parent
3508a1b496
commit
f2a149d2cf
@ -0,0 +1,5 @@ |
||||
你好！😊 很高兴见到你！ |
||||
|
||||
看起来你在问候后面加了一些空格，不管怎样，我都很开心能和你聊天！我是DeepSeek，一个热心的AI助手，随时准备为你提供帮助。 |
||||
|
||||
无论你想聊什么话题，需要解答问题，还是想要一些建议，我都会尽我所能地帮助你。今天有什么特别想了解或讨论的吗？✨ |
||||
@ -0,0 +1,5 @@ |
||||
rank(((ts_rank(close,10)-ts_rank(close,50)))*rank(ts_rank(volume,5))) |
||||
rank(ts_corr(ts_rank(close,20),ts_rank(volume,5),10)) |
||||
-1*rank((close-ts_mean(close,10))/ts_std_dev(close,10)*ts_mean(volume,30)) |
||||
rank(ts_delta(close,15)*rank(ts_corr((high+low)/2,volume,15))) |
||||
-1*rank(ts_rank(close,5)-ts_rank(close,60))*rank(ts_std_dev(close,20)*ts_mean(volume,40)) |
||||
@ -0,0 +1,5 @@ |
||||
ts_mean(group_rank(ts_mean(return_60(close), 20)), 5) / ts_std_dev(return_60(close), 20) |
||||
group_rank(ts_rank(ts_mean(close/vwap, 60), 20)) - group_rank(ts_rank(close/vwap, 5)) |
||||
ts_delay(group_rank(ts_corr(ts_mean(close, 5), ts_mean(volume, 5), 10)), 5) * ts_mean(return_20(close), 5) |
||||
group_rank(ts_mean(return_20(close), 10)) / group_rank(ts_std_dev(return_5(close), 20)) |
||||
group_rank(ts_mean(return_10(close), 5)) - group_mean(ts_mean(return_10(close), 5)) |
||||
@ -0,0 +1,20 @@ |
||||
|
||||
|
||||
Here are the five industry factors using WebSim functions: |
||||
|
||||
```python |
||||
group_mean(ts_return(close, 252), industry) # Industry Momentum Factor |
||||
group_mean((close / ts_mean(close, 252) - 1), industry) # Valuation Repair Factor |
||||
group_mean(ts_sum((close - ts_lag(close, 1)) * volume, 21), industry) # Capital Flow Factor |
||||
group_mean(ts_std(ts_return(close, 1), 21), industry) # Volatility Adjustment Factor |
||||
group_mean(ts_rank(close, 252), industry) # Relative Strength Factor |
||||
``` |
||||
|
||||
These factors: |
||||
1. Measure industry momentum using 12-month returns |
||||
2. Evaluate valuation through 200-day mean reversion |
||||
3. Track capital flow with 1-month price-volume accumulation |
||||
4. Assess volatility risk with 21-day standard deviation |
||||
5. Gauge cross-sectional strength via 12-month price ranking |
||||
|
||||
Each is calculated by averaging stock-level metrics within industry groups using `group_mean()` for industry-specific signals. |
||||
@ -0,0 +1,18 @@ |
||||
|
||||
|
||||
```python |
||||
# 行业动量因子（相对强度） |
||||
group_rank(ts_mean(close / close[3], 3), 'industry') |
||||
|
||||
# 行业估值修复因子（均值回归） |
||||
group_rank(ts_mean(close, 3) / close, 'industry') |
||||
|
||||
# 行业资金流向因子（量价结合） |
||||
group_rank(ts_sum(volume * (close - close[1]), 3), 'industry') |
||||
|
||||
# 行业波动率特征因子（风险调整） |
||||
group_rank(1 / (ts_std(close / close[1], 3) + 1e-8), 'industry') |
||||
|
||||
# 行业间相对强度因子（横截面） |
||||
group_rank(close / ts_max(close, 3), 'industry') |
||||
``` |
||||
@ -0,0 +1,7 @@ |
||||
{ |
||||
"siliconflow": { |
||||
"base_url": "https://api.siliconflow.cn/v1", |
||||
"api_keys": "sk-pvdiisdowmuwkrpnxsrlhxaovicqibmlljwrwwvbbdjaitdl", |
||||
"model": "Qwen/Qwen3-VL-235B-A22B-Instruct" |
||||
} |
||||
} |
||||
@ -1,220 +1,133 @@ |
||||
"""Alpha 因子表达式生成器 - 增强随机版本""" |
||||
|
||||
import random |
||||
import itertools |
||||
from typing import List |
||||
|
||||
class AlphaGenerator: |
||||
"""增强版因子表达式生成器，支持随机生成多样化因子""" |
||||
|
||||
def __init__(self, seed=None): |
||||
if seed: |
||||
random.seed(seed) |
||||
|
||||
# 基础操作符和函数库 |
||||
OPERATORS = ['+', '-', '*', '/'] |
||||
UNARY_OPERATORS = ['-', 'abs', 'rank', 'log', 'power', 'sqrt'] |
||||
TIME_SERIES_FUNCTIONS = [ |
||||
'delay', 'ts_mean', 'ts_std_dev', 'ts_rank', 'ts_decay_linear', |
||||
'ts_corr', 'ts_skewness', 'ts_kurtosis', 'ts_max', 'ts_min', |
||||
'ts_sum', 'ts_product', 'ts_delta', 'ts_returns' |
||||
] |
||||
GROUP_FUNCTIONS = [ |
||||
'group_rank', 'group_mean', 'group_std', 'group_neutralize', |
||||
'group_zscore', 'group_percentile' |
||||
] |
||||
CONDITIONAL_FUNCTIONS = [ |
||||
'if_else', 'trade_when', 'condition_filter', 'signal_when' |
||||
] |
||||
|
||||
# 基础字段模板 |
||||
BASE_FIELDS = ['open', 'close', 'high', 'low', 'volume', 'returns', |
||||
'vwap', 'turnover', 'volatility', 'cap', 'market_cap'] |
||||
|
||||
def generate_random_expression(self, datafields, depth=0, max_depth=3): |
||||
"""递归生成随机表达式""" |
||||
if depth >= max_depth or random.random() < 0.3: |
||||
# 叶子节点 - 选择基础字段或常数 |
||||
if random.random() < 0.7 and datafields: |
||||
field = random.choice(datafields) |
||||
return field |
||||
else: |
||||
return f"{random.uniform(0.1, 10.0):.2f}" |
||||
|
||||
# 选择操作类型 |
||||
op_type = random.choice(['binary', 'unary', 'ts_function', 'group_function', 'conditional']) |
||||
|
||||
if op_type == 'binary': |
||||
left = self.generate_random_expression(datafields, depth + 1, max_depth) |
||||
right = self.generate_random_expression(datafields, depth + 1, max_depth) |
||||
op = random.choice(self.OPERATORS) |
||||
return f"({left} {op} {right})" |
||||
|
||||
elif op_type == 'unary': |
||||
arg = self.generate_random_expression(datafields, depth + 1, max_depth) |
||||
op = random.choice(self.UNARY_OPERATORS) |
||||
if op in ['-', 'abs']: |
||||
return f"{op}({arg})" |
||||
else: |
||||
return f"{op}({arg}, {random.randint(1, 30)})" |
||||
|
||||
elif op_type == 'ts_function': |
||||
func = random.choice(self.TIME_SERIES_FUNCTIONS) |
||||
arg1 = self.generate_random_expression(datafields, depth + 1, max_depth) |
||||
lookback = random.choice([5, 10, 20, 30, 60, 90, 252]) |
||||
|
||||
if func in ['ts_corr', 'ts_covariance']: |
||||
arg2 = self.generate_random_expression(datafields, depth + 1, max_depth) |
||||
return f"{func}({arg1}, {arg2}, {lookback})" |
||||
else: |
||||
return f"{func}({arg1}, {lookback})" |
||||
|
||||
elif op_type == 'group_function': |
||||
func = random.choice(self.GROUP_FUNCTIONS) |
||||
arg = self.generate_random_expression(datafields, depth + 1, max_depth) |
||||
group_by = random.choice(['subindustry', 'industry', 'sector', 'bucket(cap, range="0.1,1,0.1")']) |
||||
return f"{func}({arg}, {group_by})" |
||||
|
||||
else: # conditional |
||||
func = random.choice(self.CONDITIONAL_FUNCTIONS) |
||||
condition = self.generate_random_expression(datafields, depth + 1, max_depth) |
||||
true_val = self.generate_random_expression(datafields, depth + 1, max_depth) |
||||
false_val = self.generate_random_expression(datafields, depth + 1, max_depth) |
||||
return f"{func}({condition} > 0, {true_val}, {false_val})" |
||||
|
||||
def generate_basic_strategies(self, datafields, count=50): |
||||
"""生成基础单因子策略""" |
||||
strategies = [] |
||||
|
||||
for _ in range(count): |
||||
# 随机选择1-3个字段组合 |
||||
num_fields = random.randint(1, 3) |
||||
selected_fields = random.sample(datafields, min(num_fields, len(datafields))) |
||||
|
||||
# 生成随机表达式 |
||||
expr = self.generate_random_expression(selected_fields) |
||||
|
||||
# 添加分组操作 |
||||
if random.random() < 0.7: |
||||
group_by = random.choice(['subindustry', 'industry']) |
||||
expr = f"group_rank({expr}, {group_by})" |
||||
|
||||
strategies.append(expr) |
||||
|
||||
return strategies |
||||
|
||||
def generate_multi_factor_strategies(self, datafields, count=50): |
||||
"""生成多因子组合策略""" |
||||
strategies = [] |
||||
|
||||
for _ in range(count): |
||||
# 选择更多字段进行复杂组合 |
||||
num_fields = random.randint(2, 5) |
||||
selected_fields = random.sample(datafields, min(num_fields, len(datafields))) |
||||
|
||||
# 生成更复杂的表达式 |
||||
expr = self.generate_random_expression(selected_fields, max_depth=4) |
||||
|
||||
# 添加中性化或标准化 |
||||
if random.random() < 0.6: |
||||
neutral_type = random.choice(['group_neutralize', 'regression_neut', 'vector_neut']) |
||||
if neutral_type == 'group_neutralize': |
||||
group_by = random.choice(['subindustry', 'industry', 'sector']) |
||||
expr = f"{neutral_type}({expr}, {group_by})" |
||||
else: |
||||
# 为回归中性化选择另一个因子 |
||||
other_field = random.choice([f for f in datafields if f not in selected_fields] or selected_fields) |
||||
expr = f"{neutral_type}({expr}, {other_field})" |
||||
|
||||
strategies.append(expr) |
||||
|
||||
return strategies |
||||
|
||||
def generate_advanced_strategies(self, datafields, count=30): |
||||
"""生成高级策略（均值回归、动量、波动率策略等）""" |
||||
strategies = [] |
||||
|
||||
# 均值回归策略 |
||||
for _ in range(count // 3): |
||||
field = random.choice(datafields) |
||||
lookback = random.choice([5, 10, 20, 30]) |
||||
expr = f"-ts_rank({field} - ts_mean({field}, {lookback}), 20)" |
||||
strategies.append(f"group_neutralize({expr}, subindustry)") |
||||
|
||||
# 动量策略 |
||||
for _ in range(count // 3): |
||||
field = random.choice(datafields) |
||||
short_lookback = random.choice([1, 2, 3, 5]) |
||||
long_lookback = random.choice([10, 20, 30, 60]) |
||||
expr = f"ts_mean({field}, {short_lookback}) - ts_mean({field}, {long_lookback})" |
||||
strategies.append(f"group_rank({expr}, subindustry)") |
||||
|
||||
# 波动率策略 |
||||
for _ in range(count // 3): |
||||
field = random.choice(datafields) |
||||
vol_lookback = random.choice([10, 20, 30]) |
||||
expr = f"{field} / ts_std_dev({field}, {vol_lookback})" |
||||
strategies.append(f"group_rank({expr}, subindustry)") |
||||
|
||||
return strategies |
||||
|
||||
def generate_strategies(self, datafields, mode=1, count=100): |
||||
""" |
||||
生成因子表达式列表 |
||||
|
||||
参数: |
||||
datafields: 数据字段列表 |
||||
mode: 生成模式 (1=基础策略, 2=多因子组合, 3=高级策略, 4=混合模式) |
||||
count: 生成数量 |
||||
|
||||
返回: |
||||
因子表达式列表 |
||||
""" |
||||
|
||||
all_fields = datafields + self.BASE_FIELDS |
||||
unique_fields = list(set(all_fields)) # 去重 |
||||
|
||||
if mode == 1: |
||||
return self.generate_basic_strategies(unique_fields, count) |
||||
elif mode == 2: |
||||
return self.generate_multi_factor_strategies(unique_fields, count) |
||||
elif mode == 3: |
||||
return self.generate_advanced_strategies(unique_fields, count) |
||||
elif mode == 4: |
||||
# 混合模式 - 从所有类型中随机选择 |
||||
basic = self.generate_basic_strategies(unique_fields, count // 3) |
||||
multi = self.generate_multi_factor_strategies(unique_fields, count // 3) |
||||
advanced = self.generate_advanced_strategies(unique_fields, count // 3) |
||||
return basic + multi + advanced |
||||
else: |
||||
print("❌ 无效的生成模式，使用混合模式") |
||||
return self.generate_strategies(unique_fields, mode=4, count=count) |
||||
|
||||
|
||||
# 使用示例 |
||||
import os |
||||
import json |
||||
import openai |
||||
from datetime import datetime |
||||
|
||||
|
||||
def load_config(config_file="config.json"): |
||||
try: |
||||
with open(config_file, 'r', encoding='utf-8') as f: |
||||
return json.load(f) |
||||
except FileNotFoundError: |
||||
print(f"创建配置文件: {config_file}") |
||||
with open(config_file, 'w', encoding='utf-8') as f: |
||||
json.dump({}, f, indent=4) |
||||
print("请在config.json中配置siliconflow的api信息") |
||||
return None |
||||
|
||||
|
||||
def create_prompt_file(): |
||||
if not os.path.exists("prompt.txt"): |
||||
with open("prompt.txt", 'w', encoding='utf-8') as f: |
||||
f.write("") |
||||
print("已创建prompt.txt，请填入提示词后重新运行") |
||||
return False |
||||
return True |
||||
|
||||
|
||||
def read_prompt(): |
||||
with open("prompt.txt", 'r', encoding='utf-8') as f: |
||||
prompt = f.read().strip() |
||||
if not prompt: |
||||
print("prompt.txt是空的，请填入提示词") |
||||
return None |
||||
return prompt |
||||
|
||||
|
||||
def create_result_folder(): |
||||
if not os.path.exists("ai_result"): |
||||
os.makedirs("ai_result") |
||||
return "ai_result" |
||||
|
||||
|
||||
def call_siliconflow(api_key, prompt, model, base_url): |
||||
try: |
||||
client = openai.OpenAI( |
||||
api_key=api_key, |
||||
base_url=base_url |
||||
) |
||||
|
||||
response = client.chat.completions.create( |
||||
model=model, |
||||
messages=[{"role": "user", "content": prompt}] |
||||
) |
||||
|
||||
return response.choices[0].message.content |
||||
|
||||
except openai.AuthenticationError: |
||||
print("API密钥错误") |
||||
except openai.RateLimitError: |
||||
print("调用频率限制") |
||||
except openai.APIError as e: |
||||
print(f"API错误: {e}") |
||||
except Exception as e: |
||||
print(f"其他错误: {e}") |
||||
return None |
||||
|
||||
|
||||
def save_result(result, folder): |
||||
timestamp = datetime.now().strftime("%Y%m%d%H%M%S") |
||||
filename = f"{timestamp}.txt" |
||||
filepath = os.path.join(folder, filename) |
||||
|
||||
with open(filepath, 'w', encoding='utf-8') as f: |
||||
f.write(result) |
||||
print(f"结果保存到: {filepath}") |
||||
|
||||
|
||||
def get_ai_config(config): |
||||
if not config: |
||||
return None |
||||
|
||||
service_name = list(config.keys())[0] |
||||
service_config = config[service_name] |
||||
|
||||
required_fields = ["api_keys", "model", "base_url"] |
||||
for field in required_fields: |
||||
if field not in service_config: |
||||
print(f"缺少配置字段: {field}") |
||||
return None |
||||
|
||||
return service_name, service_config |
||||
|
||||
|
||||
def main(): |
||||
config = load_config() |
||||
if not config: |
||||
return |
||||
|
||||
if not create_prompt_file(): |
||||
return |
||||
|
||||
prompt = read_prompt() |
||||
if not prompt: |
||||
return |
||||
|
||||
config_result = get_ai_config(config) |
||||
if not config_result: |
||||
return |
||||
|
||||
service_name, service_config = config_result |
||||
|
||||
print(f"使用服务: {service_name}") |
||||
print(f"使用模型: {service_config['model']}") |
||||
|
||||
folder = create_result_folder() |
||||
|
||||
print("正在调用AI...") |
||||
result = call_siliconflow( |
||||
service_config["api_keys"], |
||||
prompt, |
||||
service_config["model"], |
||||
service_config["base_url"] |
||||
) |
||||
|
||||
if result: |
||||
print(f"AI回复: {result[:200]}...") |
||||
save_result(result, folder) |
||||
else: |
||||
print("AI调用失败") |
||||
|
||||
|
||||
if __name__ == "__main__": |
||||
# 示例数据字段 |
||||
sample_fields = ['volume', 'close', 'open', 'high', 'low', 'returns', 'turnover', 'pe_ratio', 'pb_ratio'] |
||||
|
||||
generator = AlphaGenerator(seed=42) # 设置随机种子以便复现 |
||||
|
||||
print("=== 基础策略因子 (20个示例) ===") |
||||
basic_strategies = generator.generate_strategies(sample_fields, mode=1, count=20) |
||||
for i, strategy in enumerate(basic_strategies[:10], 1): # 只显示前10个 |
||||
print(f"{i}. {strategy}") |
||||
|
||||
print(f"\n共生成 {len(basic_strategies)} 个基础因子") |
||||
|
||||
print("\n=== 多因子组合策略 (20个示例) ===") |
||||
multi_strategies = generator.generate_strategies(sample_fields, mode=2, count=20) |
||||
for i, strategy in enumerate(multi_strategies[:10], 1): |
||||
print(f"{i}. {strategy}") |
||||
|
||||
print(f"\n共生成 {len(multi_strategies)} 个多因子组合") |
||||
|
||||
print("\n=== 随机测试 - 每次运行生成不同的因子 ===") |
||||
for run in range(3): |
||||
print(f"\n--- 第{run+1}次运行 ---") |
||||
random_strategies = generator.generate_strategies(sample_fields, mode=4, count=5) |
||||
for i, strategy in enumerate(random_strategies, 1): |
||||
print(f"{i}. {strategy}") |
||||
main() |
||||
@ -0,0 +1,37 @@ |
||||
【重要平台约束】 |
||||
平台：WorldQuant WebSim |
||||
可用函数：ts_*(时间序列), group_*(横截面), 基础数学运算 |
||||
可用字段：open, high, low, close, volume, vwap, sector, industry, country, market_cap |
||||
禁用函数：pandas, numpy, 自定义Python函数，机器学习库 |
||||
输出格式：严格一行WebSim表达式，使用WQ平台函数 |
||||
表达式要求：必须能直接在WebSim中运行 |
||||
|
||||
我正在WorldQuant研究行业轮动策略，请设计行业选择因子： |
||||
|
||||
**策略背景**： |
||||
- 目标：在不同经济周期中选择强势行业 |
||||
- 数据：使用sector, industry分类字段 |
||||
- 频率：月度调仓 |
||||
- 风险：控制行业集中度 |
||||
|
||||
**需要5个行业因子**： |
||||
1. 行业动量因子（相对强度） |
||||
2. 行业估值修复因子（均值回归） |
||||
3. 行业资金流向因子（量价结合） |
||||
4. 行业波动率特征因子（风险调整） |
||||
5. 行业间相对强度因子（横截面） |
||||
|
||||
**具体要求**： |
||||
- 必须使用group函数处理行业分组 |
||||
- 每个因子包含清晰的行业轮动逻辑 |
||||
- 考虑行业特性的持久性 |
||||
- 提供经济周期适应的解释 |
||||
|
||||
**输出格式**(一行一个, 不要输出多余的东西)： |
||||
[表达式] |
||||
[表达式] |
||||
[表达式] |
||||
... |
||||
|
||||
请提供具体的WQ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。 |
||||
@ -0,0 +1,37 @@ |
||||
# -*- coding: utf-8 -*- |
||||
import json |
||||
import httpx |
||||
|
||||
with open('config.json', 'r') as f: |
||||
config = json.load(f) |
||||
siliconflow = config['siliconflow'] |
||||
token = siliconflow['api_keys'] |
||||
|
||||
headers = {"Authorization": f"Bearer {token}"} |
||||
|
||||
def get_user_info(): |
||||
# 获取用户信息 |
||||
url = "https://api.siliconflow.cn/v1/user/info" |
||||
|
||||
response = httpx.get(url, headers=headers) |
||||
|
||||
for key, value in response.json().items(): |
||||
if key != "data": |
||||
print(f"{key}: {value}") |
||||
else: |
||||
for sub_key, sub_value in value.items(): |
||||
print(f"{sub_key}: {sub_value}") |
||||
|
||||
|
||||
def get_models_info(): |
||||
url = "https://api.siliconflow.cn/v1/models" |
||||
|
||||
response = httpx.get(url, headers=headers) |
||||
|
||||
data = response.json()['data'] |
||||
|
||||
for d in data: |
||||
print(d) |
||||
|
||||
# get_models_info() |
||||
# get_user_info() |
||||
Loading…
Reference in new issue