt, pv103_htsz_mean, 20) -ts_covariance(pv20_pre_indicator_8_feature11, pv103_tbsz_mean, 250) -ts_covariance(mdl211_saly_profitability_profitability5, mdl177_liquidityriskfactor_ohlsonscore_alt, 60) -ts_covariance(mdl26_stm_f12m_rnngs, pv103_1m_mbds_mean, 5) -ts_covariance(oth460_3l_ifpv, oth551_beta_size, 60) -ts_covariance(mdl262_oibdpq_profitability_profitability8, pv103_tbds_mean, 250) -ts_covariance(fnd72_s_pit_or_is_q_is_pension_expense_income, pv87_interval_asksizeinterval_volume_correlation, 5) -ts_covariance(fnd3_a__earbeforetaxes, pv52_yse_order_size_code, 250) -ts_covariance(oth432_che_profitability_profitability2, pv103_sbps_mean, 20) -ts_covariance(pv87_2_pretaxprofit_qf_matrix_all_chngratio_high, pv104_tbsz_mean, 250) -ts_covariance(pv20_gps_indicator_9_feature11, pv104_lsbs_mean, 20) -ts_covariance(mdl26_mstchg_pct_f12m_rnngs_14, pv52_asdaq_order_size_code, 5) -ts_covariance(fnd28_wsbshtq_value_01551q, pv64_trr_stal_cur_mkt_cap, 20) -ts_covariance(mdl264_viat_class, pv81_zsbh, 60) -ts_covariance(mdl262_cheq_profitability_profitability3, pv103_lbsz_mean, 5) -ts_covariance(oth460_uniamiq_l3, company_offer_total_size, 60) -ts_covariance(act_q_gps_surprisestd, fund_total_size, 5) -ts_covariance(anl14_low_ntprep_fy2, pv87_interval_asksizeinterval_bidsize_correlation, 250) -ts_covariance(mdl262_deltapredictni_a_mae, pv87_interval_asksizeinterval_bidsize_correlation, 20) -ts_covariance(mdl77_2deepvaluefactor_vesspem21f, pv103_sasz_mean, 5) -ts_covariance(oth432_rasv2splitprofitabilityfcf_ttm_profitability3, mdl77_liquidityriskfactor_ohlsonscore, 60) -ts_covariance(anl69_roa_expected_report_time, pv64_dif_stal_fund_creation_unit_size, 5) -ts_covariance(fnd14_hiwater_incmst_net_income_fy, pv104_lsts_mean, 5) -ts_covariance(anl10_gpspast_det_analyst_1321, pv81_zsbl, 5) -ts_covariance(fnd17_ttmnichg, pv103_saps_mean, 5) -ts_covariance(mdl230_totalcap_cusip_spe2yfvc_cf, fnd31_ohlsonscore, 20) -ts_covariance(fnd3_Q_interestincome, pv103_sbsz_mean, 5) -ts_covariance(anl49_backfill_typeofearningspershare, pv104_tbsz_mean, 20) -ts_covariance(pv20_eps_indicator_1_feature7, fund_total_size, 250) -ts_covariance(count_negative_profitability_question, pv104_masz_mean, 60) -ts_covariance(pv87_daily_qtr_matrix_net_income_gaap_consensus_mean_numdown, pv87_interval_asksizeinterval_bidsize_correlation, 20) -ts_covariance(anl15_ebtgics_ind_cal_fy3_3m_chg, pv103_1m_mlrt_mean, 20) -ts_covariance(mdl77_earningmomentumfactor_stdevfy1epsp, pv64_trr_stal_cur_mkt_cap, 60) -ts_covariance(mdl77_historicalgrowthfactor_pspeghc, oth567_deltascore_company_size_432, 20) -ts_covariance(fnd65_allcap_sedol_fqsurstd60dlag, pv98_daily_mean_taks_taks_mean, 5) -ts_covariance(mdl138_in_3idp, pv52_yse_chicago_order_size_code, 20) -ts_covariance(mdl77_fa_pge_cf, pv52_yse_order_size_code, 250) -ts_covariance(fnd28_wcfsq1nbf_value_01401q, mdl248_avg_txn_val, 20) -ts_covariance(oth460_unopincq_l3, fund_total_size, 250) -ts_covariance(mdl177_2_globaldevnorthamerica_v502_fc_epsrm, pv52_asdaq_order_size_code, 250) -ts_covariance(fnd28_statisticsa_value_05290a, pv103_masz_mean, 60) -ts_covariance(investment_income_nonoperating, mdl77_2liquidityriskfactor_ohlsonscore, 20) -ts_covariance(fnd65_us5000_cusip_fqsurstd60dlag, pv81_sasl, 250) -ts_covariance(fnd7_ointfund_qipon, pv103_lbsz_mean, 5) -ts_covariance(pv87_webv2_expavg20_group_css_earnings, mdl177_2_liquidityriskfactor_ohlsonscore, 60) -ts_covariance(fnd28_pftlta_value_08365a, pv103_1m_maks_mean, 250) -ts_covariance(mdl264_3l_21fspe, pv103_tbsz_mean, 250) -ts_covariance(fnd72_s_pit_or_cr_q_trail_12m_eps, pv52_asdaq_order_size_code, 5) -ts_covariance(pv20_gps_indicator_q_feature8, oth551_beta_size, 5) -ts_covariance(mdl77_earningsmomemtummodel_fc_y2repsg, pv64_dif_stal_fund_creation_unit_size, 250) -ts_covariance(est_12m_opr_std, pv87_interval_asksizeinterval_slippage_rankcorrel, 20) -ts_covariance(pv87_2_operatingprofit_qf_matrix_p1_dts, pv81_zsbm, 5) -ts_covariance(anl49_netprofit, pv81_trlm, 20) -ts_covariance(mdl77_2min2ygrossmargin, oth137_avg_txn_val, 20) -ts_covariance(oth432_ebit_compustatdeltapredict_annual_funda_predict, pv103_mbsz_mean, 5) -ts_covariance(anl14_high_ebit_fp4, pv87_interval_bidsizeinterval_slippage_rankcorrel, 20) -ts_covariance(pv20_eps_indicator_8_feature10, pv81_trlt, 250) -ts_covariance(anl82_opry_profitability_profitability12, pv87_interval_bidsizeinterval_slippage_rankcorrel, 250) -ts_covariance(fnd65_allcap_sedol_lagegp, pv104_lasz_mean, 20) -ts_covariance(anl15_ebt_gr_18_m_mean, oth567score_company_size, 250) -ts_covariance(mdl26_trailing_4_quarter_pe, pv103_mbsz_mean, 20) -ts_covariance(pv87_2_operatingprofit_qf_matrix_p1_median, pv52_asdaq_bx_order_size_code, 5) -ts_covariance(mdl77_historicalgrowthfactor_chg3yepsast, pv103_1m_mlrt_mean, 250) -ts_covariance(pv20_gps_indicator_n_feature5, mdl77_liquidityriskfactor_ohlsonscore, 60) -ts_covariance(pv20_net_indicator_n_feature3, pv64_dif_fund_creation_unit_size, 250) -ts_covariance(mdl77_omomemtumanalystmodel_qma_repearnmom, pv87_interval_asksizeinterval_slippage_correlation, 20) -ts_covariance(anl14_low_ntprep_fy2, pv103_laks_mean, 60) -ts_covariance(oth460_2l_eiuv, pv103_maus_mean, 5) -ts_covariance(oth432_oibdpq_profitability_profitability4, pv103_tbds_mean, 250) -ts_covariance(anl69_roe_expected_report_time, pv103_tbds_mean, 5) -ts_covariance(fnd65_allcap_sedol_pspeghc, pv87_interval_asksize_mean, 20) -ts_covariance(anl10_ebtfq1_smart_ests_v1_936, pv87_interval_bidsize_std, 60) -ts_covariance(pv87_2_eps_qf_matrix_all_high, pv87_interval_asksizeinterval_bidsize_rankcorrel, 60) -ts_covariance(mdl26_rm02fy_prsprs_fy2_rnngs, pv98_daily_mean_taks_taks_mean, 20) -ts_covariance(fnd72_pit_or_cr_a_prof_margin, pv81_trlt, 20) -ts_covariance(pv20_gps_indicator_1_feature8, pv103_tbsz_mean, 250) -ts_covariance(anl4_qfv4_eps_number, pv81_stlh, 5) -ts_covariance(eps_fiscal_quarter, nws31_d1_bodysize, 60) -ts_covariance(pv87_simpleavg20_group_css_earnings, pv87_interval_asksize_std, 60) -ts_covariance(pv87_2_pretaxprofit_qf_matrix_all_chngratio_median, pv87_interval_bidsizeinterval_slippage_rankcorrel, 60) -ts_covariance(mdl177_valanalystmodel_qva_earnquality, pv52_asdaq_psx_order_size_code, 60) -ts_covariance(mdl77_oearningmomentumfactor_rev6, pv103_tasz_mean, 250) -ts_covariance(oth553_ptg_wordcnt, pv52_yse_arca_order_size_code, 250) -ts_covariance(anl14_mean_ntprep_fp5, pv52_yse_national_order_size_code, 250) -ts_covariance(mdl264_sani_l3, pv87_interval_bidsize_kurtosis, 60) -ts_covariance(oth432_accruals_oeps12_predict, pv52_yse_american_order_size_code, 60) -ts_covariance(anl82_nety_profitability_profitability10, pv64_out_stal_fund_creation_unit_size, 5) -ts_covariance(anl15_ebtgics_s_fy3_cos_dn, pv87_interval_asksizeinterval_volume_correlation, 60) -ts_covariance(pv87_2_pretaxprofit_rep_af_matrix_all_chngratio_high, pv81_zsbh, 250) -ts_covariance(fnd3_aacctadj__earbeforetaxes, pv103_tasz_mean, 250) -ts_covariance(anl10_ebiinnovation_score_fy2_2589, fnd72_pit_or_cr_q_debt_to_mkt_cap, 60) -ts_covariance(pv87_2_epsr_qf_matrix_all_high, pv64_trr_stal_cur_mkt_cap, 60) -ts_covariance(anl49_earningspershare, pv103_mbus_mean, 250) -ts_covariance(fnd72_pit_or_cr_a_net_inc_growth, pv52_yse_national_order_size_code, 20) -ts_covariance(mdl262_niq_compustatdeltapredict_funda_mada, pv87_interval_asksizeinterval_slippage_correlation, 5) -ts_covariance(fnd28_wcfsa1_value_01266a, pv103_tbds_mean, 5) -ts_covariance(fnd65_totalcap_cusip_fc_numrevq1, rsk70_mfm2_usfast_size, 5) -ts_covariance(mdl230_us5000_cusip_ebitdadebtchg, fnd31_ohlsonscore, 20) -ts_covariance(pv87_2_epsr_af_matrix_p1_dts, pv103_tasz_mean, 5) -ts_covariance(anl10_nerpast_det_indicator_913, pv64_dif_fund_creation_unit_size, 5) -ts_covariance(anl44_second_en_netprofit_rep_coveredby, pv103_lasz_mean, 250) -ts_covariance(fnd7_ointfund_rq21hsc, company_offer_total_size, 5) -ts_covariance(pv20_ebt_indicator_2_feature9, pv104_tasz_mean, 60) -ts_covariance(gross_profit_total, pv103_tasz_mean, 250) -ts_covariance(mdl26_v14_nnlyst_rvsng_dwn_fq1_rnngs_7, pv64_trr_stal_cur_mkt_cap, 5) -ts_covariance(oth460_usa_compustat_q1_dl8_ibadjq_l2, pv87_interval_asksizeinterval_bidsize_correlation, 250) -ts_covariance(anl4_epsr_value, pv103_mbus_mean, 20) -ts_covariance(oth460_ibadjq_l3, pv81_stsl, 20) -ts_covariance(pv20_net_indicator_n_feature6, pv52_yse_american_order_size_code, 20) -ts_covariance(anl10_ebspast_det_excflag, pv104_masz_mean, 5) -ts_covariance(fnd89_jones_accrual_major_11, pv87_interval_asksize_mean, 60) -ts_covariance(fnd28_annualgrowth_value_08601a, pv81_zsbl, 5) -ts_covariance(anl14_numofests_ebitda_fp4, pv104_masz_mean, 5) -ts_covariance(mdl177_valueanalystmodel_qva_earnval_alt, oth137_avg_txn_val, 250) -ts_covariance(mdl26_mn_f_rvsnclstr_nlysts_fy2_rnngs, oth567_deltascore_company_size_432, 20) -ts_covariance(pv87_2_netprofit_rep_qf_matrix_p1_median, fnd31_ohlsonscore, 5) -ts_covariance(fnd72_q1_geo_grow_ebitda_cap_exp_to_int, pv81_trlm, 250) -ts_covariance(mdl230_us5000_cusip_ebitdadebt, pv64_dif_stal_fund_creation_unit_size, 60) -ts_covariance(oth460_net_margin_l2, mdl177_2_liquidityriskfactor_ohlsonscore, 250) -ts_covariance(mdl77_valuemomemtummodel_earningsqualitymodule, pv52_yse_arca_order_size_code, 5) -ts_covariance(pv87_epsyld_gro_mean, pv103_1m_mbds_mean, 20) -ts_covariance(mdl262_compustatpredictivetxtq_prediction, pv104_tbsz_mean, 250) -ts_covariance(mdl262_txtq_compustatdeltapredict_funda_predict, pv98_tbds_mean, 5) -ts_covariance(mdl230_allcap_sedol_saleeps, fund_total_size, 20) -ts_covariance(mdl262_rasv2splitprofitabilityebitd_ttm_profitability9, oth567score_company_size_358, 250) -ts_covariance(anl10_ebiinnovation_score_fy2_2589, fnd65_allcap_sedol_ohlsonscore, 20) -ts_covariance(ern6_actual_period, pv103_hasz_mean, 20) -ts_covariance(mdl262_rasv2splitprofitabilityebitd_q_profitability9, pv87_interval_asksize_median, 60) -ts_covariance(mdl262_sbda_a_profitability_profitability4, pv87_interval_asksize_numintervalsincehigh, 5) -ts_covariance(anl69_eps_best_eps, fnd72_pit_or_cr_a_debt_to_mkt_cap, 60) -ts_covariance(fnd28_wcfsq1nbf_value_01706q, pv98_daily_mean_taks_taks_mean, 20) -ts_covariance(fnd72_pit_or_cr_a_trail_12m_oper_inc, pv87_interval_asksizeinterval_bidsize_correlation, 5) -ts_covariance(oth460_ttax_l3, pv87_interval_bidsizeinterval_volume_correlation, 60) -ts_covariance(oth432_txtq_mae, oth551_resret_size, 60) -ts_covariance(anl82_preq_deltaprofitability_profitability11, company_offer_total_size, 20) -ts_covariance(fnd6_newa1v1300_ibadj, oth567score_company_size_358, 20) -ts_covariance(star_eq_ope_rank, pv98_daily_mean_taks_taks_mean, 5) -ts_covariance(anl15_dps_gr_cal_fy1_pe, oth551_r2_size, 5) -ts_covariance(mdl26_dsnc_prd_srprs_flg_chg_fq4_rnngs, pv104_insm_mean, 60) -ts_covariance(anl49_backfill_estdcurrentperatio, fnd72_s_pit_or_cr_q_debt_to_mkt_cap, 60) -ts_covariance(mdl77_2mqf_yoychggpm, fnd65_totalcap_cusip_ohlsonscore, 60) -ts_covariance(pv20_pre_indicator_3_feature0, pv87_interval_bidsize_median, 250) -ts_covariance(anl14_numofests_opp_fp1, fnd14_zs_lf_nlf_drp_ncr, 5) -ts_covariance(anl14_mean_ntp_fy1, mdl177_2_liquidityriskfactor_ohlsonscore, 250) -ts_covariance(mdl211_preq_profitability_profitability7, company_size_sentiment_score, 5) -ts_covariance(fnd65_us5000_cusip_pfcmtt, pv103_masz_mean, 5) -ts_covariance(mdl177_historicalgrowthfactor_chgeps, pv103_sbsz_mean, 20) -ts_covariance(mdl109_b_mtl_dlyspe, pv103_1m_mlrt_mean, 60) -ts_covariance(oth432_compustatpredictiveniq_prediction, pv103_tasz_mean, 5) -ts_covariance(mdl211_nety_y1_mada, mdl177_2_liquidityriskfactor_ohlsonscore, 20) -ts_covariance(pv87_2_operatingprofit_qf_matrix_all_low, mdl230_totalcap_cusip_ohlsonscore, 250) -ts_covariance(pv20_ebt_indicator_7_feature6, pv87_interval_asksize_skewness, 60) -ts_covariance(anl82_preq_q4_madp, pv103_lasz_mean, 250) -ts_covariance(anl82_delta_netq_q1_predict, pv81_lasz, 250) -ts_covariance(mdl230_totalcap_cusip_epschgetr, oth551_resret_size, 20) -ts_covariance(mdl26_7dy_bld_stmt_flg_fq4_rnngs, pv104_lsbs_mean, 250) -ts_covariance(fnd89_jones_accrual_pct_major_13, oth567score_company_size, 20) -ts_covariance(anl4_netprofit_mean, pv103_maks_topofbook_maks_mean, 250) -ts_covariance(act_12m_prr_value, pv87_interval_bidsize_mean, 60) -ts_covariance(fnd72_s_pit_or_cr_q_ev_to_t12m_ebit, pv104_tlrt_mean, 20) -ts_covariance(mdl211_ebtq_deltaprofitability_profitability11, pv103_sbps_mean, 250) -ts_covariance(pv87_2_pretaxprofit_rep_af_matrix_p1_chngratio_number, pv87_interval_bidsize_skewness, 250) -ts_covariance(anl82_oprq_q4_madp, fnd31_ohlsonscore, 20) -ts_covariance(pv87_2_netprofit_rep_af_matrix_all_low, pv103_mbsz_mean, 60) -ts_covariance(anl15_ebt_gr_cal_fy3_cos, im_shares_transacted, 60) -ts_covariance(pv20_indicator_4_feature8, mdl77_liquidityriskfactor_ohlsonscore, 20) -ts_covariance(max_ebit_guidance, mdl230_allcap_sedol_ohlsonscore, 20) -ts_covariance(mdl26_rm02fy_prsprs_fy2_rnngs, pv52_asdaq_order_size_code, 250) -ts_covariance(fnd90_us_qes_gamef_grossprofit_asset, pv103_tbsz_mean, 60) -ts_covariance(anl15_ebt_gr_18_m_6m_chg, rsk70_mfm2_usfast_size, 60) -ts_covariance(gross_profit_to_assets_ratio, pv98_tbds_mean, 250) -ts_covariance(fnd17_margin5yr, rsk70_mfm2_usfast_size, 20) -ts_covariance(fnd23_annfv1a_nimv, pv103_lasz_mean, 5) -ts_covariance(oth395_major_12_13, nws31_bodysize, 60) -ts_covariance(mdl264_2l_qic, pv104_tasz_mean, 5) -ts_covariance(mdl230_us5000_cusip_spe2yfvc_cf, pv64_trr_stal_cur_mkt_cap, 60) -ts_covariance(anl14_numofests_ntprep_fp1, pv87_interval_asksize_mean, 250) -ts_covariance(mdl262_rasv2splitprofitabilityni_q_profitability3, pv64_trr_cur_mkt_cap, 250) -ts_covariance(pv20_pre_indicator_6_feature10, fund_total_size, 20) -ts_covariance(mdl26_mstchg_pct_fq2_rnngs_60, pv52_yse_order_size_code, 20) -ts_covariance(mdl264_1l_declg, fund_total_size, 5) -ts_covariance(anl14_high_ntprep_fy3, mdl230_us5000_cusip_ohlsonscore, 5) -ts_covariance(pv87_ann_matrix_net_income_gaap_estimate_high, pv52_yse_national_order_size_code, 250) -ts_covariance(pv87_daily_qtr_matrix_net_income_gaap_consensus_mean_numup, pv87_interval_bidsize_std, 20) -ts_covariance(mdl26_nnlyst_rvsng_p_fy1_rnngs_7, mdl248_avg_txn_val, 60) -ts_covariance(mdl26_v14_prsprise_pct_fq2_earnings, rsk70_mfm2_usfast_size, 250) -ts_covariance(pv87_2_operatingprofit_af_matrix_p1_chngratio_mean, pv103_sbps_mean, 5) -ts_covariance(anl14_median_ebit_fy4, pv87_interval_asksize_numintervalsincehigh, 250) -ts_covariance(anl15_dps_gr_12_m_pe, nws31_d1_bodysize, 60) -ts_covariance(mdl77_liquidityriskfactor_impduration, pv52_asdaq_psx_order_size_code, 60) -ts_covariance(pv87_2_netprofit_af_matrix_all_dts, pv103_1m_tlrt_mean, 250) -ts_covariance(oth432_oiadpq_profitability_profitability1, pv87_interval_asksizeinterval_bidsize_rankcorrel, 250) -ts_covariance(fnd72_q1_inc_tax_exp_to_net_sales, pv103_lbsz_mean, 60) -ts_covariance(pv20_ebt_indicator_7_feature2, pv87_interval_bidsize_kurtosis, 5) -ts_covariance(mdl177_2_earningsqualityfactor_saleeps, nws31_d1_bodysize, 20) -ts_covariance(mdl262_txtq_compustatdeltapredict_funda_mada, im_shares_transacted, 5) -ts_covariance(anl10_epsinnovation_score_fy1, pv103_tbsz_mean, 5) -ts_covariance(anl14_high_eps_fy4, company_size_sentiment_score, 20) -ts_covariance(mdl77_ohistoricalgrowthfactor_speghc, pv87_interval_asksizeinterval_slippage_correlation, 20) -ts_covariance(earnings_news_mention_count, pv104_lsbs_mean, 250) -ts_covariance(est_q_opr_num_28d, fund_total_size, 250) -ts_covariance(fnd72_s_pit_or_cr_q_trail_12m_inc_bef_xo_item, pv103_tbds_mean, 20) -ts_covariance(fnd72_a2_retention_ratio, fund_total_size, 250) -ts_covariance(mdl77_oindustryrrelativevaluefactor_curindcoreepsp_, mdl77_2liquidityriskfactor_ohlsonscore, 5) -ts_covariance(mdl262_deltapredictebt_q_predict, pv104_hasz_mean, 5) -ts_covariance(oth432_ninc_trkdpitdeltapredict_funda_mae, pv81_zsbm, 60) -ts_covariance(est_q_opr_mean_28d, pv103_maks_topofbook_maks_mean, 20) -ts_covariance(fnd6_newa2v1300_ni, pv104_mlrt_mean, 60) -ts_covariance(ebit_reported_value, pv87_interval_asksizeinterval_bidsize_correlation, 20) -ts_covariance(pv20_ebt_indicator_n_feature4, pv87_interval_bidsize_std, 5) -ts_covariance(mdl177_v1_400_sue, pv64_dif_stal_fund_creation_unit_size, 5) -ts_covariance(anl49_backfill_earningspershareindicator, pv81_zsbl, 250) -ts_covariance(anl15_ebtgics_gr_cal_fy2_gro, oth551_r2_size, 60) -ts_covariance(anl10_ebsfq1_consensus, oth551_r2_size, 250) -ts_covariance(mdl262_che_profitability_profitability9, pv104_mlrt_mean, 5) -ts_covariance(est_12m_ner_std_28d, pv81_lasz, 250) -ts_covariance(mdl262_sbda_a_profitability_profitability2, pv103_laks_mean, 20) -ts_covariance(rsk62_id_spe, pv104_tasz_mean, 250) -ts_covariance(fnd72_pit_or_cr_a_ev_to_t12m_ebitda, pv103_laks_mean, 250) -ts_covariance(oth432_rasv2splitprofitabilityebt_ttm_profitability4, pv87_interval_bidsize_numintervalsincelow, 20) -ts_covariance(pv87_2_netprofit_af_matrix_p1_chngratio_std, pv104_hbsz_mean, 60) -ts_covariance(anl10_ebismun_2qf_2231, pv52_yse_national_order_size_code, 250) -ts_covariance(pv87_neg_earnings_matrix_css_sum, pv103_1m_maks_mean, 60) -ts_covariance(mdl262_rasv2splitprofitabilityebitd_q_profitability10, pv103_1m_maks_mean, 250) -ts_covariance(mdl264_usa_compustat_q1_dl8_ibadjq_l3, pv64_dif_fund_creation_unit_size, 250) -ts_covariance(oth460_usa_compustat_q1_dl8_epsfiq_l5, mdl177_2_liquidityriskfactor_ohlsonscore, 5) -ts_covariance(mdl77_2ccacw, pv98_quote_size_mean, 5) -ts_covariance(mdl177_2_liquidityriskfactor_oplev, pv87_interval_asksize_kurtosis, 250) -ts_covariance(pv20_net_indicator_q_feature7, pv103_laks_mean, 250) -ts_covariance(pv87_prv2_expavg60_group_event_sentiment_score_earnings, pv103_hasz_mean, 250) -ts_covariance(fnd28_newa1_value_08385a, pv103_1m_mbds_mean, 250) -ts_covariance(mdl177_2_globaldevnorthamerica_v502_wcacc, pv87_interval_asksize_mean, 5) -ts_covariance(fnd6_txs, pv52_yse_arca_order_size_code, 250) -ts_covariance(mdl26_nm_stm_nlysts_fy2_rnngs, mdl230_allcap_sedol_ohlsonscore, 250) -ts_covariance(mdl262_che_profitability_profitability5, pv98_quote_size_mean, 5) -ts_covariance(mdl77_ooearningsmomemtummodel_fc_numrevy1, pv81_stlh, 250) -ts_covariance(fnd65_totalcap_cusip_pe_wt, pv87_interval_bidsize_kurtosis, 250) -ts_covariance(pv20_gps_indicator_6_feature10, pv103_maus_mean, 60) -ts_covariance(fnd28_wcfsa1_value_01503a, mdl230_allcap_sedol_ohlsonscore, 250) -ts_covariance(mdl230_allcap_sedol_pedwf_cf, pv87_interval_asksizeinterval_slippage_rankcorrel, 20) -ts_covariance(mdl262_rasv2splitprofitabilityfcf_q_profitability9, nws31_d1_bodysize, 60) -ts_covariance(mdl77_momemtumanalystmodel_qma_composite, fnd65_allcap_sedol_ohlsonscore, 20) -ts_covariance(anl10_ebsfy2_smart_ests_v1, pv87_interval_bidsizeinterval_slippage_correlation, 60) -ts_covariance(oth432_compustatpredictiveepsfiq_mad_pred, pv87_interval_asksize_mean, 5) -ts_covariance(anl15_ebt_ind_cal_fy1_1m_chg, rsk70_mfm2_usfast_size, 5) -ts_covariance(pv20_net_indicator_8_feature12, pv104_lbsz_mean, 20) -ts_covariance(fnd65_allcap_sedol_bmpo, pv103_masz_mean, 250) -ts_covariance(pv20_eps_indicator_1_feature2, pv87_interval_asksize_median, 5) -ts_covariance(anl14_stddev_opp_fy1, oth551_resret_size, 250) -ts_covariance(anl69_eps_best_eps_lo, pv103_1m_mlrt_mean, 5) -ts_covariance(anl82_netq_deltaprofitability_profitability2, mdl230_totalcap_cusip_ohlsonscore, 20) -ts_covariance(mdl264_net_margin_l2, pv52_yse_arca_order_size_code, 60) -ts_covariance(fnd72_s_pit_or_is_a_is_rd_expend, mdl77_2liquidityriskfactor_ohlsonscore, 20) -ts_covariance(est_12m_opr_mean_3mth_ago, pv81_zsbm, 60) -ts_covariance(mdl177_2_earningsqualityfactor_salegpm, pv104_lsbs_mean, 5) -ts_covariance(anl82_saly_deltaprofitability_profitability8, fnd72_pit_or_cr_a_debt_to_mkt_cap, 60) -ts_covariance(mdl262_compustatpredictiveoiadpq_mad_pred, pv104_tbsz_mean, 60) -ts_covariance(rsk62_beta_5_100_spe, pv87_interval_bidsize_median, 5) -ts_covariance(long_term_dividend_payout_ratio, company_offer_total_size, 60) -ts_covariance(fnd3_aacctadj_ebit, pv52_yse_national_order_size_code, 20) -ts_covariance(pv87_net_income_normalized_actual, pv103_tlrt_topofbook_tlrt_mean, 20) -ts_covariance(anl14_stddev_ebit_fp1, pv81_sasl, 60) -ts_covariance(mdl77_2mqf_niper, pv64_dif_fund_creation_unit_size, 20) -ts_covariance(est_ptpr, pv52_yse_chicago_order_size_code, 60) -ts_covariance(mdl26_v14_mstchg_pct_fy1_rnngs_14, pv98_tbds_mean, 20) -ts_covariance(gross_profit_current, pv81_stsl, 20) -ts_covariance(mdl264_usa_compustat_q1_dl8_miiq_l2, pv103_maus_mean, 250) -ts_covariance(fnd23_annfvmfm_rltr, pv103_lask_topofbook_lask_mean, 250) -ts_covariance(anl49_vector_depreciationdepletionamortization, pv103_tbsz_mean, 250) -ts_covariance(fnd31_qsg5additionalfactor3_backfilled_apg, pv103_sbps_mean, 250) -ts_covariance(est_12m_ner_median, pv103_1m_tlrt_mean, 5) -ts_covariance(pv87_ann_matrix_net_income_normalized_estimate_median, pv81_lasz, 5) -ts_covariance(anl14_high_epsrep_fy3, pv103_mbus_mean, 60) -ts_covariance(est_q_ner_median_3mth_ago, pv103_masz_mean, 250) -ts_covariance(pv87_web_weightedavg60_group_v2_0_1_css_earnings, mdl230_totalcap_cusip_ohlsonscore, 20) -ts_covariance(fnd65_us5000_cusip_min1yopmargin, pv81_zsbl, 60) -ts_covariance(anl15_ebt_ind_cal_fy3_6m_chg, fnd65_us5000_cusip_ohlsonscore, 250) -ts_covariance(mdl262_rasv2splitprofitabilityebit_ttm_profitability12, pv52_yse_arca_order_size_code, 250) -ts_covariance(anl10_gpssmun_1qf_2624, pv103_mbsz_mean, 5) -ts_covariance(mdl262_compustatpredictiveepspiq_prediction, pv103_tbds_mean, 20) -ts_covariance(pv87_2_epsr_af_matrix_all_chngratio_low, pv81_zsbl, 5) -ts_covariance(anl10_nerinnovate_increase_fy1_1995, mdl230_us5000_cusip_ohlsonscore, 5) -ts_covariance(fnd3_q_ebitda, pv87_interval_bidsizeinterval_volume_correlation, 250) -ts_covariance(oth460_berry_l2, pv87_interval_asksizeinterval_bidsize_correlation, 20) -ts_covariance(fnd65_us5000_cusip_aoer, pv103_mlrt_topofbook_mlrt_mean, 250) -ts_covariance(fnd72_s_pit_or_cr_q_com_eqy_to_tot_asset, pv103_tlrt_topofbook_tlrt_mean, 250) -ts_covariance(anl10_ebiinnovate_decrease_fq2_2606, pv81_zsah, 250) -ts_covariance(oth432_deltapredictebitd_a_mae, pv103_lasz_mean, 5) -ts_covariance(mdl264_epsfx_class, pv104_hlts_mean, 60) -ts_covariance(research_development_expense_reported_value, pv103_laks_mean, 250) -ts_covariance(mdl230_allcap_sedol_perg, fnd14_zs_lf_nlf_drp_ncr, 250) -ts_covariance(fnd65_totalcap_cusip_mpgghcy3_, pv81_zsbt, 5) -ts_covariance(mdl77_put_put_indepsp, pv104_tasz_mean, 5) -ts_covariance(mdl264_opepsq_l2, pv104_tasz_mean, 250) -ts_covariance(mdl77_2gdna_yoychgaa, mdl177_2_liquidityriskfactor_ohlsonscore, 250) -ts_covariance(mdl262_cheq_profitability_profitability2, pv52_yse_national_order_size_code, 5) -ts_covariance(mdl262_deltapredictebitd_q_predict, pv103_mbds_topofbook_mbds_mean, 60) -ts_covariance(est_q_eps_std_3mth_ago, pv87_interval_bidsize_skewness, 250) -ts_covariance(anl82_oprq_profitability_profitability10, pv103_mbds_topofbook_mbds_mean, 60) -ts_covariance(anl15_ebt_s_cal_fy3_mean, pv52_asdaq_order_size_code, 5) -ts_covariance(mdl116_epsyld_fy2_trendpredict_model_mada, fnd72_s_pit_or_cr_q_debt_to_mkt_cap, 20) -ts_covariance(pv87_2_netprofit_rep_qf_matrix_p1_low, pv104_masz_mean, 250) -ts_covariance(fnd28_value_18841a, pv104_lbsz_mean, 20) -ts_covariance(pv20_sal_indicator_p_feature1, pv87_interval_asksizeinterval_bidsize_rankcorrel, 5) -ts_covariance(anl14_high_ntp_fp2, pv87_interval_bidsizeinterval_volume_correlation, 20) -ts_covariance(mdl109_d_mtl_dlyspe, pv104_tbsz_mean, 5) -ts_covariance(anl14_stddev_ntp_fy1, pv103_sbsz_mean, 5) -ts_covariance(anl69_roe_expected_report_dt, pv52_yse_american_order_size_code, 60) -ts_covariance(mdl77_omomemtumanalystmodel_qma_mktresponse, pv64_dif_stal_fund_creation_unit_size, 250) -ts_covariance(oth460_usa_compustat_q1_dl8_epspxq_l4, pv104_hbsz_mean, 60) -ts_covariance(anl10_ebtfq2_pred_surps_v0_964, pv103_1m_mlrt_mean, 5) -ts_covariance(pv87_daily_ann_matrix_r1_net_income_normalized_consensus_mean_numnochangeunfiltered, oth567score_company_size, 250) -ts_covariance(quarterly_retained_earnings_total, pv87_interval_asksize_median, 5) -ts_covariance(pv20_ebt_indicator_q_feature5, pv64_trr_stal_cur_mkt_cap, 60) -ts_covariance(fnd72_pit_or_is_a_is_interest_inc, pv98_daily_mean_taks_taks_mean, 20) -ts_covariance(mdl77_earningsmomemtummodel_fc_fqsurstd, oth551_beta_size, 250) -ts_covariance(fnd65_totalcap_cusip_mad3yttmni, pv87_interval_asksize_skewness, 60) -ts_covariance(pv20_opr_indicator_o_feature7, pv104_insm_mean, 5) -ts_covariance(earning_time_type, pv104_lasz_mean, 5) -ts_covariance(mdl177_2_historicalgrowthfactor_chg3yepsp, oth567_deltascore_company_size_432, 250) -ts_covariance(mdl211_saly_deltaprofitability_profitability9, fnd31_ohlsonscore, 250) -ts_covariance(mdl262_eps_oeps12_mae, pv104_masz_mean, 5) -ts_covariance(mdl264_uopiq_class, fnd72_pit_or_cr_q_debt_to_mkt_cap, 250) -ts_covariance(annual_retained_earnings_total_fast_d1, pv103_maus_mean, 60) -ts_covariance(anl15_bps_ind_12_m_pe, oth551_resret_size, 60) -ts_covariance(oth401_game_earning_smooth, oth567score_company_size, 60) -ts_covariance(mdl230_us5000_cusip_altmanz, pv103_htsz_mean, 20) -ts_covariance(mdl211_netq_profitability_profitability7, fund_total_size, 250) -ts_covariance(anl15_ebtgics_ind_18_m_cos_dn, pv103_maus_mean, 60) -ts_covariance(mdl264_usa_compustat_q1_dl8_epspiq_l1, mdl230_us5000_cusip_ohlsonscore, 5) -ts_covariance(mdl262_trkdpitpredictiveebitda_prediction, oth551_r2_size, 250) -ts_covariance(pv20_ebt_indicator_7_feature2, pv104_masz_mean, 60) -ts_covariance(pv87_2_operatingprofit_af_matrix_p1_dts, pv98_tbds_mean, 60) -ts_covariance(fnd28_ishta_value_01751a, pv64_dif_fund_creation_unit_size, 20) -ts_covariance(oth460_ciac_class, mdl77_2liquidityriskfactor_ohlsonscore, 20) -ts_covariance(mdl26_v14_mstchg_pct_f12m_rnngs_90, pv103_maks_topofbook_maks_mean, 20) -ts_covariance(anl44_netprofit_gaap_best_cur_fiscal_qtr_period, company_offer_total_size, 60) -ts_covariance(fnd72_pit_or_cr_q_announcement_dt, pv87_interval_asksize_numintervalsincehigh, 250) -ts_covariance(earnings_announcement_time_of_day_fast_d1, pv52_asdaq_bx_order_size_code, 20) -ts_covariance(fnd28_bdea_value_18854a, pv81_sbsl, 60) -ts_covariance(anl82_ebtq_deltaprofitability_profitability5, oth567score_company_size_358, 20) -ts_covariance(pv87_pos_earnings_matrix_bee_sum, mdl77_2liquidityriskfactor_ohlsonscore, 20) -ts_covariance(pv20_ebt_indicator_6_feature6, pv103_lbsz_mean, 5) -ts_covariance(anl15_ebtgics_ind_cal_fy1_6m_chg, company_offer_total_size, 250) -ts_covariance(mdl262_ninc_trkdpitdeltapredict_funda_mae, pv87_interval_asksizeinterval_bidsize_correlation, 5) -ts_covariance(fnd28_wsfsq_value_01451q, company_offer_size_value, 5) -ts_covariance(oth432_deltapredictni_a_predict, mdl177_2_liquidityriskfactor_ohlsonscore, 20) -ts_covariance(mdl354_group_pt12yf_dlyspe, pv98_daily_mean_taks_taks_mean, 20) -ts_covariance(anl15_cpsgics_s_18_m_pe, pv64_trr_cur_mkt_cap, 5) -ts_covariance(mdl262_predictiveebt_a_predict, mdl177_liquidityriskfactor_ohlsonscore_alt, 5) -ts_covariance(mdl262_deltapredictebit_q_predict, pv81_zsat, 5) -ts_covariance(anl10_ebtrevise_ratio_to_consensus_fq2_1011, pv81_zsbl, 5) -ts_covariance(fnd72_pit_or_cr_q_prof_margin, mdl230_totalcap_cusip_ohlsonscore, 250) -ts_covariance(mdl230_totalcap_cusip_rev6fy2, pv81_zsbl, 5) -ts_covariance(mdl211_ebtq_deltaprofitability_profitability12, pv103_hasz_mean, 5) -ts_covariance(mdl26_rvsnclstr_vg_rvsn_pr_fq1_rnngs, mdl77_liquidityriskfactor_ohlsonscore, 5) -ts_covariance(anl15_ebtgics_ind_cal_fy3_total, fund_total_size, 250) -ts_covariance(anl14_stddev_ntp_fp1, pv87_interval_asksize_std, 20) -ts_covariance(fnd72_pit_or_cr_a_com_eqy_to_tot_asset, pv64_out_stal_fund_creation_unit_size, 5) -ts_covariance(pv87_ann_matrix_net_income_normalized_estimate_low, pv87_interval_bidsizeinterval_slippage_rankcorrel, 5) -ts_covariance(oth460_3l_qacf, pv104_tbsz_mean, 20) -ts_covariance(anl10_epsinnovate_decrease_fq2, pv52_yse_national_order_size_code, 20) -ts_covariance(mdl264_usa_compustat_q1_dl8_nopiq_l4, pv64_trr_stal_cur_mkt_cap, 250) -ts_covariance(anl10_nersmun_1yf_2417, pv87_interval_bidsize_std, 60) -ts_covariance(anl14_high_ebitda_fp5, pv104_lasz_mean, 250) -ts_covariance(fnd7_ointfund_qipspe, pv52_yse_arca_order_size_code, 20) -ts_covariance(anl82_oprq_profitability_profitability1, pv52_yse_national_order_size_code, 20) -ts_covariance(count_neutral_profitability_summary_fast_d1, pv87_interval_asksizeinterval_slippage_rankcorrel, 20) -ts_covariance(est_12m_pre_mean_3mth_ago, pv64_dif_stal_fund_creation_unit_size, 5) -ts_covariance(mdl26_v14_nnlyst_rvsng_dwn_fy1_rnngs_14, pv104_tasz_mean, 60) -ts_covariance(anl10_ebsfy2_pred_surps_v0, pv87_interval_asksizeinterval_bidsize_rankcorrel, 60) -ts_covariance(oth432_rasv2splitprofitabilityebt_ttm_profitability5, mdl230_totalcap_cusip_ohlsonscore, 20) -ts_covariance(fnd7_ointfund_qiim, pv103_sbps_mean, 250) -ts_covariance(mdl262_acae_q_profitability_profitability8, pv103_tbsz_mean, 60) -ts_covariance(fnd89_csjones_industry_major_14, pv87_interval_bidsize_skewness, 20) -ts_covariance(mdl230_allcap_sedol_aor, pv81_trlt, 250) -ts_covariance(mdl262_compustat_models_profitability7, pv103_mlrt_topofbook_mlrt_mean, 250) -ts_covariance(anl10_nerrevise_ratio_to_consensus_fq2_2016, pv103_sbsz_mean, 20) -ts_covariance(fnd65_us5000_cusip_divcov, pv87_interval_bidsizeinterval_slippage_rankcorrel, 5) -ts_covariance(pv87_weightedavg60_group_ess_earnings, pv81_stlh, 20) -ts_covariance(pretax_income_total, pv104_tasz_mean, 250) -ts_covariance(mdl264_onet_l1, oth137_avg_txn_val, 20) -ts_covariance(fnd2_itxreexftfedstyitxrt, mdl230_allcap_sedol_ohlsonscore, 60) -ts_covariance(anl10_ebsnormal_decrease_fy2, pv64_dif_fund_creation_unit_size, 60) -ts_covariance(oth432_rasv2splitprofitabilityni_ttm_profitability1, pv81_stlh, 250) -ts_covariance(anl14_numofests_ntp_fy2, pv87_interval_bidsize_numintervalsincelow, 250) -ts_covariance(mdl211_saly_deltaprofitability_profitability9, pv87_interval_bidsizeinterval_slippage_rankcorrel, 5) -ts_covariance(fnd23_intfvm_ipos, nws31_bodysize, 5) -ts_covariance(mdl211_salq_deltaprofitability_profitability7, pv87_interval_asksize_std, 60) -ts_covariance(fnd89_major_11_31, pv81_sasl, 5) -ts_covariance(pv20_indicator_0_feature0_121, pv103_lbsz_mean, 5) -ts_covariance(pv20_eps_indicator_2_feature0, nws31_bodysize, 5) -ts_covariance(pv87_net_income_normalized_of_estimates_scale, pv87_interval_asksize_mean, 5) -ts_covariance(pv20_ebt_indicator_q_feature11, oth567_deltascore_company_size_432, 60) -ts_covariance(mdl211_ebty_deltaprofitability_profitability5, pv103_1m_mlrt_mean, 20) -ts_covariance(anl10_nerrevise_value_fq1_2012, pv87_interval_asksizeinterval_slippage_correlation, 60) -ts_covariance(act_12m_opr_value, pv98_quote_size_mean, 5) -ts_covariance(pv20_gps_indicator_7_feature11, pv87_interval_asksizeinterval_bidsize_correlation, 250) -ts_covariance(fnd6_cptmfmq_opepsq, fnd72_s_pit_or_cr_q_debt_to_mkt_cap, 60) -ts_covariance(anl15_ebtgics_gr_cal_fy1_3m_chg, pv104_masz_mean, 250) -ts_covariance(fnd7_ointhstfundfn_hfqspepo, fnd65_totalcap_cusip_ohlsonscore, 60) -ts_covariance(mdl262_oiadpq_profitability_profitability3, pv103_1m_tlrt_mean, 5) -ts_covariance(pv20_opr_indicator_n_feature2, pv52_yse_order_size_code, 250) -ts_covariance(pv20_sal_indicator_2_feature12, mdl77_2liquidityriskfactor_ohlsonscore, 60) -ts_covariance(quarterly_earnings_before_tax_total, pv103_htsz_mean, 20) -ts_covariance(pv20_pre_indicator_n_feature10, pv104_tlrt_mean, 250) -ts_covariance(fnd72_s_pit_or_is_q_earn_for_common, pv87_interval_asksizeinterval_volume_correlation, 20) -ts_covariance(anl82_salq_profitability_profitability1, pv103_maus_mean, 5) -ts_covariance(fnd65_totalcap_cusip_mpoghc, pv104_tasz_mean, 5) -ts_covariance(mdl177_historicalgrowthfactor_pctchg3yeps, pv81_trlt, 20) -ts_covariance(mdl26_bld_stmt_flg_fq2_rnngs, fnd65_totalcap_cusip_ohlsonscore, 60) -ts_covariance(anl4_ptpr_number, pv52_asdaq_psx_order_size_code, 250) -ts_covariance(earning_fiscal_year_end, pv64_out_stal_fund_creation_unit_size, 5) -ts_covariance(fnd28_newa2_value_09216a, nws31_d1_bodysize, 60) -ts_covariance(est_q_eps_std_4wks_ago, pv87_interval_bidsize_skewness, 5) -ts_covariance(anl14_high_eps_fp5, pv103_laks_mean, 60) -ts_covariance(mdl264_viat_l3, mdl230_allcap_sedol_ohlsonscore, 250) -ts_covariance(pv20_opr_indicator_1_feature1, pv103_sbsz_mean, 60) -ts_covariance(mdl262_che_profitability_profitability2, fnd72_s_pit_or_cr_q_debt_to_mkt_cap, 5) -ts_covariance(anl69_dps_expected_report_time, pv64_out_stal_fund_creation_unit_size, 5) -ts_covariance(mdl26_p_yld_pct_stm_fy1, pv104_hbsz_mean, 60) -ts_covariance(anl82_opry_deltaprofitability_profitability3, pv104_mbsz_mean, 5) -ts_covariance(est_12m_gps_std_28d, pv104_lbsz_mean, 20) -ts_covariance(mdl77_2liquidityriskfactor_impduration, pv103_tlrt_topofbook_tlrt_mean, 60) -ts_covariance(mdl264_epspi_class, fnd72_s_pit_or_cr_q_debt_to_mkt_cap, 5) -ts_covariance(oth460_usa_compustat_q1_dl8_nopiq_l4, pv103_mlrt_topofbook_mlrt_mean, 250) -ts_covariance(earning_time_type_fast_d1, pv103_hbsz_mean, 20) -ts_covariance(anl82_opry_y1_madp, pv64_trr_cur_mkt_cap, 60) -ts_covariance(pv20_net_indicator_q_feature0, company_offer_total_size, 60) -ts_covariance(fnd65_totalcap_cusip_rev3my1std, mdl248_avg_txn_val, 5) -ts_covariance(fnd23_icsm_m_caic, pv103_tlrt_topofbook_tlrt_mean, 250) -ts_covariance(pe_ratio_relative_component_rank, company_size_sentiment_score, 250) -ts_covariance(mdl26_lw_stmt_chng_fy1_rnngs_30, pv87_interval_bidsize_mean, 5) -ts_covariance(pv20_pre_indicator_1_feature11, pv103_tbsz_mean, 60) -ts_covariance(mdl262_txtq_compustatdeltapredict_funda_madp, fnd65_allcap_sedol_ohlsonscore, 250) -ts_covariance(anl69_sales_best_eeps_nxt_yr, pv103_1m_maks_mean, 60) -ts_covariance(anl44_netprofit_gaap_best_eeps_nxt_yr, pv104_lsas_mean, 60) -ts_covariance(anl4_epsr_high, nws31_d1_bodysize, 20) -ts_covariance(oth432_saleq_profitability_profitability9, pv98_tbds_mean, 20) +ts_mean(mdl116_epsyld_gro_trendpredict_model_mae, 20) +ts_mean(mdl262_compustat_models_profitability12, 60) +ts_mean(anl15_ebt_s_cal_fy2_cos, 20) +ts_mean(mdl262_ebit_profitability_profitability1, 250) +ts_mean(anl10_ebifq2_smart_ests_v1_2225, 5) +ts_mean(fnd65_us5000_cusip_fc_curindfwdep_, 250) +ts_mean(mdl26_v14_sma_prsprise_fq1_earnings, 60) +ts_mean(anl49_backfill_netprofitindicator, 5) +ts_mean(anl82_opry_deltaprofitability_profitability12, 20) +ts_mean(profitability_sentiment_polarity_fast_d1, 20) +ts_mean(pv87_v2_expavg20_group_css_earnings, 250) +ts_mean(fnd28_growthratesa_value_08605a, 5) +ts_mean(fnd23_intfvm_tnos, 250) +ts_mean(pv87_daily_qtr_matrix_net_income_gaap_consensus_mean_numnochange, 5) +ts_mean(fnd28_newa2_value_08636a, 20) +ts_mean(anl15_ebt_gr_cal_fy0_val, 250) +ts_mean(fnd3_aacctadj__earbeforetaxes_fast_d1, 20) +ts_mean(oth432_niq_compustatdeltapredict_funda_predict, 20) +ts_mean(anl15_ebt_gr_cal_fy3_1m_chg, 20) +ts_mean(anl15_dpsgics_s_18_m_pe, 250) +ts_mean(anl10_ebiinnovation_score_fq2_2599, 5) +ts_mean(rp_ess_earnings, 20) +ts_mean(mdl264_epspxq_class, 250) +ts_mean(mdl77_putput_qualmod, 60) +ts_mean(fnd72_pit_or_is_a_is_impairment_goodwill_intangibl, 250) +ts_mean(anl15_ebt_s_cal_fy2_3m_chg, 5) +ts_mean(pv87_daily_qtr_matrix_r6_net_income_gaap_consensus_mean_numdown, 250) +ts_mean(fnd72_pit_or_cr_q_trail_12m_ebita, 20) +ts_mean(anl10_gpspast_det_indicator_1320, 5) +ts_mean(oth432_sbda_a_profitability_profitability6, 250) +ts_mean(pv20_ebt_indicator_6_feature4, 60) +ts_mean(oth432_rasv2splitprofitabilityrev_q_profitability5, 250) +ts_mean(anl15_ebtgics_gr_fy2_cos_dn, 60) +ts_mean(fnd28_annualperf_value_09121a, 5) +ts_mean(mdl26_prsprs_fq4_rnngs_rt, 250) +ts_mean(mdl264_1l_21declg, 5) +ts_mean(mdl264_1l_qtxtrn, 60) +ts_mean(anl14_numofests_ebitda_fp1, 250) +ts_mean(mws50_ber_2, 60) +ts_mean(mdl177_historicalgrowthfactor_chg3yepsp, 250) +ts_mean(pv20_gps_indicator_4_feature4, 20) +ts_mean(oth395_major_12_22, 60) +ts_mean(oth432_trkdpitpredictiveebitda_mad_pred, 20) +ts_mean(oth432_earnings_cap1, 20) +ts_mean(oth395_csjones_all_r2_major_14, 20) +ts_mean(mdl230_allcap_sedol_slope4qeps5y, 20) +ts_mean(anl15_ebtgics_gr_cal_fy2_mean, 250) +ts_mean(fnd65_totalcap_cusip_mpgghcm3_, 20) +ts_mean(anl15_ebt_ind_12_m_st_dev, 250) +ts_mean(mdl230_us5000_cusip_mktcappera, 60) +ts_mean(fnd6_nopio, 250) +ts_mean(anl69_eps_gaap_best_eeps_nxt_yr, 5) +ts_mean(mdl230_us5000_cusip_fc_curep, 60) +ts_mean(pv20_sal_indicator_p_feature7, 5) +ts_mean(anl10_netnormal_increase_fq2_2519, 20) +ts_mean(pv20_sal_indicator_9_feature7, 5) +ts_mean(anl82_oprq_deltaprofitability_profitability11, 5) +ts_mean(anl69_eps_best_eps, 20) +ts_mean(anl10_ebirevise_value_fq1_2601, 250) +ts_mean(fnd65_allcap_sedol_yoychgaa, 250) +ts_mean(oth460_reunaq_l1, 60) +ts_mean(anl10_gpspast_det_estage_1323, 20) +ts_mean(fnd65_allcap_sedol_fc_fcfroey1p, 250) +ts_mean(fnd65_allcap_sedol_rev3my2std, 5) +ts_mean(pv20_gps_indicator_2_feature4, 20) +ts_mean(fnd65_us5000_cusip_pctchg3yeps, 20) +ts_mean(anl82_delta_oprq_q3_predict, 250) +ts_mean(mdl230_allcap_sedol_avg10yep, 250) +ts_mean(fnd6_nxints, 60) +ts_mean(mdl262_saleq_profitability_profitability4, 5) +ts_mean(anl44_pretaxprofit_rep_value, 60) +ts_mean(fnd23_annfv1a_aieo, 5) +ts_mean(mdl219_3_tmtscni_avq, 20) +ts_mean(fnd28_annualprof_value_08320a, 60) +ts_mean(fnd72_q1_geo_grow_oper_inc, 5) +ts_mean(earning_fiscal_year_fast_d1, 60) +ts_mean(fnd65_totalcap_cusip_bmpo, 60) +ts_mean(pv87_qtr_matrix_net_income_normalized_consensus_mean_numnochangeunfiltered, 250) +ts_mean(pv87_neg_earnings_matrix_css_max, 20) +ts_mean(mdl264_glcep_class, 250) +ts_mean(oth432_piq_compustatdeltapredict_funda_mada, 5) +ts_mean(anl10_netfy2_smart_ests_v1_1044, 20) +ts_mean(mdl262_rasv2splitprofitabilityebitd_ttm_profitability2, 60) +ts_mean(mdl264_siio_class, 5) +ts_mean(est_12m_net_num, 60) +ts_mean(earnings_projection_metric_type_v2, 5) +ts_mean(anl14_actvalue_epsrep_fy0, 250) +ts_mean(fn_def_income_tax_expense_a, 20) +ts_mean(pv20_opr_indicator_n_feature3, 5) +ts_mean(fnd65_allcap_sedol_gtl, 250) +ts_mean(mdl177_valueanalystmodel_qva_incstmt_alt, 250) +ts_mean(mdl77_2deepvaluefactor_ebitdap, 5) +ts_mean(anl14_actvalue_eps_fy0, 60) +ts_mean(mdl264_glceeps_l1, 20) +ts_mean(fnd65_us5000_cusip_min1yopmargin, 250) +ts_mean(fnd65_allcap_sedol_apemtt, 250) +ts_mean(anl10_nerinnovate_increase_fy2_2011, 250) +ts_mean(mdl77_historicalgrowthfactor_cvopinc, 250) +ts_mean(pv20_pre_indicator_2_feature11, 20) +ts_mean(fnd6_cptnewqv1300_req, 20) +ts_mean(anl15_ebtgics_s_12_m_cos_dn, 250) +ts_mean(pv87_web_weightedavg60_group_v2_0_1_css_earnings, 250) +ts_mean(pv20_sal_indicator_2_feature8, 60) +ts_mean(anl10_ebifq2_consensus_2242, 60) +ts_mean(fnd6_newqeventv110_glceeps12, 5) +ts_mean(mdl77_2earningsmomemtummodel_emm_composite, 20) +ts_mean(fnd31_avg10yepinf, 5) +ts_mean(star_v14_val_earnings_projection_fy6, 250) +ts_mean(anl15_cpsgics_s_cal_fy2_pe, 5) +ts_mean(fnd89_major_11_22, 60) +ts_mean(mdl230_us5000_cusip_fc_numest, 20) +ts_mean(mdl230_allcap_sedol_gspea2y_cf, 20) +ts_mean(pv20_gps_indicator_1_feature1, 5) +ts_mean(oth432_sbda_a_profitability_profitability6, 5) +ts_mean(pv87_v2_simpleavg60_group_event_sentiment_score_earnings, 5) +ts_mean(anl15_salgics_s_cal_fy2_pe, 5) +ts_mean(mdl262_eibt_a_profitability_profitability6, 250) +ts_mean(mdl26_dsnc_prd_srprs_flg_chg_fq2_rnngs, 20) +ts_mean(mdl230_us5000_cusip_fc_estep, 250) +ts_mean(oth395_major_14_11, 5) +ts_mean(anl15_ebtgics_ind_12_m_gro, 20) +ts_mean(anl44_netprofit_gaap_best_eeps_cur_yr, 5) +ts_mean(anl44_2_netprofit_coveredby, 60) +ts_mean(mdl262_niq_profitability_profitability8, 5) +ts_mean(pv20_gps_indicator_o_feature10, 5) +ts_mean(mdl31_cf_chg_1_fy, 250) +ts_mean(oth432_che_profitability_profitability6, 5) +ts_mean(fnd2_dfdfeditxexp, 5) +ts_mean(oth432_compustatpredictivetxtq_prediction, 60) +ts_mean(pv20_net_indicator_7_feature6, 60) +ts_mean(fnd65_allcap_sedol_roe, 250) +ts_mean(mdl77_deepvaluefactor_pqipmtt, 60) +ts_mean(rsk62_risk_spe, 20) +ts_mean(anl14_numofests_ntprep_fp1, 5) +ts_mean(mdl211_netq_deltaprofitability_profitability3, 20) +ts_mean(fnd72_pit_or_is_q_cni_tni_si, 250) +ts_mean(mdl77_liquidityriskfactor_mktcappera, 5) +ts_mean(anl10_ebtpast_det_estvalue_825, 250) +ts_mean(oth395_jones_accrual_pct_major_14, 60) +ts_mean(anl10_ebtfq1_pred_surps_v0_928, 60) +ts_mean(pv20_sal_indicator_9_feature10, 60) +ts_mean(mdl177_historicalgrowthfactor_pctchg3yeps_alt, 20) +ts_mean(fnd65_totalcap_cusip_pe_wt, 250) +ts_mean(pv87_daily_qtr_matrix_net_income_normalized_consensus_mean_numanalystsunfiltered, 20) +ts_mean(anl82_oprq_deltaprofitability_profitability3, 250) +ts_mean(est_12m_prr_std_3mth_ago, 250) +ts_mean(mdl262_sbda_a_profitability_profitability8, 5) +ts_mean(fnd31_rev3my1std, 250) +ts_mean(pv87_prv2_expavg20_group_event_sentiment_score_earnings, 250) +ts_mean(est_12m_opr_high, 5) +ts_mean(fnd65_allcap_sedol_fc_rel5yfwdep, 60) +ts_mean(retained_earnings_value, 250) +ts_mean(anl15_ebt_s_18_m_total, 20) +ts_mean(anl69_eps_gaap_lo_best_eeps_cur_yr, 60) +ts_mean(anl4_gric_high, 20) +ts_mean(fnd90_us_qes_gamef_grossprofit_asset, 250) +ts_mean(mdl262_saleq_profitability_profitability5, 5) +ts_mean(fnd28_newa2_value_09126a, 20) +ts_mean(oth395_major_14_31, 250) +ts_mean(pv87_gross_margin_chg_mean, 5) +ts_mean(anl14_high_ntprep_fp3, 250) +ts_mean(oth432_rasv2splitprofitabilityebitd_ttm_profitability7, 5) +ts_mean(pv87_2_operatingprofit_af_matrix_all_b_number, 250) +ts_mean(fnd23_icsm_mvb_ttiv, 20) +ts_mean(mdl219_2_rel5ycoreepsp, 60) +ts_mean(mdl264_2l_qic, 20) +ts_mean(fnd65_us5000_cusip_sue, 60) +ts_mean(est_12m_opr_num, 20) +ts_mean(mdl26_grwth_ths_yr_sctr_prcntl_rnngs, 5) +ts_mean(anl14_numofests_eps_fp1, 5) +ts_mean(anl14_median_ebitda_fp3, 5) +ts_mean(mdl177_fa_fc_fwdep, 250) +ts_mean(mdl264_2l_qic, 250) +ts_mean(mdl177_2_valueanalystmodel_qva_earnval, 20) +ts_mean(anl10_ebifq2_pred_surps_v2_2239, 60) +ts_mean(fnd23_annfv1a_ttiv, 60) +ts_mean(mdl77_valueanalystmodel_qva_composite, 5) +ts_mean(mdl262_sbda_a_profitability_profitability5, 250) +ts_mean(est_12m_ner_mean, 250) +ts_mean(fnd72_pit_or_cr_a_eps_growth, 250) +ts_mean(scaling_factor_quarter4_earnings, 250) +ts_mean(fnd7_ointhstfund_hqer, 20) +ts_mean(mdl177_fangma_emf_usa_fangma_emf15, 60) +ts_mean(oth553_ptg_question_ratio, 20) +ts_mean(fnd23_blsmvb_atda, 250) +ts_mean(mdl264_3l_mpns, 60) +ts_mean(mdl264_cmea_class, 20) +ts_mean(pv20_gps_indicator_6_feature9, 60) +ts_mean(annual_minority_interest_net_income_fast_d1, 5) +ts_mean(anl82_delta_preq_q4_predict, 60) +ts_mean(anl14_mean_ebit_fp1, 250) +ts_mean(fnd6_cptnewqeventv110_epsx12, 5) +ts_mean(anl15_salgics_s_18_m_pe, 20) +ts_mean(fnd65_allcap_sedol_fc_ebop, 60) +ts_mean(pv87_ebt_excl_excep_standardized_unanticipated_earnings, 20) +ts_mean(mdl211_netq_profitability_profitability11, 60) +ts_mean(mdl262_oibdpq_profitability_profitability9, 60) +ts_mean(nws20_d1_acb, 60) +ts_mean(mdl77_ohistoricalgrowthfactor_slope4qeps3y, 20) +ts_mean(anl4_afv4_eps_number, 60) +ts_mean(pv87_2_netprofit_af_matrix_all_dts, 20) +ts_mean(fnd23_annfv1a_atda, 250) +ts_mean(anl14_numofests_epsrep_fp5, 20) +ts_mean(oth432_trkdpit_models_profitability5, 20) +ts_mean(fnd28_wcnewa1_value_01262a, 20) +ts_mean(anl82_salq_deltaprofitability_profitability3, 250) +ts_mean(pv87_pos_analyst_matrix_bee_count, 20) +ts_mean(act_q_opr_surprisenum, 60) +ts_mean(est_q_pre_median_3mth_ago, 20) +ts_mean(pv20_net_indicator_2_feature11, 250) +ts_mean(anl15_ebt_gr_cal_fy2_pe, 250) +ts_mean(pv20_net_indicator_q_feature0, 60) +ts_mean(mdl177_2_earningmomentumfactor400_sue, 60) +ts_mean(fnd65_totalcap_cusip_spe2yfvc_cf, 5) +ts_mean(oth432_rasv2splitprofitabilityebt_q_profitability10, 250) +ts_mean(est_12m_net_raisednum_4wks, 20) +ts_mean(mdl77_ovalueanalystmodel_qva_earnval, 60) +ts_mean(mdl26_stdv_stm_nlysts_fq3_rnngs, 20) +ts_mean(mdl264_spceeps_l3, 5) +ts_mean(anl4_flag_erbfintax, 250) +ts_mean(mdl230_allcap_sedol_rp, 60) +ts_mean(mdl77_2mqf_niper, 20) +ts_mean(pv20_opr_indicator_q_feature8, 20) +ts_mean(pv87_2_pretaxprofit_af_matrix_p1_chngratio_high, 60) +ts_mean(fnd65_us5000_cusip_gspea1y_cf, 5) +ts_mean(mdl219_2_qva_finstmt, 5) +ts_mean(pv20_gps_indicator_n_feature2, 5) +ts_mean(mdl77_ooearningsmomemtummodel_emm_composite, 250) +ts_mean(pv87_2_netprofit_rep_qf_matrix_p1_low, 60) +ts_mean(pv20_pre_indicator_7_feature1, 20) +ts_mean(fnd65_allcap_sedol_ttmopincev, 20) +ts_mean(mdl77_2put_put_indroe, 5) +ts_mean(oth460_suie_l1, 60) +ts_mean(oth432_rtlr_q_profitability_profitability3, 60) +ts_mean(pv20_sal_indicator_o_feature5, 5) +ts_mean(pv87_2_pretaxprofit_qf_matrix_all_mean, 60) +ts_mean(anl15_cpsgics_gr_cal_fy1_pe, 20) +ts_mean(est_12m_gps_raised_1wk, 5) +ts_mean(star_v14_val_earnings_projection_fy10, 20) +ts_mean(oth432_deltapredictebt_a_predict, 60) +ts_mean(anl14_low_opp_fp1, 5) +ts_mean(anl69_rec_expected_report_time, 5) +ts_mean(oth432_compustat_models_profitability9, 250) +ts_mean(fnd28_annualprof_value_08381a, 20) +ts_mean(oth432_che_profitability_profitability3, 20) +ts_mean(pv20_opr_indicator_8_feature10, 60) +ts_mean(fnd65_allcap_sedol_niper, 5) +ts_mean(mdl26_sm_stm_fq2_rnngs, 60) +ts_mean(mdl177_2_globaldevnorthamerica_v502_fc_fcfp, 60) +ts_mean(mdl262_piq_profitability_profitability11, 60) +ts_mean(est_q_gps_mean_4wks_ago, 20) +ts_mean(mdl230_totalcap_cusip_apemtt, 5) +ts_mean(anl69_eps_4wk_chg_best_eeps_cur_yr, 20) +ts_mean(oth460_eibt_class, 5) +ts_mean(mdl230_allcap_sedol_ttmopincev, 5) +ts_mean(pv20_ebt_indicator_7_feature10, 250) +ts_mean(mdl230_totalcap_cusip_yoychgroa, 5) +ts_mean(mdl26_prsprise_flag_fy1_earnings, 60) +ts_mean(fnd14_rtq_td_rtq_cni_tn_cni_twh, 250) +ts_mean(oth432_rasv2splitprofitabilityfcf_q_profitability2, 60) +ts_mean(pv20_pre_indicator_1_feature12, 250) +ts_mean(pv20_gps_indicator_4_feature10, 250) +ts_mean(fnd72_s_pit_or_bs_q_2_announcement_dt, 60) +ts_mean(mdl262_rasv2splitprofitabilityni_ttm_profitability6, 250) +ts_mean(mdl230_totalcap_cusip_min3ygrossmargin, 250) +ts_mean(mdl177_2_earningsqualityfactor_yoychgaa, 250) +ts_mean(mdl211_opry_y1_madp, 60) +ts_mean(fnd72_q2_oper_inc_to_int_exp, 20) +ts_mean(fnd72_s_pit_or_bs_q_bs_retain_earn, 20) +ts_mean(quarterly_ebitda_total, 60) +ts_mean(anl44_netprofit_gaap_best_crncy_iso, 60) +ts_mean(oth432_rasv2splitprofitabilityni_q_profitability5, 5) +ts_mean(mdl177_2_valueanalystmodel_qva_earnquality, 20) +ts_mean(mdl77_momemtumanalystmodel_qma_composite, 20) +ts_mean(anl14_median_eps_fp2, 20) +ts_mean(anl14_numofests_eps_fp2, 20) +ts_mean(fnd65_us5000_cusip_slope4qeps3y, 60) +ts_mean(pv87_daily_qtr_matrix_r6_net_income_normalized_consensus_mean_numnochangeunfiltered, 5) +ts_mean(pv20_ebt_indicator_q_feature0, 5) +ts_mean(oth432_cheq_profitability_profitability12, 250) +ts_mean(fnd65_us5000_cusip_pfcmtt, 60) +ts_mean(anl49_valugaugeestimatingdifficultyrank, 20) +ts_mean(fnd72_s_pit_or_cr_q_net_debt_to_ebitda_after_capex, 20) +ts_mean(mdl177_liquidityriskfactor_altmanz_alt, 5) +ts_mean(anl69_pe_expected_report_time, 20) +ts_mean(mdl230_totalcap_cusip_apgghcm3_, 250) +ts_mean(mdl219_1_yoychggpm, 250) +ts_mean(mdl138_pg_4idp, 5) +ts_mean(mdl264_2l_qipspe, 20) +ts_mean(mdl26_srprs_pct_lst_y_rnngs, 60) +ts_mean(mdl262_epsfi_compustatdeltapredict_annual_funda_madp, 250) +ts_mean(anl15_salgics_s_12_m_pe, 250) +ts_mean(fnd28_newa2_value_08911a, 250) +ts_mean(pv87_v2_weightedavg20_group_css_earnings, 20) +ts_mean(pv20_eps_indicator_6_feature0, 20) +ts_mean(anl10_netfq2_smart_ests_v0_1072, 5) +ts_mean(fnd23_intfvm_tbie, 5) +ts_mean(mdl77_2400_cpgspea2y, 250) +ts_mean(oth432_ninc_predict, 20) +ts_mean(pv20_gps_indicator_8_feature12, 250) +ts_mean(anl69_eps_gaap_hi_best_eeps_nxt_yr, 60) +ts_mean(mdl77_2put_put_indroe, 250) +ts_mean(mdl211_salq_profitability_profitability3, 60) +ts_mean(anl4_qf_az_wol_spe, 20) +ts_mean(oth432_compustatpredictivetxtq_mad_act, 20) +ts_mean(fnd31_qsg2_additionalfactor_twepsrev, 20) +ts_mean(mdl211_netq_deltaprofitability_profitability11, 20) +ts_mean(mdl219_2_qva_shortfall, 20) +ts_mean(anl14_low_ntprep_fy3, 5) +ts_mean(oth432_rasv2splitprofitabilityni_ttm_profitability4, 60) +ts_mean(anl10_netfq2_pred_surps_v0_1065, 5) +ts_mean(pv87_2_netprofit_rep_qf_matrix_all_chngratio_std, 60) +ts_mean(oth460_fcaq_class, 250) +ts_mean(oth460_usa_compustat_q1_dl8_txtq_l2, 20) +ts_mean(anl14_low_ntprep_fp5, 60) +ts_mean(mdl77_deepvaluefactor_proformaep, 5) +ts_mean(anl10_ebiinnovate_decrease_fq1_2580, 5) +ts_mean(mdl77_2valuemomemtummodel_earningsexpectationmodule, 60) +ts_mean(fnd65_totalcap_cusip_ccacw, 20) +ts_mean(quarterly_gross_profit_total_fast_d1, 5) +ts_mean(mdl264_citotalq_l1, 60) +ts_mean(mdl26_rvsnclstr_vg_rvsn_fq3_rnngs, 250) +ts_mean(oth432_acae_q_profitability_profitability3, 250) +ts_mean(anl10_netrevise_ratio_to_close_fq1_2529, 20) +ts_mean(mdl26_grwth_nxt_yr_ndstry_prcntl_rnngs, 5) +ts_mean(mdl264_3l_ipon, 60) +ts_mean(earnings_date_fiscal_quarter, 250) +ts_mean(mdl116_epsyld_gro_trendpredict_model_madp, 250) +ts_mean(anl15_dpsgics_s_cal_fy0_pe, 20) +ts_mean(mdl264_3l_niis, 20) +ts_mean(anl16_beforeest_value, 250) +ts_mean(anl4_ebitda_number, 60) +ts_mean(fnd3_Q_nic, 20) +ts_mean(pv20_ebt_indicator_4_feature3, 5) +ts_mean(mdl77_momemtumanalystmodel_qma_mktresponse, 5) +ts_mean(fnd65_allcap_sedol_mpgghcy3_, 250) +ts_mean(pv87_daily_ann_matrix_r1_net_income_normalized_consensus_mean_numanalysts, 60) +ts_mean(annual_income_tax_expense, 5) +ts_mean(fnd7_ointfund_21xspe, 250) +ts_mean(fnd31_rev3my1std, 5) +ts_mean(fnd23_intfvm_xbin, 250) +ts_mean(mdl26_60dy_srprs_lst_y_rnngs, 60) +ts_mean(mdl230_totalcap_cusip_mpoghc, 20) +ts_mean(anl69_best_ptp_stddev, 250) +ts_mean(fnd72_pit_or_is_a_announcement_dt, 5) +ts_mean(fnd23_blsmvb_tdbl, 60) +ts_mean(fnd23_intfvm_iads, 250) +ts_mean(anl10_gpsfq2_smart_ests_v2_2622, 20) +ts_mean(fnd3_Q_interestincome, 250) +ts_mean(fnd65_us5000_cusip_ebitdap, 5) +ts_mean(anl15_cps_ind_cal_fy1_pe, 60) +ts_mean(fnd31_devnorthamericaadditionalfactor4_pu, 20) +ts_mean(mdl264_1l_mpns, 5) +ts_mean(pv20_ebt_indicator_p_feature2, 250) +ts_mean(pv20_pre_indicator_8_feature3, 20) +ts_mean(pv20_eps_indicator_9_feature11, 20) +ts_mean(pv20_eps_indicator_n_feature2, 250) +ts_mean(pv20_pre_indicator_7_feature11, 250) +ts_mean(mdl77_historicalgrowthfactor_pctchg3yeps, 250) +ts_mean(oth432_trkdpit_models_profitability12, 20) +ts_mean(anl44_2_pretaxprofit_prevalue, 250) +ts_mean(mdl77_2gdna_pfcmtt, 250) +ts_mean(mdl264_usa_compustat_q1_dl8_epsfxq_l1, 5) +ts_mean(mdl77_ohistoricalgrowthfactor_speghc, 5) +ts_mean(oth432_rasv2splitprofitabilityni_ttm_profitability8, 250) +ts_mean(anl82_ebty_profitability_profitability12, 5) +ts_mean(oth395_csjones_industry_r2_major_14, 20) +ts_mean(anl15_ebtgics_gr_18_m_cos_dn, 5) +ts_mean(oth432_compustat_models_leverage_payoff4, 20) +ts_mean(mdl77_2put_put_qualmod, 5) +ts_mean(mdl77_earningmomentumfactor_stdevfy1epsp, 250) +ts_mean(fnd28_wcnddq1_value_04001q, 20) +ts_mean(fnd28_newa1_value_05260a, 60) +ts_mean(mdl77_2gdna_yoychggpm, 20) +ts_mean(anl69_best_ptp_4wk_up, 5) +ts_mean(mdl26_prsprise_flag_fq4_earnings, 60) +ts_mean(anl14_median_ebit_fp4, 250) +ts_mean(pv20_eps_indicator_p_feature6, 60) +ts_mean(oth432_che_profitability_profitability9, 20) +ts_mean(oth432_piq_profitability_profitability7, 5) +ts_mean(fnd65_us5000_cusip_fc_numrevy1, 60) +ts_mean(nws7_news_freq_2_qerf, 60) +ts_mean(fnd65_totalcap_cusip_y3speq4rqsr, 20) +ts_mean(pv20_opr_indicator_n_feature2, 60) +ts_mean(anl15_ebtgics_gr_cal_fy3_1m_chg, 250) +ts_mean(fnd23_annfv1a_tait, 20) +ts_mean(mdl77_2gdna_mktcappera, 250) +ts_mean(fnd89_major_13_12, 60) +ts_mean(fnd6_newqeventv110_spceq, 60) +ts_mean(earning_number_value, 250) +ts_mean(fnd3_aacctadj_interestincome, 60) +ts_mean(fnd28_wcishtq_value_01551q, 20) +ts_mean(pv20_gps_indicator_7_feature0, 60) +ts_mean(mdl77_put_put_valmod, 250) +ts_mean(pv87_weightedavg60_group_v2_0_1_ess_earnings, 250) +ts_mean(anl82_netq_q2_predict, 20) +ts_mean(est_q_prr_mean_28d, 250) +ts_mean(anl44_2_netprofit_rep_lastactvalue, 60) +ts_mean(mdl77_2earningsmomemtummodel_fc_fqsurstd, 5) +ts_mean(fnd65_totalcap_cusip_y3speq4vc, 5) +ts_mean(pv87_daily_qtr_matrix_net_income_gaap_consensus_mean_numnochangeunfiltered, 20) +ts_mean(anl14_high_eps_fp4, 5) +ts_mean(oth460_2l_tbie, 20) +ts_mean(mdl262_rasv2splitprofitabilityebitd_q_profitability12, 5) +ts_mean(pv20_sal_indicator_3_feature6, 60) +ts_mean(fnd23_annfv1a_niis, 60) +ts_mean(fnd65_us5000_cusip_reinrate, 60) +ts_mean(fnd7_ointfund_r21fpeo, 5) +ts_mean(fnd7_ointfund_rqpdbio, 250) +ts_mean(star_v14_val_earnings_projection_fy9, 5) +ts_mean(anl69_eps_best_eeps_cur_yr, 20) +ts_mean(pv87_2_eps_af_matrix_p1_chngratio_number, 5) +ts_mean(fnd89_jones_nondiscretionary_accrual_major_11, 20) +ts_mean(anl15_ebtgics_ind_18_m_mean, 60) +ts_mean(fnd7_ointfund_rqer, 5) +ts_mean(scaling_factor_fy2_earnings, 60) +ts_mean(fnd28_wsu_value_04101a, 20) +ts_mean(anl14_actvalue_ntprep_fp0, 5) +ts_mean(pv87_2_eps_af_matrix_all_dts, 5) +ts_mean(anl14_stddev_ntp_fy4, 20) +ts_mean(est_q_net_raisednum_4wks, 20) +ts_mean(anl15_ebtgics_s_12_m_1m_chg, 60) +ts_mean(anl14_numofests_opp_fy1, 20) +ts_mean(mdl230_allcap_sedol_pqipmtt, 250) +ts_mean(anl15_salgics_gr_cal_fy2_pe, 5) +ts_mean(mean_neutral_profitability_answer, 20) +ts_mean(mdl31_oe_prior_qtr, 5) +ts_mean(mdl262_compustat_models_profitability10, 5) +ts_mean(mdl211_netq_q3_mada, 60) +ts_mean(anl14_low_eps_fy4, 250) +ts_mean(mdl262_ebit_profitability_profitability3, 60) +ts_mean(anl82_delta_netq_q2_mae, 60) +ts_mean(mdl262_rasv2splitprofitabilityni_q_profitability3, 250) +ts_mean(anl69_cest_best_eeps_cur_yr, 60) +ts_mean(quarterly_ebit_total, 60) +ts_mean(mdl77_earningsqualityfactor_ccacw, 250) +ts_mean(oth432_predictiveni_a_predict, 60) +ts_mean(fnd28_value_05212a, 5) +ts_mean(fnd65_totalcap_cusip_rp, 60) +ts_mean(oth432_rasv2splitprofitabilityni_ttm_profitability10, 250) +ts_mean(anl15_ebt_s_18_m_total, 5) +ts_mean(anl14_numofests_eps_fy5, 60) +ts_mean(mdl26_stm_chng_fq2_rnngs_30, 250) +ts_mean(mdl230_totalcap_cusip_min2yopmargin, 20) +ts_mean(mdl77_2gdna_ttmaccu, 20) +ts_mean(oth460_spceeps_class, 5) +ts_mean(mdl230_totalcap_cusip_numrevy2, 60) +ts_mean(est_q_prr_median, 20) +ts_mean(anl4_afv4_dts_spe, 60) +ts_mean(pv20_eps_indicator_6_feature10, 20) +ts_mean(fnd65_us5000_cusip_y5speq4vc, 60) +ts_mean(fnd72_s_pit_or_cr_q_trail_12m_oper_inc, 60) +ts_mean(fnd3_a_retained_ear_fast_d1, 20) +ts_mean(oth432_ninc_q_profitability_profitability2, 60) +ts_mean(anl49_vector_4thfiscalquarterearningspershare, 5) +ts_mean(anl14_mean_ebitda_fp3, 20) +ts_mean(mdl138_qpdi5_op_margin, 60) +ts_mean(mdl262_rasv2splitprofitabilityebitd_ttm_profitability2, 5) +ts_mean(oth432_ninc_trkdpitdeltapredict_funda_predict, 60) +ts_mean(mdl77_oput_put_valmod, 60) +ts_mean(fnd28_value_05223a, 250) +ts_mean(anl69_dps_new_expected_report_dt, 20) +ts_mean(oth432_che_profitability_profitability9, 60) +ts_mean(mdl26_smrtst_grwth_f24mf12m_rnngs, 60) +ts_mean(mdl26_dsnc_clstr_bgn_fy1_rnngs, 5) +ts_mean(mdl230_totalcap_cusip_twepsstdrev, 20) +ts_mean(est_q_opr_mean_4wks_ago, 5) +ts_mean(anl10_ebifq1_consensus_2220, 20) +ts_mean(fnd6_mfmq_piq, 5) +ts_mean(oth460_snpm_class, 20) +ts_mean(anl15_ebt_s_12_m_1m_chg, 60) +ts_mean(pv20_ebt_indicator_7_feature6, 20) +ts_mean(anl15_ebtgics_gr_cal_fy1_3m_chg, 5) +ts_mean(next_earnings_quarter_fast_d1, 60) +ts_mean(mdl262_predictiveebitd_ttm_predict, 250) +ts_mean(anl15_ebt_gr_cal_fy1_mean, 60) +ts_mean(mdl211_preq_deltaprofitability_profitability2, 60) +ts_mean(fnd65_us5000_cusip_chgroeart, 20) +ts_mean(projected_earnings_fy5, 250) +ts_mean(mdl116_epsyld_fy2_trendpredict_model_predict, 20) +ts_mean(mdl77_2put_put_momod, 250) +ts_mean(fnd72_s_pit_or_cr_q_announcement_dt, 5) +ts_mean(mdl116_epsyld_fy2_deltapredict_model_predict, 250) +ts_mean(mdl264_3l_eiuv, 250) +ts_mean(pretax_income_reported_value, 5) +ts_mean(anl82_prey_y1_mada, 250) +ts_mean(anl10_gpsfq1_pred_surps_v1_2648, 60) +ts_mean(pv20_gps_indicator_9_feature1, 5) +ts_mean(anl44_2_pretaxprofit_value, 5) +ts_mean(anl4_ebitda_std, 60) +ts_mean(anl14_numofests_ebitda_fy5, 5) +ts_mean(anl14_low_ebitda_fp1, 250) +ts_mean(anl14_numofests_epsrep_fy5, 60) +ts_mean(pv20_opr_indicator_q_feature11, 20) +ts_mean(pv87_2_eps_qf_matrix_all_low, 250) diff --git a/手动创建新金融逻辑1.txt b/手动创建新金融逻辑1.txt index 2245894..e9fc3d9 100644 --- a/手动创建新金融逻辑1.txt +++ b/手动创建新金融逻辑1.txt @@ -1,16 +1,19 @@ 我正在设计一个基于金融逻辑的量化 alpha 因子研究方向。请为我生成一个全新的、具有金融直觉支撑的研究主题，并严格按照以下格式输出： -名称 -（用中文简洁命名该因子策略，如“供应链集中度风险溢价”） +研究因子名称 +（用中文简洁命名该因子策略，如“xxxx”你自己起名） 假设 -（用一段中文清晰阐述经济或行为金融逻辑：为什么该现象可能导致系统性定价偏差？需说明多空逻辑） +（用一段中文清晰阐述经济或行为金融逻辑：需要时一个符合金融直觉的逻辑） 实施方案 -（用一段中文描述如何从数据层面实现该因子：包括核心指标、是否需要标准化/行业中性化、是否使用时序算子处理缺失值、是否引入辅助验证变量等） +（用一段中文描述如何从数据层面实现该因子） + +*=*=*=*=*=* 输出格式要求 =*=*=*=*=*=*= 紧接着，在下一行，输出一个纯 Python 列表，包含可用于在 EAV 结构数据库中模糊匹配 name 字段的基础英文词根（keyword stems），要求如下： 仅含字符串，无任何注释、说明或额外内容； - 每个词必须是单个、不可再分的英文核心词干（例如：supplier、concentration、payable）； - 禁止出现带数字、下划线、斜杠或复合结构的词（如 top5、supplier_ratio、client_dependency 等）； - 覆盖主指标（如衡量集中度的字段）及辅助验证指标（如采购、成本、账款等）； + 每个词必须是单个、不可再分的英文核心词干； + 禁止出现带数字、下划线、斜杠或复合结构的词； + 覆盖主指标（如衡量集中度的字段）及辅助验证指标； 所有词小写，无重复。 -请确保整体输出仅包含上述四部分，不要添加任何额外解释、标题或格式。 \ No newline at end of file +请确保整体输出仅包含上述四部分，不要添加任何额外解释、标题或格式。 +以上输出完之后, 将上述的研究主题的英文版, 也生成一份 \ No newline at end of file