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381 lines
14 KiB
381 lines
14 KiB
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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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=None):
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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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region: str = "USA",
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universe: str = "TOP3000",
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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
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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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region: 市场区域，如 "USA", "CHINA"
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universe: 股票池，如 "TOP3000"
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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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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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# 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&settings.delay={delay}&settings.region={region}&status=UNSUBMITTED%1FIS_FAIL&settings.universe={universe}"
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)
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total_available = 0
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for attempt in range(max_retries):
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try:
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count_response = self.client.get(count_url)
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total_available = count_response.json()["count"]
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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}")
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if attempt < max_retries - 1:
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time.sleep(2.0)
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else:
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self.logger.error("获取Alpha总数最终失败")
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return []
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if total_available == 0:
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self.logger.warning("未找到任何Alpha")
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return []
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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")
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while len(fetched_alphas) < total_alphas and offset < total_available:
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# 构建请求URL
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url = (
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f"https://api.worldquantbrain.com/users/self/alphas?stage=IS&limit={limit}"
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f"&offset={offset}&settings.delay={delay}&settings.region={region}&hidden={hidden}&status=UNSUBMITTED%1FIS_PASS&settings.universe={universe}"
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)
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try:
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# 使用重试机制
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response = None
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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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break
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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(2.0)
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else:
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raise
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if response.status_code == 400:
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self.logger.warning(f"遇到API限制 (offset={offset})，停止获取更多数据")
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break
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response_data = response.json()
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if not isinstance(response_data, dict) or "results" not in response_data:
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self.logger.error(f"API返回了意外的数据: {response_data}")
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break
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alphas = response_data["results"]
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if not alphas:
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break
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total_accessed += len(alphas)
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# 关键词筛选
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if required_fields:
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if match_mode == "all":
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filtered_alphas = [
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alpha for alpha in alphas
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if all(field in alpha["regular"]["code"] for field in required_fields)
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]
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elif match_mode == "any":
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filtered_alphas = [
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alpha for alpha in alphas
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if any(field in alpha["regular"]["code"] for field in required_fields)
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]
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else:
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raise ValueError("match_mode 必须是 'all' 或 'any'")
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else:
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filtered_alphas = alphas
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# 进一步筛选夏普比率和fitness
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final_filtered = []
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for alpha in filtered_alphas:
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sharpe = alpha.get("is", {}).get("sharpe", 0)
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fitness = alpha.get("is", {}).get("fitness", 0)
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sharpe_ok = (min_sharpe is None) or (sharpe is not None and abs(sharpe) >= min_sharpe)
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fitness_ok = (min_fitness is None) or (fitness is not None and abs(fitness) >= min_fitness)
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if sharpe_ok and fitness_ok:
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if submittable:
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checks = alpha.get("is", {}).get("checks", [])
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fail_count = sum(1 for check in checks if check.get("result") == "FAIL")
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if fail_count == 0 and auto_color:
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alpha_id = alpha.get("id")
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if alpha_id:
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success = self.update_alpha_color(alpha_id, color)
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if success:
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colored_count += 1
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if fail_count == 0:
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final_filtered.append(alpha)
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else:
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final_filtered.append(alpha)
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fetched_alphas.extend(final_filtered)
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# 更新进度条
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pbar.update(len(alphas))
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pbar.set_postfix({
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"Region": region,
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"已扫描": total_accessed,
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"找到": len(fetched_alphas),
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"本次": len(final_filtered),
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"标记": colored_count,
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})
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if len(alphas) < limit:
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break
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offset += limit
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# 添加延迟避免请求过快
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time.sleep(0.5)
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except Exception as e:
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self.logger.error(f"请求失败: {e}")
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break
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pbar.close()
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# 输出标记统计
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if auto_color and colored_count > 0:
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self.logger.info(f"共标记了 {colored_count} 个Alpha颜色为{color}")
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self.wechat_check_corr_message(f"共标记了 {colored_count} 个Alpha颜色为{color}")
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alpha_list = fetched_alphas[:total_alphas]
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if not alpha_list:
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self.logger.warning("未找到任何符合条件的Alpha！请检查筛选条件是否过于严格。")
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else:
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df = pd.DataFrame(alpha_list)
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df["temp_fitness"] = df.apply(
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lambda row: row["is"].get("fitness", 0) if isinstance(row.get("is"), dict) else 0,
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axis=1
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)
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df_sorted = df.sort_values(by="temp_fitness", ascending=False)
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df_sorted = df_sorted.drop("temp_fitness", axis=1)
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output_path = self.base_path / output_file_name
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if mode == "w":
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df_sorted.to_csv(output_path, index=False)
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elif mode == "a":
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df_sorted.to_csv(output_path, mode="a", index=False, header=False)
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self.logger.info(f"批量回测初步检测结果已经下载！共{len(alpha_list)}条记录！\n{output_path}文件名保存！")
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return alpha_list
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def close(self):
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"""关闭客户端"""
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if self.client:
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self.client.close()
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# 使用示例
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if __name__ == "__main__":
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# 创建管理器实例（会自动登录）
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manager = AlphaManager()
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if manager.client:
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# 搜索包含"put"关键词的可提交Alpha
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results = manager.get_alphas(
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total_alphas=10,
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region="USA",
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universe="TOP3000",
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required_fields=['put'],
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match_mode="all",
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submittable=True,
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auto_color=True,
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color="GREEN",
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output_file_name="my_alpha_list.csv"
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)
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print(f"找到 {len(results) if results else 0} 个符合条件的Alpha")
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# 关闭客户端
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manager.close() |