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464 lines
15 KiB
464 lines
15 KiB
# -*- coding: utf-8 -*-
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import os
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import random
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import sys
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import openai
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import httpx
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import csv
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from datetime import datetime
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import jieba
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import time
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import sqlite3
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sys.path.append(os.path.join(os.path.abspath(__file__).split('AlphaGenerator')[0] + 'AlphaGenerator'))
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PROJECT_PATH = os.path.join(os.path.abspath(__file__).split('AlphaGenerator')[0] + 'AlphaGenerator')
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PREPARE_PROMPT = os.path.join(PROJECT_PATH, 'prepare_prompt')
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KEYS_TEXT = os.path.join(PREPARE_PROMPT, 'keys_text.txt')
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USE_AI = 0
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TEMPERATURE = 0.1
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RANDOM_DATA_SETS_COUNT = 0
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# 数据集筛选之后大于此数值, 则随机抽取x条数据
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MAX_DATA_COUNT = 800
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RANDOM_DATA_COUNT = 400
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# 数据库搜索字段
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REGION = 'USA'
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UNIVERSE = 'TOP3000'
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LOAD_OPERATOR = 0
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SILICONFLOW_API_KEY = "sk-pvdiisdowmuwkrpnxsrlhxaovicqibmlljwrwwvbbdjaitdl"
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SILICONFLOW_BASE_URL = "https://api.siliconflow.cn/v1"
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MODELS = [
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'Pro/deepseek-ai/DeepSeek-V3.1-Terminus',
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'deepseek-ai/DeepSeek-V3.2-Exp',
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'Qwen/Qwen3-VL-235B-A22B-Instruct',
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# 'MiniMaxAI/MiniMax-M2',
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# 'zai-org/GLM-4.6',
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# 'inclusionAI/Ring-flash-2.0',
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# 'zai-org/GLM-4.6',
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# 'inclusionAI/Ling-flash-2.0',
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# 'inclusionAI/Ring-flash-2.0',
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]
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def process_text(text):
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filter_list = ['\n', '\t', '\r', '\b', '\f', '\v', '：', '的', '或', '10', '天', '了', '可', '是', '该', '，', ' ', '、', '让', '和', '集',
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'/', '日', '在', '（', '_', '-', ')', '(', '上', '距', '与', '比', '下', '及', '）', '...', '；', '%', '&', '+', ',', '.',
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':', ';', '<', '=', '>', '?', '[', ']', '|', '—', '。'
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]
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text_list = jieba.lcut(text)
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results = []
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for tl in text_list:
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should_include = True
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for fl in filter_list:
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if fl == tl:
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should_include = False
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break
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if should_include:
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results.append(tl.lower())
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results = [item for item in results if item != '"' and len(item) > 2]
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if results:
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return list(set(results))
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else:
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return None
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def keysTextLoader():
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if not os.path.exists(KEYS_TEXT):
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print(f"文件不存在: {KEYS_TEXT}")
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exit(1)
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with open(KEYS_TEXT, 'r', encoding='utf-8') as f:
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text_list = [line.strip() for line in f if line.strip()]
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if not text_list:
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print('关键词文本无数据, 程序退出')
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exit(1)
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result_str = process_text(';'.join(text_list))
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print(f'\n关键词文本处理结果: {result_str}\n')
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return result_str
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def txtFileLoader(file_path):
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if not os.path.exists(file_path):
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print(f"文件不存在: {file_path}")
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exit(1)
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with open(file_path, 'r', encoding='utf-8') as f:
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return [line.strip() for line in f if line.strip()]
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def csvFileLoader(file_path, keys_text):
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if not os.path.exists(file_path):
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print(f"文件不存在: {file_path}")
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exit(1)
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data_dict = {} # 使用字典来存储，以id为键
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with open(file_path, 'r', encoding='utf-8') as f:
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reader = csv.reader(f)
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for row in reader:
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for key in keys_text:
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if key in row[11] or key in row[12]:
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item_id = row[0]
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# 如果id不存在，或者想要保留第一个出现的记录
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if item_id not in data_dict:
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data_dict[item_id] = {
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'id': int(row[0]),
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'data_set_name': f"可以使用:{row[1]}",
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'description': f"不可使用,仅供参考:{row[2]}"
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}
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# 将字典的值转换为列表
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return list(data_dict.values())
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def sqliteLoader(file_path, keys_text):
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if not os.path.exists(file_path):
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print(f"SQLite数据库文件不存在: {file_path}")
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exit(1)
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data_dict = {} # 使用字典来存储，以id为键
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try:
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conn = sqlite3.connect(file_path)
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cursor = conn.cursor()
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# 首先筛选符合 region 和 universe 条件的数据
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cursor.execute("SELECT id, name, description, region, universe FROM data_sets WHERE region=? AND universe=?",
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(REGION, UNIVERSE))
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rows = cursor.fetchall()
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for row in rows:
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row_id, name, description, region, universe = row
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# 检查关键词是否在 name 中
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for key in keys_text:
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if key in name:
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item_id = str(row_id)
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if item_id not in data_dict:
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data_dict[item_id] = {
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'id': int(row_id),
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'data_set_name': f"可以使用:{name}",
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'description': f"不可使用,仅供参考:{description}"
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}
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conn.close()
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# 将字典的值转换为列表
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return list(data_dict.values())
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except sqlite3.Error as e:
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print(f"SQLite数据库错误: {e}")
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exit(1)
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def extend_data_sets(file_path, original_data_sets):
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result = original_data_sets.copy()
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if not os.path.exists(file_path):
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print(f"文件不存在: {file_path}")
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return result
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all_data_sets = []
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with open(file_path, 'r', encoding='utf-8') as f:
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reader = csv.reader(f)
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for row in reader:
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all_data_sets.append({
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'id': int(row[0]),
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'data_set_name': f"可以使用:{row[1]}",
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'description': f"不可使用,仅供参考:{row[2]}",
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})
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if RANDOM_DATA_SETS_COUNT and RANDOM_DATA_SETS_COUNT > 0:
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total_count = len(original_data_sets) + RANDOM_DATA_SETS_COUNT
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# 找出所有可用的数据集（不在原始数据中的）
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available_datasets = []
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# 先获取原始数据集的所有ID
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original_ids = []
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for item in original_data_sets:
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original_ids.append(item['id'])
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# 找出不在原始数据中的数据集
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for dataset in all_data_sets:
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exists = False
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for oid in original_ids:
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if dataset['id'] == oid:
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exists = True
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break
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if not exists:
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available_datasets.append(dataset)
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# 计算最多能添加多少个
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max_can_add = len(available_datasets)
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need_to_add = RANDOM_DATA_SETS_COUNT
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if need_to_add > max_can_add:
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print(f"警告：要求添加{need_to_add}个，但只有{max_can_add}个可用")
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need_to_add = max_can_add
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# 随机选择需要数量的数据集
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random_selected = random.sample(available_datasets, need_to_add)
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# 添加到结果
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result.extend(random_selected)
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final_result = []
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for item in result:
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final_result.append({
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'data_set_name': item['data_set_name'],
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'description': item['description']
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})
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return final_result
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def promptLoader(alpha_prompt_path):
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if not os.path.exists(alpha_prompt_path):
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print("alpha_prompt.txt文件不存在")
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exit(1)
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with open(alpha_prompt_path, 'r', encoding='utf-8') as f:
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prompt = f.read().strip()
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if not prompt:
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print("alpha_prompt.txt是空的")
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exit(1)
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return prompt.replace('\n\n', '\n')
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def operatorLoader(operator_prompt_path):
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if not os.path.exists(operator_prompt_path):
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print("operator.txt文件不存在")
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exit(1)
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with open(operator_prompt_path, 'r', encoding='utf-8') as f:
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operator_lines = [line.strip() for line in f.readlines() if line.strip()]
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if not operator_lines:
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print("operator.txt是空的")
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exit(1)
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return "\n".join(operator_lines)
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def mistakesNoteBookLoader(mistakes_notebook_path):
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if not os.path.exists(mistakes_notebook_path):
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print("mistakes_notebook.txt文件不存在")
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return ''
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with open(mistakes_notebook_path, 'r', encoding='utf-8') as f:
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mistakes_notebook_lines = [line.strip() for line in f.readlines() if line.strip()]
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if not mistakes_notebook_lines:
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print("mistakes_notebook.txt是空的")
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exit(1)
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return "\n".join(mistakes_notebook_lines)
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def create_result_folder():
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folder_name = "generated_alpha"
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if not os.path.exists(folder_name):
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os.makedirs(folder_name)
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now = datetime.now()
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year_folder = os.path.join(folder_name, str(now.year))
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month_folder = os.path.join(year_folder, f"{now.month:02d}")
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day_folder = os.path.join(month_folder, f"{now.day:02d}")
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if not os.path.exists(year_folder):
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os.makedirs(year_folder)
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if not os.path.exists(month_folder):
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os.makedirs(month_folder)
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if not os.path.exists(day_folder):
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os.makedirs(day_folder)
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return day_folder
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def call_siliconflow(prompt, model):
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try:
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client = openai.OpenAI(
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api_key=SILICONFLOW_API_KEY,
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base_url=SILICONFLOW_BASE_URL
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)
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response = client.chat.completions.create(
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model=model,
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messages=[
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{"role": "system", "content": "你是一个专业的量化投资专家，擅长编写Alpha因子。"},
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{"role": "user", "content": prompt}
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],
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temperature=TEMPERATURE
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)
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return response.choices[0].message.content
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except openai.AuthenticationError:
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print("API密钥错误")
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except openai.RateLimitError:
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print("调用频率限制")
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except openai.APIError as e:
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print(f"API错误: {e}")
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except Exception as e:
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print(f"其他错误: {e}")
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exit(1)
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def save_result(result, folder, model_name):
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now = datetime.now()
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time_filename = now.strftime("%H%M%S")
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filename = f"{model_name}_{time_filename}.txt"
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filepath = os.path.join(folder, filename)
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with open(filepath, 'w', encoding='utf-8') as f:
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f.write(result)
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print(f"结果保存到: {filepath}")
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def get_user_info():
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headers = {"Authorization": f"Bearer {SILICONFLOW_API_KEY}"}
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url = "https://api.siliconflow.cn/v1/user/info"
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response = httpx.get(url, headers=headers)
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data = response.json()['data']
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balance = data['totalBalance']
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print(f"余额: {balance}")
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return float(balance)
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def manual_prompt(prompt):
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manual_prompt_path = os.path.join(PROJECT_PATH, "manual_prompt")
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if not os.path.exists(manual_prompt_path):
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os.makedirs(manual_prompt_path)
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now = datetime.now()
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year_folder = os.path.join(manual_prompt_path, str(now.year))
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month_folder = os.path.join(year_folder, f"{now.month:02d}")
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day_folder = os.path.join(month_folder, f"{now.day:02d}")
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if not os.path.exists(year_folder):
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os.makedirs(year_folder)
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if not os.path.exists(month_folder):
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os.makedirs(month_folder)
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if not os.path.exists(day_folder):
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os.makedirs(day_folder)
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# 文件名后添加保存时间
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filename = f"manual_prompt_{now.strftime('%Y%m%d%H%M%S')}.txt"
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filepath = os.path.join(day_folder, filename)
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with open(filepath, 'w', encoding='utf-8') as f:
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f.write(prompt)
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print(f"手动提示词保存到: {filepath}")
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def call_ai(prompt, model):
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balance = get_user_info()
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folder = create_result_folder()
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print(f"正在调用AI...{model}")
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result = call_siliconflow(prompt, model)
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if result:
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print(f"AI回复: {result[:200]}...")
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model_name = model.replace("/", "_")
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save_result(result, folder, model_name)
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used_balance = balance - get_user_info()
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print(f'本次调用 api 使用额度 {used_balance}')
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else:
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print("AI调用失败")
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def prepare_prompt(data_sets):
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prompt = ''
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# 读取基础提示词
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alpha_prompt_path = os.path.join(PREPARE_PROMPT, "alpha_prompt.txt")
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prompt += promptLoader(alpha_prompt_path)
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# 读取操作符
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prompt += "\n\n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\n\n"
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prompt += "========================= 操作符开始 =======================================\n"
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prompt += "注意: Operator: 后面的是操作符(是可以使用的),\nDescription: 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\n"
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prompt += "特别注意!!!! 必须按照操作符字段Operator的使用说明生成 alpha"
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if LOAD_OPERATOR:
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operator_prompt_path = os.path.join(PREPARE_PROMPT, "operator.txt")
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operator = operatorLoader(operator_prompt_path)
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prompt += operator
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prompt += "\n========================= 操作符结束 =======================================\n\n"
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prompt += "========================= 数据字段开始 =======================================\n"
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prompt += "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)\n\n"
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for data_set in data_sets:
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prompt += str(data_set) + '\n'
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prompt += "========================= 数据字段结束 =======================================\n\n"
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prompt += "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\n\n"
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mistakes_note_book_path = os.path.join(PREPARE_PROMPT, "mistakes_notebook.txt")
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mistakesNoteBook = mistakesNoteBookLoader(mistakes_note_book_path)
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if mistakesNoteBook:
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prompt += mistakesNoteBook
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return prompt
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def main():
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# 将金融逻辑, 分割成标签
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keys_text = keysTextLoader()
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# # 分割好的标签, 搜索对应的数据集, 返回匹配到的结果
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# data_sets_path = os.path.join(PREPARE_PROMPT, "all_data_combined.csv")
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# result_data_sets = csvFileLoader(data_sets_path, keys_text)
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# if not result_data_sets:
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# print(f'搜索数据集为空, 程序退出')
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# exit(1)
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data_sets_path = os.path.join(PREPARE_PROMPT, "data_sets.db")
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result_data_sets = sqliteLoader(data_sets_path, keys_text)
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if not result_data_sets:
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print(f'搜索数据集为空, 程序退出')
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exit(1)
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# 扩展数据集
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mistakes_notebook_path = os.path.join(PREPARE_PROMPT, "all_data_combined.csv")
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if RANDOM_DATA_SETS_COUNT:
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print('=' * 100)
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print(f'扩展前 {len(result_data_sets)} 条数据')
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result_data_sets = extend_data_sets(mistakes_notebook_path, result_data_sets)
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print(f'扩展后 {len(result_data_sets)} 条数据')
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print('=' * 100)
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print(f'从数据集中提取了 {len(result_data_sets)} 条数据')
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if len(result_data_sets) > MAX_DATA_COUNT:
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print(f'筛选数据集数量大于 {MAX_DATA_COUNT}, 随机选择其中的 {RANDOM_DATA_COUNT} 条')
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data_sets = random.sample(result_data_sets, RANDOM_DATA_COUNT)
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else:
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data_sets = result_data_sets
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# 组合提示词
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prompt = prepare_prompt(data_sets)
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# # 如果需要手动在页面段模型, 使用提示词, 打开这个, 将生成的提示词存到本地
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manual_prompt(prompt)
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if USE_AI:
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for model in MODELS:
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call_ai(prompt, model)
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time.sleep(5)
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if __name__ == "__main__":
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main() |