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alpha_id,alpha_type,author,settings,regular,dateCreated,dateSubmitted,dateModified,name,favorite,hidden,color,category,tags,classifications,grade,stage,status,is,os,train,test,prod,competitions,themes,pyramids,pyramidThemes,team,osmosisPoints
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npkZlgwd,REGULAR,YC93384,"{'instrumentType': 'EQUITY', 'region': 'USA', 'universe': 'TOP3000', 'delay': 1, 'decay': 0, 'neutralization': 'INDUSTRY', 'truncation': 0.01, 'pasteurization': 'ON', 'unitHandling': 'VERIFY', 'nanHandling': 'OFF', 'maxTrade': 'OFF', 'maxPosition': 'OFF', 'language': 'FASTEXPR', 'visualization': False, 'startDate': '2014-01-01', 'endDate': '2023-12-31'}","{'code': 'multiply(last_diff_value(ts_sum(subtract(implied_volatility_call_120, implied_volatility_put_90), 20), 5),ts_mean(volume,20))', 'description': 'Idea: Long volatility skew momentum in high-option-liquidity stocks weighted by trading volume.\nRationale for data used: implied_volatility_call_120, implied_volatility_put_90\nRationale for operators used: multiply, last_diff_value, ts_sum, subtract, ts_mean', 'operatorCount': 5}",2026-02-07T04:08:56-05:00,,2026-02-07T04:45:53-05:00,,False,False,,,[],[],,IS,UNSUBMITTED,"{'pnl': 25004583, 'bookSize': 20000000, 'longCount': 2140, 'shortCount': 787, 'turnover': 0.0744, 'returns': 0.2505, 'drawdown': 0.4119, 'margin': 0.006739, 'sharpe': 1.14, 'fitness': 1.61, 'startDate': '2014-01-01', 'investabilityConstrained': {'pnl': 13948144, 'bookSize': 20000000, 'longCount': 2166, 'shortCount': 763, 'turnover': 0.0627, 'returns': 0.1398, 'drawdown': 0.4277, 'margin': 0.004458, 'fitness': 1.03, 'sharpe': 0.97}, 'riskNeutralized': {'pnl': 17145827, 'bookSize': 20000000, 'longCount': 2140, 'shortCount': 787, 'turnover': 0.0744, 'returns': 0.1718, 'drawdown': 0.3665, 'margin': 0.004621, 'fitness': 1.15, 'sharpe': 0.98}, 'checks': [{'name': 'LOW_SHARPE', 'result': 'WARNING', 'limit': 1.58, 'value': 1.14}, {'name': 'LOW_FITNESS', 'result': 'PASS', 'limit': 1.0, 'value': 1.61}, {'name': 'LOW_TURNOVER', 'result': 'PASS', 'limit': 0.01, 'value': 0.0744}, {'name': 'HIGH_TURNOVER', 'result': 'PASS', 'limit': 0.7, 'value': 0.0744}, {'name': 'CONCENTRATED_WEIGHT', 'result': 'WARNING', 'date': '2017-09-21', 'limit': 0.1, 'value': 0.479658}, {'name': 'LOW_SUB_UNIVERSE_SHARPE', 'result': 'PASS', 'limit': 0.49, 'value': 0.52}, {'name': 'SELF_CORRELATION', 'result': 'PENDING'}, {'name': 'DATA_DIVERSITY', 'result': 'PENDING'}, {'name': 'PROD_CORRELATION', 'result': 'PENDING'}, {'name': 'REGULAR_SUBMISSION', 'result': 'PENDING'}, {'name': 'IS_LADDER_SHARPE', 'result': 'PASS', 'year': 2, 'startDate': '2024-01-02', 'endDate': '2022-01-03', 'limit': 2.02, 'value': 2.06}, {'name': 'POWER_POOL_CORRELATION', 'result': 'PENDING'}, {'name': 'MATCHES_COMPETITION', 'result': 'WARNING', 'competitions': [{'id': 'DCC2026', 'name': 'Data Creation Challenge 2026'}]}, {'result': 'PASS', 'name': 'MATCHES_PYRAMID', 'effective': 2, 'multiplier': 1.1, 'pyramids': [{'name': 'USA/D1/PV', 'multiplier': 1.1}, {'name': 'USA/D1/OPTION', 'multiplier': 1.3}]}, {'result': 'WARNING', 'name': 'MATCHES_THEMES', 'themes': [{'id': 'M4ZYl3D', 'multiplier': 2.0, 'name': 'USA HighTurnover Datasets Theme'}]}, {'result': 'PENDING', 'name': 'MATCHES_THEMES', 'themes': [{'id': 'EDrKz34', 'multiplier': 1.0, 'name': 'All regions/D1 Power Pool Mar`26'}]}, {'name': 'OSMOSIS_ALLOCATION', 'result': 'WARNING'}]}",,,,,,,,,,
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LLOgaLvL,REGULAR,YC93384,"{'instrumentType': 'EQUITY', 'region': 'USA', 'universe': 'TOP3000', 'delay': 1, 'decay': 0, 'neutralization': 'INDUSTRY', 'truncation': 0.08, 'pasteurization': 'ON', 'unitHandling': 'VERIFY', 'nanHandling': 'OFF', 'maxTrade': 'OFF', 'maxPosition': 'OFF', 'language': 'FASTEXPR', 'visualization': False, 'startDate': '2014-01-01', 'endDate': '2023-12-31'}","{'code': 'ts_rank(add(ts_count_nans(pv87_2_nav_qf_matrix_all_chngratio_median, 45), ts_count_nans(mdl264_call_put_erlanger_ratio_l3, 45)), 90)', 'description': None, 'operatorCount': 4}",2026-01-19T04:36:02-05:00,,2026-01-19T04:36:02-05:00,,False,False,,,[],[],,IS,UNSUBMITTED,"{'pnl': 10827219, 'bookSize': 20000000, 'longCount': 2486, 'shortCount': 389, 'turnover': 0.0484, 'returns': 0.1085, 'drawdown': 0.183, 'margin': 0.004481, 'sharpe': 1.27, 'fitness': 1.18, 'startDate': '2014-01-01', 'investabilityConstrained': {'pnl': 10545973, 'bookSize': 20000000, 'longCount': 2333, 'shortCount': 797, 'turnover': 0.038, 'returns': 0.1057, 'drawdown': 0.1061, 'margin': 0.005561, 'fitness': 1.27, 'sharpe': 1.38}, 'riskNeutralized': {'pnl': 3060381, 'bookSize': 20000000, 'longCount': 2486, 'shortCount': 389, 'turnover': 0.0484, 'returns': 0.0307, 'drawdown': 0.1434, 'margin': 0.001267, 'fitness': 0.3, 'sharpe': 0.6}, 'checks': [{'name': 'LOW_SHARPE', 'result': 'WARNING', 'limit': 1.58, 'value': 1.27}, {'name': 'LOW_FITNESS', 'result': 'PASS', 'limit': 1.0, 'value': 1.18}, {'name': 'LOW_TURNOVER', 'result': 'PASS', 'limit': 0.01, 'value': 0.0484}, {'name': 'HIGH_TURNOVER', 'result': 'PASS', 'limit': 0.7, 'value': 0.0484}, {'name': 'CONCENTRATED_WEIGHT', 'result': 'PASS'}, {'name': 'LOW_SUB_UNIVERSE_SHARPE', 'result': 'PASS', 'limit': 0.55, 'value': 0.95}, {'name': 'SELF_CORRELATION', 'result': 'PENDING'}, {'name': 'DATA_DIVERSITY', 'result': 'PENDING'}, {'name': 'PROD_CORRELATION', 'result': 'PENDING'}, {'name': 'REGULAR_SUBMISSION', 'result': 'PENDING'}, {'name': 'IS_LADDER_SHARPE', 'result': 'WARNING', 'year': 2, 'startDate': '2024-01-02', 'endDate': '2022-01-03', 'limit': 1.58, 'value': 1.26}, {'name': 'POWER_POOL_CORRELATION', 'result': 'PENDING'}, {'name': 'MATCHES_COMPETITION', 'result': 'WARNING', 'competitions': [{'id': 'DCC2026', 'name': 'Data Creation Challenge 2026'}]}, {'result': 'PASS', 'name': 'MATCHES_PYRAMID', 'effective': 2, 'multiplier': 1.1, 'pyramids': [{'name': 'USA/D1/PV', 'multiplier': 1.1}, {'name': 'USA/D1/MODEL', 'multiplier': 1.4}]}, {'result': 'WARNING', 'name': 'MATCHES_THEMES', 'themes': [{'id': 'M4ZYl3D', 'multiplier': 2.0, 'name': 'USA HighTurnover Datasets Theme'}]}, {'result': 'PENDING', 'name': 'MATCHES_THEMES', 'themes': [{'id': 'EDrKz34', 'multiplier': 1.0, 'name': 'All regions/D1 Power Pool Mar`26'}]}, {'name': 'OSMOSIS_ALLOCATION', 'result': 'WARNING'}]}",,,,,,,,,,
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@@ -0,0 +1,333 @@
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import httpx
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from httpx import BasicAuth, Timeout
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import pandas as pd
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from tqdm import tqdm
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from pathlib import Path
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import logging
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from typing import List, Optional
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import time
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from datetime import datetime, timedelta
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class AlphaManager:
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def __init__(self, credentials_file='account.txt', base_path=None):
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"""
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初始化Alpha管理器
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Args:
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credentials_file: 账号文件路径（备用）
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base_path: 输出文件保存路径
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"""
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self.client = None
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self.base_path = Path(base_path) if base_path else Path.cwd()
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self.logger = self._setup_logger()
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# 登录
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self.login(credentials_file)
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def _setup_logger(self):
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"""设置日志记录器"""
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logger = logging.getLogger(__name__)
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logger.setLevel(logging.INFO)
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if not logger.handlers:
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handler = logging.StreamHandler()
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formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
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handler.setFormatter(formatter)
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logger.addHandler(handler)
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return logger
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def login(self, credentials_file='account.txt'):
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"""登录WorldQuant Brain API"""
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try:
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# 从nacos获取账号密码
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with httpx.Client(timeout=10.0) as temp_client:
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nacos_resp = temp_client.get(
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'http://192.168.31.41:30848/nacos/v1/cs/configs?dataId=wq_account&group=quantify'
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)
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if nacos_resp.status_code != 200:
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self.logger.error('获取账号密码失败')
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return False
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config = nacos_resp.json()
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username = config.get('user_name')
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password = config.get('password')
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if not username or not password:
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self.logger.error('账号密码不完整')
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return False
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self.logger.info(f"正在登录账户: {username}")
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# 创建客户端并设置超时（关键修复）
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# 设置更长的超时时间：连接30秒，读取60秒
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timeout = Timeout(connect=30.0, read=60.0, write=30.0, pool=30.0)
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self.client = httpx.Client(
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auth=BasicAuth(username, password),
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timeout=timeout
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)
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# 发送登录请求
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response = self.client.post('https://api.worldquantbrain.com/authentication')
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if response.status_code == 201:
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self.logger.info("登录成功!")
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return True
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else:
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self.logger.error(f"登录失败: {response.status_code} - {response.text}")
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self.client.close()
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self.client = None
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return False
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except Exception as e:
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self.logger.error(f"登录异常: {e}")
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return False
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def update_alpha_color(self, alpha_id: str, color: str) -> bool:
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"""标记Alpha颜色"""
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if not self.client:
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self.logger.error("客户端未登录")
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return False
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try:
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update_data = {"color": color}
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response = self.client.patch(
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f"https://api.worldquantbrain.com/alphas/{alpha_id}",
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json=update_data
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)
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return response.status_code == 200
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except Exception as e:
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self.logger.error(f"标记颜色失败: {e}")
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return False
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def wechat_check_corr_message(self, message: str):
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"""微信通知（可选功能）"""
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self.logger.info(f"通知消息: {message}")
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def _make_request_with_retry(self, url: str, max_retries: int = 3, retry_delay: float = 2.0):
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"""
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带重试机制的请求
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Args:
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url: 请求URL
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max_retries: 最大重试次数
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retry_delay: 重试延迟（秒）
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Returns:
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httpx.Response 或 None
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"""
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for attempt in range(max_retries):
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try:
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response = self.client.get(url)
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return response
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except Exception as e:
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self.logger.warning(f"请求失败 (尝试 {attempt+1}/{max_retries}): {e}")
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if attempt < max_retries - 1:
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time.sleep(retry_delay)
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else:
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self.logger.error(f"请求最终失败: {url}")
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raise
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return None
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def get_alphas(
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self,
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total_alphas: int = 5,
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limit: int = 100,
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delay: int = 1,
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days_back: int = 7,
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required_fields: Optional[List[str]] = None,
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match_mode: str = "all",
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min_sharpe: Optional[float] = None,
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min_fitness: Optional[float] = None,
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hidden: str = "false",
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submittable: bool = False,
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auto_color: bool = False,
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color: str = "GREEN",
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output_file_name: str = "alpha_search_list.csv",
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mode: str = "w",
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max_retries: int = 3,
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):
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"""
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搜索Alpha并筛选（按时间范围，默认最近一周）
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Args:
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total_alphas: 最多获取多少个Alpha
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limit: 每次API请求获取的数量
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delay: API请求的延迟设置
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days_back: 查询多少天前的数据（默认7天，即一周）
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required_fields: 关键词列表，如 ['put', 'call']
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match_mode: 关键词匹配模式："all"(全匹配) 或 "any"(任一匹配)
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min_sharpe: 最小夏普比率阈值
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min_fitness: 最小适应度阈值
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hidden: 是否搜索已隐藏的Alpha ("true"/"false")
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submittable: 是否只筛选可提交（无FAIL检查项）的Alpha
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auto_color: 是否自动给符合条件的Alpha标记颜色
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color: 标记的颜色
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output_file_name: 输出的CSV文件名
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mode: 写入模式："w"(覆盖写入) 或 "a"(追加写入)
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max_retries: 请求失败时的最大重试次数
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Returns:
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list: 符合条件的Alpha列表
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"""
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if not self.client:
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self.logger.error("客户端未登录，无法执行搜索")
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return []
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# 验证颜色参数
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valid_colors = [None, "GREEN", "YELLOW", "RED", "BLUE", "PURPLE", "ORANGE"]
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if color not in valid_colors:
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raise ValueError(f"颜色必须是以下之一: {valid_colors}")
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# 计算时间范围（美国东部时间 UTC-5，夏令时 UTC-4）
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now = datetime.now()
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end_date = now
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start_date = now - timedelta(days=days_back)
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|
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# 格式化为 ISO 8601 格式（带时区偏移）
|
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# 使用 UTC-4（夏令时），如果是标准时间则改为 -05:00
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|
start_date_str = start_date.strftime("%Y-%m-%dT%H:%M:%S-04:00")
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|
end_date_str = end_date.strftime("%Y-%m-%dT%H:%M:%S-04:00")
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|
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|
self.logger.info(f"查询时间范围: {start_date_str} 到 {end_date_str}")
|
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|
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|
fetched_alphas = []
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|
offset = 0
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|
total_accessed = 0
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|
colored_count = 0
|
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|
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|
# 先获取总数（带重试）
|
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|
count_url = (
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|
f"https://api.worldquantbrain.com/users/self/alphas?stage=IS&hidden={hidden}"
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|
f"&limit=1&status=UNSUBMITTED%1FIS_FAIL"
|
||||||
|
f"&dateCreated%3E={start_date_str}&dateCreated%3C={end_date_str}"
|
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|
)
|
||||||
|
|
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|
total_available = 0
|
||||||
|
for attempt in range(max_retries):
|
||||||
|
try:
|
||||||
|
count_response = self.client.get(count_url)
|
||||||
|
total_available = count_response.json()["count"]
|
||||||
|
break
|
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|
except Exception as e:
|
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|
self.logger.warning(f"获取Alpha总数失败 (尝试 {attempt+1}/{max_retries}): {e}")
|
||||||
|
if attempt < max_retries - 1:
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|
time.sleep(2.0)
|
||||||
|
else:
|
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|
self.logger.error("获取Alpha总数最终失败")
|
||||||
|
return []
|
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|
|
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|
if total_available == 0:
|
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|
self.logger.warning("未找到任何Alpha")
|
||||||
|
return []
|
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|
|
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|
self.logger.info(f"共找到 {total_available} 个Alpha，开始筛选...")
|
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|
|
||||||
|
pbar = tqdm(total=min(total_available, 10000), desc="扫描Alpha", unit="条")
|
||||||
|
|
||||||
|
while len(fetched_alphas) < total_alphas and offset < total_available:
|
||||||
|
# 构建请求URL（使用时间范围，移除 region 和 universe）
|
||||||
|
url = (
|
||||||
|
f"https://api.worldquantbrain.com/users/self/alphas?stage=IS&limit={limit}"
|
||||||
|
f"&offset={offset}&hidden={hidden}&status=UNSUBMITTED%1FIS_FAIL"
|
||||||
|
f"&dateCreated%3E={start_date_str}&dateCreated%3C={end_date_str}&order=dateCreated"
|
||||||
|
)
|
||||||
|
|
||||||
|
try:
|
||||||
|
# 使用重试机制
|
||||||
|
response = None
|
||||||
|
for attempt in range(max_retries):
|
||||||
|
try:
|
||||||
|
response = self.client.get(url)
|
||||||
|
break
|
||||||
|
except Exception as e:
|
||||||
|
self.logger.warning(f"请求失败 (尝试 {attempt+1}/{max_retries}): {e}")
|
||||||
|
if attempt < max_retries - 1:
|
||||||
|
time.sleep(2.0)
|
||||||
|
else:
|
||||||
|
raise
|
||||||
|
|
||||||
|
if response.status_code == 400:
|
||||||
|
self.logger.warning(f"遇到API限制 (offset={offset})，停止获取更多数据")
|
||||||
|
break
|
||||||
|
|
||||||
|
response_data = response.json()
|
||||||
|
|
||||||
|
if not isinstance(response_data, dict) or "results" not in response_data:
|
||||||
|
self.logger.error(f"API返回了意外的数据: {response_data}")
|
||||||
|
break
|
||||||
|
|
||||||
|
alphas = response_data["results"]
|
||||||
|
|
||||||
|
if not alphas:
|
||||||
|
break
|
||||||
|
|
||||||
|
total_accessed += len(alphas)
|
||||||
|
|
||||||
|
# 直接保存所有获取到的Alpha，不做任何筛选
|
||||||
|
fetched_alphas.extend(alphas)
|
||||||
|
|
||||||
|
# 更新进度条
|
||||||
|
pbar.update(len(alphas))
|
||||||
|
pbar.set_postfix({
|
||||||
|
"时间范围": f"{days_back}天",
|
||||||
|
"已扫描": total_accessed,
|
||||||
|
"已获取": len(fetched_alphas),
|
||||||
|
})
|
||||||
|
|
||||||
|
if len(alphas) < limit:
|
||||||
|
break
|
||||||
|
|
||||||
|
offset += limit
|
||||||
|
|
||||||
|
# 添加延迟避免请求过快
|
||||||
|
time.sleep(0.5)
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
self.logger.error(f"请求失败: {e}")
|
||||||
|
break
|
||||||
|
|
||||||
|
pbar.close()
|
||||||
|
|
||||||
|
# 获取所有数据，不限制数量
|
||||||
|
alpha_list = fetched_alphas
|
||||||
|
|
||||||
|
if not alpha_list:
|
||||||
|
self.logger.warning("未获取到任何Alpha数据！")
|
||||||
|
else:
|
||||||
|
df = pd.DataFrame(alpha_list)
|
||||||
|
|
||||||
|
output_path = self.base_path / output_file_name
|
||||||
|
|
||||||
|
if mode == "w":
|
||||||
|
df.to_csv(output_path, index=False)
|
||||||
|
elif mode == "a":
|
||||||
|
df.to_csv(output_path, mode="a", index=False, header=False)
|
||||||
|
|
||||||
|
self.logger.info(f"一周内Alpha数据下载完成！共{len(alpha_list)}条记录！\n保存至: {output_path}")
|
||||||
|
|
||||||
|
return alpha_list
|
||||||
|
|
||||||
|
def close(self):
|
||||||
|
"""关闭客户端"""
|
||||||
|
if self.client:
|
||||||
|
self.client.close()
|
||||||
|
|
||||||
|
|
||||||
|
# 使用示例
|
||||||
|
if __name__ == "__main__":
|
||||||
|
# 创建管理器实例（会自动登录）
|
||||||
|
manager = AlphaManager()
|
||||||
|
|
||||||
|
if manager.client:
|
||||||
|
# 获取最近一周内的所有Alpha数据（不做任何筛选）
|
||||||
|
results = manager.get_alphas(
|
||||||
|
days_back=7, # 查询最近7天（一周）的数据
|
||||||
|
output_file_name="alpha_one_week_all.csv"
|
||||||
|
)
|
||||||
|
|
||||||
|
print(f"共获取 {len(results) if results else 0} 条Alpha数据")
|
||||||
|
|
||||||
|
# 关闭客户端
|
||||||
|
manager.close()
|
||||||
Reference in New Issue
Block a user