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# -*- coding: utf-8 -*-
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"""
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===================================
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AkshareFetcher - 主数据源 (Priority 1)
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===================================
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数据来源：
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1. 东方财富爬虫（通过 akshare 库） - 默认数据源
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2. 新浪财经接口 - 备选数据源
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3. 腾讯财经接口 - 备选数据源
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特点：免费、无需 Token、数据全面
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风险：爬虫机制易被反爬封禁
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防封禁策略：
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1. 每次请求前随机休眠 2-5 秒
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2. 随机轮换 User-Agent
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3. 使用 tenacity 实现指数退避重试
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4. 熔断器机制：连续失败后自动冷却
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增强数据：
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- 实时行情：量比、换手率、市盈率、市净率、总市值、流通市值
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- 筹码分布：获利比例、平均成本、筹码集中度
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"""
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import logging
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import os
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import random
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import time
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from dataclasses import dataclass, field
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from datetime import datetime
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from typing import Optional, Dict, Any, List, Tuple
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import pandas as pd
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from tenacity import (
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retry,
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stop_after_attempt,
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wait_exponential,
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retry_if_exception_type,
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before_sleep_log,
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)
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from .base import BaseFetcher, DataFetchError, RateLimitError, STANDARD_COLUMNS
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from .realtime_types import (
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UnifiedRealtimeQuote, ChipDistribution, RealtimeSource,
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get_realtime_circuit_breaker, get_chip_circuit_breaker,
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safe_float, safe_int # 使用统一的类型转换函数
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)
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# 保留旧的 RealtimeQuote 别名，用于向后兼容
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RealtimeQuote = UnifiedRealtimeQuote
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logger = logging.getLogger(__name__)
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# User-Agent 池，用于随机轮换
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USER_AGENTS = [
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'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36',
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'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36',
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'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:121.0) Gecko/20100101 Firefox/121.0',
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'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/17.2 Safari/605.1.15',
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'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36',
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]
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# 缓存实时行情数据（避免重复请求）
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# TTL 设为 20 分钟 (1200秒)：
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# - 批量分析场景：通常 30 只股票在 5 分钟内分析完，20 分钟足够覆盖
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# - 实时性要求：股票分析不需要秒级实时数据，20 分钟延迟可接受
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# - 防封禁：减少 API 调用频率
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_realtime_cache: Dict[str, Any] = {
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'data': None,
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'timestamp': 0,
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'ttl': 1200 # 20分钟缓存有效期
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}
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# ETF 实时行情缓存
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_etf_realtime_cache: Dict[str, Any] = {
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'data': None,
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'timestamp': 0,
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'ttl': 1200 # 20分钟缓存有效期
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}
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def _is_etf_code(stock_code: str) -> bool:
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"""
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判断代码是否为 ETF 基金
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ETF 代码规则：
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- 上交所 ETF: 51xxxx, 52xxxx, 56xxxx, 58xxxx
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- 深交所 ETF: 15xxxx, 16xxxx, 18xxxx
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Args:
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stock_code: 股票/基金代码
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Returns:
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True 表示是 ETF 代码，False 表示是普通股票代码
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"""
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etf_prefixes = ('51', '52', '56', '58', '15', '16', '18')
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code = stock_code.strip().split('.')[0]
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return code.startswith(etf_prefixes) and len(code) == 6
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def _is_hk_code(stock_code: str) -> bool:
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"""
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判断代码是否为港股
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港股代码规则：
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- 5位数字代码，如 '00700' (腾讯控股)
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- 部分港股代码可能带有前缀，如 'hk00700', 'hk1810'
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Args:
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stock_code: 股票代码
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Returns:
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True 表示是港股代码，False 表示不是港股代码
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"""
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# 去除可能的 'hk' 前缀并检查是否为纯数字
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code = stock_code.lower()
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if code.startswith('hk'):
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# 带 hk 前缀的一定是港股，去掉前缀后应为纯数字（1-5位）
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numeric_part = code[2:]
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return numeric_part.isdigit() and 1 <= len(numeric_part) <= 5
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# 无前缀时，5位纯数字才视为港股（避免误判 A 股代码）
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return code.isdigit() and len(code) == 5
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def _is_us_code(stock_code: str) -> bool:
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"""
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判断代码是否为美股
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美股代码规则：
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- 1-5个大写字母，如 'AAPL' (苹果), 'TSLA' (特斯拉)
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- 可能包含 '.' 用于特殊股票类别，如 'BRK.B' (伯克希尔B类股)
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Args:
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stock_code: 股票代码
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Returns:
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True 表示是美股代码，False 表示不是美股代码
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Examples:
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>>> _is_us_code('AAPL')
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True
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>>> _is_us_code('TSLA')
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True
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>>> _is_us_code('BRK.B')
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True
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>>> _is_us_code('600519')
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False
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>>> _is_us_code('hk00700')
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False
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"""
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import re
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code = stock_code.strip().upper()
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# 美股：1-5个大写字母，可能包含一个点和字母（如 BRK.B）
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return bool(re.match(r'^[A-Z]{1,5}(\.[A-Z])?$', code))
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class AkshareFetcher(BaseFetcher):
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"""
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Akshare 数据源实现
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优先级：1（最高）
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数据来源：东方财富网爬虫
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关键策略：
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- 每次请求前随机休眠 2.0-5.0 秒
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- 随机 User-Agent 轮换
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- 失败后指数退避重试（最多3次）
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"""
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name = "AkshareFetcher"
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priority = int(os.getenv("AKSHARE_PRIORITY", "1"))
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def __init__(self, sleep_min: float = 2.0, sleep_max: float = 5.0):
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"""
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初始化 AkshareFetcher
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Args:
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sleep_min: 最小休眠时间（秒）
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sleep_max: 最大休眠时间（秒）
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"""
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self.sleep_min = sleep_min
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self.sleep_max = sleep_max
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self._last_request_time: Optional[float] = None
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def _set_random_user_agent(self) -> None:
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"""
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设置随机 User-Agent
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通过修改 requests Session 的 headers 实现
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这是关键的反爬策略之一
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"""
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try:
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import akshare as ak
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# akshare 内部使用 requests，我们通过环境变量或直接设置来影响
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# 实际上 akshare 可能不直接暴露 session，这里通过 fake_useragent 作为补充
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random_ua = random.choice(USER_AGENTS)
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logger.debug(f"设置 User-Agent: {random_ua[:50]}...")
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except Exception as e:
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logger.debug(f"设置 User-Agent 失败: {e}")
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def _enforce_rate_limit(self) -> None:
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"""
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强制执行速率限制
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策略：
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1. 检查距离上次请求的时间间隔
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2. 如果间隔不足，补充休眠时间
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3. 然后再执行随机 jitter 休眠
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"""
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if self._last_request_time is not None:
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elapsed = time.time() - self._last_request_time
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min_interval = self.sleep_min
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if elapsed < min_interval:
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additional_sleep = min_interval - elapsed
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logger.debug(f"补充休眠 {additional_sleep:.2f} 秒")
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time.sleep(additional_sleep)
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# 执行随机 jitter 休眠
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self.random_sleep(self.sleep_min, self.sleep_max)
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self._last_request_time = time.time()
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@retry(
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stop=stop_after_attempt(3), # 最多重试3次
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wait=wait_exponential(multiplier=1, min=2, max=30), # 指数退避：2, 4, 8... 最大30秒
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retry=retry_if_exception_type((ConnectionError, TimeoutError)),
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before_sleep=before_sleep_log(logger, logging.WARNING),
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)
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def _fetch_raw_data(self, stock_code: str, start_date: str, end_date: str) -> pd.DataFrame:
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"""
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从 Akshare 获取原始数据
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根据代码类型自动选择 API：
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- 美股：使用 ak.stock_us_daily()
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- 港股：使用 ak.stock_hk_hist()
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- ETF 基金：使用 ak.fund_etf_hist_em()
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- 普通 A 股：使用 ak.stock_zh_a_hist()
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流程：
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1. 判断代码类型（美股/港股/ETF/A股）
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2. 设置随机 User-Agent
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3. 执行速率限制（随机休眠）
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4. 调用对应的 akshare API
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5. 处理返回数据
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"""
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# 根据代码类型选择不同的获取方法
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if _is_us_code(stock_code):
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return self._fetch_us_data(stock_code, start_date, end_date)
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elif _is_hk_code(stock_code):
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return self._fetch_hk_data(stock_code, start_date, end_date)
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elif _is_etf_code(stock_code):
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return self._fetch_etf_data(stock_code, start_date, end_date)
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else:
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return self._fetch_stock_data(stock_code, start_date, end_date)
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def _fetch_stock_data(self, stock_code: str, start_date: str, end_date: str) -> pd.DataFrame:
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"""
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获取普通 A 股历史数据
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策略：
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1. 优先尝试东方财富接口 (ak.stock_zh_a_hist)
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2. 失败后尝试新浪财经接口 (ak.stock_zh_a_daily)
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3. 最后尝试腾讯财经接口 (ak.stock_zh_a_hist_tx)
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"""
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# 尝试列表
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methods = [
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(self._fetch_stock_data_em, "东方财富"),
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(self._fetch_stock_data_sina, "新浪财经"),
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(self._fetch_stock_data_tx, "腾讯财经"),
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]
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last_error = None
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for fetch_method, source_name in methods:
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try:
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logger.info(f"[数据源] 尝试使用 {source_name} 获取 {stock_code}...")
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df = fetch_method(stock_code, start_date, end_date)
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if df is not None and not df.empty:
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logger.info(f"[数据源] {source_name} 获取成功")
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return df
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except Exception as e:
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last_error = e
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logger.warning(f"[数据源] {source_name} 获取失败: {e}")
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# 继续尝试下一个
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# 所有都失败
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raise DataFetchError(f"Akshare 所有渠道获取失败: {last_error}")
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def _fetch_stock_data_em(self, stock_code: str, start_date: str, end_date: str) -> pd.DataFrame:
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"""
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获取普通 A 股历史数据 (东方财富)
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数据来源：ak.stock_zh_a_hist()
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"""
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import akshare as ak
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# 防封禁策略 1: 随机 User-Agent
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self._set_random_user_agent()
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# 防封禁策略 2: 强制休眠
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self._enforce_rate_limit()
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logger.info(f"[API调用] ak.stock_zh_a_hist(symbol={stock_code}, ...)")
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try:
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import time as _time
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api_start = _time.time()
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df = ak.stock_zh_a_hist(
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symbol=stock_code,
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period="daily",
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start_date=start_date.replace('-', ''),
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end_date=end_date.replace('-', ''),
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adjust="qfq"
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)
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api_elapsed = _time.time() - api_start
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if df is not None and not df.empty:
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logger.info(f"[API返回] ak.stock_zh_a_hist 成功: {len(df)} 行, 耗时 {api_elapsed:.2f}s")
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return df
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else:
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logger.warning(f"[API返回] ak.stock_zh_a_hist 返回空数据")
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return pd.DataFrame()
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except Exception as e:
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error_msg = str(e).lower()
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if any(keyword in error_msg for keyword in ['banned', 'blocked', '频率', 'rate', '限制']):
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raise RateLimitError(f"Akshare(EM) 可能被限流: {e}") from e
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raise e
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def _fetch_stock_data_sina(self, stock_code: str, start_date: str, end_date: str) -> pd.DataFrame:
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"""
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获取普通 A 股历史数据 (新浪财经)
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数据来源：ak.stock_zh_a_daily()
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"""
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import akshare as ak
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# 转换代码格式：sh600000, sz000001
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if stock_code.startswith(('6', '5', '9')):
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symbol = f"sh{stock_code}"
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else:
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symbol = f"sz{stock_code}"
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self._enforce_rate_limit()
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try:
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df = ak.stock_zh_a_daily(
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symbol=symbol,
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start_date=start_date.replace('-', ''),
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end_date=end_date.replace('-', ''),
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adjust="qfq"
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)
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# 标准化新浪数据列名
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# 新浪返回：date, open, high, low, close, volume, amount, outstanding_share, turnover
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if df is not None and not df.empty:
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# 确保日期列存在
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if 'date' in df.columns:
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df = df.rename(columns={'date': '日期'})
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# 映射其他列以匹配 _normalize_data 的期望
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# _normalize_data 期望：日期, 开盘, 收盘, 最高, 最低, 成交量, 成交额
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rename_map = {
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'open': '开盘', 'high': '最高', 'low': '最低',
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'close': '收盘', 'volume': '成交量', 'amount': '成交额'
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}
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df = df.rename(columns=rename_map)
|
|
|
|
|
|
# 计算涨跌幅（新浪接口可能不返回）
|
|
|
if '收盘' in df.columns:
|
|
|
df['涨跌幅'] = df['收盘'].pct_change() * 100
|
|
|
df['涨跌幅'] = df['涨跌幅'].fillna(0)
|
|
|
|
|
|
return df
|
|
|
return pd.DataFrame()
|
|
|
|
|
|
except Exception as e:
|
|
|
raise e
|
|
|
|
|
|
def _fetch_stock_data_tx(self, stock_code: str, start_date: str, end_date: str) -> pd.DataFrame:
|
|
|
"""
|
|
|
获取普通 A 股历史数据 (腾讯财经)
|
|
|
数据来源：ak.stock_zh_a_hist_tx()
|
|
|
"""
|
|
|
import akshare as ak
|
|
|
|
|
|
# 转换代码格式：sh600000, sz000001
|
|
|
if stock_code.startswith(('6', '5', '9')):
|
|
|
symbol = f"sh{stock_code}"
|
|
|
else:
|
|
|
symbol = f"sz{stock_code}"
|
|
|
|
|
|
self._enforce_rate_limit()
|
|
|
|
|
|
try:
|
|
|
df = ak.stock_zh_a_hist_tx(
|
|
|
symbol=symbol,
|
|
|
start_date=start_date.replace('-', ''),
|
|
|
end_date=end_date.replace('-', ''),
|
|
|
adjust="qfq"
|
|
|
)
|
|
|
|
|
|
# 标准化腾讯数据列名
|
|
|
# 腾讯返回：date, open, close, high, low, volume, amount
|
|
|
if df is not None and not df.empty:
|
|
|
rename_map = {
|
|
|
'date': '日期', 'open': '开盘', 'high': '最高',
|
|
|
'low': '最低', 'close': '收盘', 'volume': '成交量',
|
|
|
'amount': '成交额'
|
|
|
}
|
|
|
df = df.rename(columns=rename_map)
|
|
|
|
|
|
# 腾讯数据通常包含 '涨跌幅'，如果没有则计算
|
|
|
if 'pct_chg' in df.columns:
|
|
|
df = df.rename(columns={'pct_chg': '涨跌幅'})
|
|
|
elif '收盘' in df.columns:
|
|
|
df['涨跌幅'] = df['收盘'].pct_change() * 100
|
|
|
df['涨跌幅'] = df['涨跌幅'].fillna(0)
|
|
|
|
|
|
return df
|
|
|
return pd.DataFrame()
|
|
|
|
|
|
except Exception as e:
|
|
|
raise e
|
|
|
|
|
|
def _fetch_etf_data(self, stock_code: str, start_date: str, end_date: str) -> pd.DataFrame:
|
|
|
"""
|
|
|
获取 ETF 基金历史数据
|
|
|
|
|
|
数据来源：ak.fund_etf_hist_em()
|
|
|
|
|
|
Args:
|
|
|
stock_code: ETF 代码，如 '512400', '159883'
|
|
|
start_date: 开始日期，格式 'YYYY-MM-DD'
|
|
|
end_date: 结束日期，格式 'YYYY-MM-DD'
|
|
|
|
|
|
Returns:
|
|
|
ETF 历史数据 DataFrame
|
|
|
"""
|
|
|
import akshare as ak
|
|
|
|
|
|
# 防封禁策略 1: 随机 User-Agent
|
|
|
self._set_random_user_agent()
|
|
|
|
|
|
# 防封禁策略 2: 强制休眠
|
|
|
self._enforce_rate_limit()
|
|
|
|
|
|
logger.info(f"[API调用] ak.fund_etf_hist_em(symbol={stock_code}, period=daily, "
|
|
|
f"start_date={start_date.replace('-', '')}, end_date={end_date.replace('-', '')}, adjust=qfq)")
|
|
|
|
|
|
try:
|
|
|
import time as _time
|
|
|
api_start = _time.time()
|
|
|
|
|
|
# 调用 akshare 获取 ETF 日线数据
|
|
|
df = ak.fund_etf_hist_em(
|
|
|
symbol=stock_code,
|
|
|
period="daily",
|
|
|
start_date=start_date.replace('-', ''),
|
|
|
end_date=end_date.replace('-', ''),
|
|
|
adjust="qfq" # 前复权
|
|
|
)
|
|
|
|
|
|
api_elapsed = _time.time() - api_start
|
|
|
|
|
|
# 记录返回数据摘要
|
|
|
if df is not None and not df.empty:
|
|
|
logger.info(f"[API返回] ak.fund_etf_hist_em 成功: 返回 {len(df)} 行数据, 耗时 {api_elapsed:.2f}s")
|
|
|
logger.info(f"[API返回] 列名: {list(df.columns)}")
|
|
|
logger.info(f"[API返回] 日期范围: {df['日期'].iloc[0]} ~ {df['日期'].iloc[-1]}")
|
|
|
logger.debug(f"[API返回] 最新3条数据:\n{df.tail(3).to_string()}")
|
|
|
else:
|
|
|
logger.warning(f"[API返回] ak.fund_etf_hist_em 返回空数据, 耗时 {api_elapsed:.2f}s")
|
|
|
|
|
|
return df
|
|
|
|
|
|
except Exception as e:
|
|
|
error_msg = str(e).lower()
|
|
|
|
|
|
# 检测反爬封禁
|
|
|
if any(keyword in error_msg for keyword in ['banned', 'blocked', '频率', 'rate', '限制']):
|
|
|
logger.warning(f"检测到可能被封禁: {e}")
|
|
|
raise RateLimitError(f"Akshare 可能被限流: {e}") from e
|
|
|
|
|
|
raise DataFetchError(f"Akshare 获取 ETF 数据失败: {e}") from e
|
|
|
|
|
|
def _fetch_us_data(self, stock_code: str, start_date: str, end_date: str) -> pd.DataFrame:
|
|
|
"""
|
|
|
获取美股历史数据
|
|
|
|
|
|
数据来源：ak.stock_us_daily()（新浪财经接口）
|
|
|
|
|
|
Args:
|
|
|
stock_code: 美股代码，如 'AMD', 'AAPL', 'TSLA'
|
|
|
start_date: 开始日期，格式 'YYYY-MM-DD'
|
|
|
end_date: 结束日期，格式 'YYYY-MM-DD'
|
|
|
|
|
|
Returns:
|
|
|
美股历史数据 DataFrame
|
|
|
"""
|
|
|
import akshare as ak
|
|
|
|
|
|
# 防封禁策略 1: 随机 User-Agent
|
|
|
self._set_random_user_agent()
|
|
|
|
|
|
# 防封禁策略 2: 强制休眠
|
|
|
self._enforce_rate_limit()
|
|
|
|
|
|
# 美股代码直接使用大写
|
|
|
symbol = stock_code.strip().upper()
|
|
|
|
|
|
logger.info(f"[API调用] ak.stock_us_daily(symbol={symbol}, adjust=qfq)")
|
|
|
|
|
|
try:
|
|
|
import time as _time
|
|
|
api_start = _time.time()
|
|
|
|
|
|
# 调用 akshare 获取美股日线数据
|
|
|
# stock_us_daily 返回全部历史数据，后续需要按日期过滤
|
|
|
df = ak.stock_us_daily(
|
|
|
symbol=symbol,
|
|
|
adjust="qfq" # 前复权
|
|
|
)
|
|
|
|
|
|
api_elapsed = _time.time() - api_start
|
|
|
|
|
|
# 记录返回数据摘要
|
|
|
if df is not None and not df.empty:
|
|
|
logger.info(f"[API返回] ak.stock_us_daily 成功: 返回 {len(df)} 行数据, 耗时 {api_elapsed:.2f}s")
|
|
|
logger.info(f"[API返回] 列名: {list(df.columns)}")
|
|
|
|
|
|
# 按日期过滤
|
|
|
df['date'] = pd.to_datetime(df['date'])
|
|
|
start_dt = pd.to_datetime(start_date)
|
|
|
end_dt = pd.to_datetime(end_date)
|
|
|
df = df[(df['date'] >= start_dt) & (df['date'] <= end_dt)]
|
|
|
|
|
|
if not df.empty:
|
|
|
logger.info(f"[API返回] 过滤后日期范围: {df['date'].iloc[0].strftime('%Y-%m-%d')} ~ {df['date'].iloc[-1].strftime('%Y-%m-%d')}")
|
|
|
logger.debug(f"[API返回] 最新3条数据:\n{df.tail(3).to_string()}")
|
|
|
else:
|
|
|
logger.warning(f"[API返回] 过滤后数据为空，日期范围 {start_date} ~ {end_date} 无数据")
|
|
|
|
|
|
# 转换列名为中文格式以匹配 _normalize_data
|
|
|
# stock_us_daily 返回: date, open, high, low, close, volume
|
|
|
rename_map = {
|
|
|
'date': '日期',
|
|
|
'open': '开盘',
|
|
|
'high': '最高',
|
|
|
'low': '最低',
|
|
|
'close': '收盘',
|
|
|
'volume': '成交量',
|
|
|
}
|
|
|
df = df.rename(columns=rename_map)
|
|
|
|
|
|
# 计算涨跌幅（美股接口不直接返回）
|
|
|
if '收盘' in df.columns:
|
|
|
df['涨跌幅'] = df['收盘'].pct_change() * 100
|
|
|
df['涨跌幅'] = df['涨跌幅'].fillna(0)
|
|
|
|
|
|
# 估算成交额（美股接口不返回）
|
|
|
if '成交量' in df.columns and '收盘' in df.columns:
|
|
|
df['成交额'] = df['成交量'] * df['收盘']
|
|
|
else:
|
|
|
df['成交额'] = 0
|
|
|
|
|
|
return df
|
|
|
else:
|
|
|
logger.warning(f"[API返回] ak.stock_us_daily 返回空数据, 耗时 {api_elapsed:.2f}s")
|
|
|
return pd.DataFrame()
|
|
|
|
|
|
except Exception as e:
|
|
|
error_msg = str(e).lower()
|
|
|
|
|
|
# 检测反爬封禁
|
|
|
if any(keyword in error_msg for keyword in ['banned', 'blocked', '频率', 'rate', '限制']):
|
|
|
logger.warning(f"检测到可能被封禁: {e}")
|
|
|
raise RateLimitError(f"Akshare 可能被限流: {e}") from e
|
|
|
|
|
|
raise DataFetchError(f"Akshare 获取美股数据失败: {e}") from e
|
|
|
|
|
|
def _fetch_hk_data(self, stock_code: str, start_date: str, end_date: str) -> pd.DataFrame:
|
|
|
"""
|
|
|
获取港股历史数据
|
|
|
|
|
|
数据来源：ak.stock_hk_hist()
|
|
|
|
|
|
Args:
|
|
|
stock_code: 港股代码，如 '00700', '01810'
|
|
|
start_date: 开始日期，格式 'YYYY-MM-DD'
|
|
|
end_date: 结束日期，格式 'YYYY-MM-DD'
|
|
|
|
|
|
Returns:
|
|
|
港股历史数据 DataFrame
|
|
|
"""
|
|
|
import akshare as ak
|
|
|
|
|
|
# 防封禁策略 1: 随机 User-Agent
|
|
|
self._set_random_user_agent()
|
|
|
|
|
|
# 防封禁策略 2: 强制休眠
|
|
|
self._enforce_rate_limit()
|
|
|
|
|
|
# 确保代码格式正确（5位数字）
|
|
|
code = stock_code.lower().replace('hk', '').zfill(5)
|
|
|
|
|
|
logger.info(f"[API调用] ak.stock_hk_hist(symbol={code}, period=daily, "
|
|
|
f"start_date={start_date.replace('-', '')}, end_date={end_date.replace('-', '')}, adjust=qfq)")
|
|
|
|
|
|
try:
|
|
|
import time as _time
|
|
|
api_start = _time.time()
|
|
|
|
|
|
# 调用 akshare 获取港股日线数据
|
|
|
df = ak.stock_hk_hist(
|
|
|
symbol=code,
|
|
|
period="daily",
|
|
|
start_date=start_date.replace('-', ''),
|
|
|
end_date=end_date.replace('-', ''),
|
|
|
adjust="qfq" # 前复权
|
|
|
)
|
|
|
|
|
|
api_elapsed = _time.time() - api_start
|
|
|
|
|
|
# 记录返回数据摘要
|
|
|
if df is not None and not df.empty:
|
|
|
logger.info(f"[API返回] ak.stock_hk_hist 成功: 返回 {len(df)} 行数据, 耗时 {api_elapsed:.2f}s")
|
|
|
logger.info(f"[API返回] 列名: {list(df.columns)}")
|
|
|
logger.info(f"[API返回] 日期范围: {df['日期'].iloc[0]} ~ {df['日期'].iloc[-1]}")
|
|
|
logger.debug(f"[API返回] 最新3条数据:\n{df.tail(3).to_string()}")
|
|
|
else:
|
|
|
logger.warning(f"[API返回] ak.stock_hk_hist 返回空数据, 耗时 {api_elapsed:.2f}s")
|
|
|
|
|
|
return df
|
|
|
|
|
|
except Exception as e:
|
|
|
error_msg = str(e).lower()
|
|
|
|
|
|
# 检测反爬封禁
|
|
|
if any(keyword in error_msg for keyword in ['banned', 'blocked', '频率', 'rate', '限制']):
|
|
|
logger.warning(f"检测到可能被封禁: {e}")
|
|
|
raise RateLimitError(f"Akshare 可能被限流: {e}") from e
|
|
|
|
|
|
raise DataFetchError(f"Akshare 获取港股数据失败: {e}") from e
|
|
|
|
|
|
def _normalize_data(self, df: pd.DataFrame, stock_code: str) -> pd.DataFrame:
|
|
|
"""
|
|
|
标准化 Akshare 数据
|
|
|
|
|
|
Akshare 返回的列名（中文）：
|
|
|
日期, 开盘, 收盘, 最高, 最低, 成交量, 成交额, 振幅, 涨跌幅, 涨跌额, 换手率
|
|
|
|
|
|
需要映射到标准列名：
|
|
|
date, open, high, low, close, volume, amount, pct_chg
|
|
|
"""
|
|
|
df = df.copy()
|
|
|
|
|
|
# 列名映射（Akshare 中文列名 -> 标准英文列名）
|
|
|
column_mapping = {
|
|
|
'日期': 'date',
|
|
|
'开盘': 'open',
|
|
|
'收盘': 'close',
|
|
|
'最高': 'high',
|
|
|
'最低': 'low',
|
|
|
'成交量': 'volume',
|
|
|
'成交额': 'amount',
|
|
|
'涨跌幅': 'pct_chg',
|
|
|
}
|
|
|
|
|
|
# 重命名列
|
|
|
df = df.rename(columns=column_mapping)
|
|
|
|
|
|
# 添加股票代码列
|
|
|
df['code'] = stock_code
|
|
|
|
|
|
# 只保留需要的列
|
|
|
keep_cols = ['code'] + STANDARD_COLUMNS
|
|
|
existing_cols = [col for col in keep_cols if col in df.columns]
|
|
|
df = df[existing_cols]
|
|
|
|
|
|
return df
|
|
|
|
|
|
def get_realtime_quote(self, stock_code: str, source: str = "em") -> Optional[UnifiedRealtimeQuote]:
|
|
|
"""
|
|
|
获取实时行情数据（支持多数据源）
|
|
|
|
|
|
数据源优先级（可配置）：
|
|
|
1. em: 东方财富（akshare ak.stock_zh_a_spot_em）- 数据最全，含量比/PE/PB/市值等
|
|
|
2. sina: 新浪财经（akshare ak.stock_zh_a_spot）- 轻量级，基本行情
|
|
|
3. tencent: 腾讯直连接口 - 单股票查询，负载小
|
|
|
|
|
|
Args:
|
|
|
stock_code: 股票/ETF代码
|
|
|
source: 数据源类型，可选 "em", "sina", "tencent"
|
|
|
|
|
|
Returns:
|
|
|
UnifiedRealtimeQuote 对象，获取失败返回 None
|
|
|
"""
|
|
|
# 检查熔断器状态
|
|
|
circuit_breaker = get_realtime_circuit_breaker()
|
|
|
source_key = f"akshare_{source}"
|
|
|
|
|
|
if not circuit_breaker.is_available(source_key):
|
|
|
logger.warning(f"[熔断] 数据源 {source_key} 处于熔断状态，跳过")
|
|
|
return None
|
|
|
|
|
|
# 根据代码类型选择不同的获取方法
|
|
|
if _is_us_code(stock_code):
|
|
|
# 美股不使用 Akshare，由 YfinanceFetcher 处理
|
|
|
logger.debug(f"[API跳过] {stock_code} 是美股，Akshare 不支持美股实时行情")
|
|
|
return None
|
|
|
elif _is_hk_code(stock_code):
|
|
|
return self._get_hk_realtime_quote(stock_code)
|
|
|
elif _is_etf_code(stock_code):
|
|
|
return self._get_etf_realtime_quote(stock_code)
|
|
|
else:
|
|
|
# 普通 A 股：根据 source 选择数据源
|
|
|
if source == "sina":
|
|
|
return self._get_stock_realtime_quote_sina(stock_code)
|
|
|
elif source == "tencent":
|
|
|
return self._get_stock_realtime_quote_tencent(stock_code)
|
|
|
else:
|
|
|
return self._get_stock_realtime_quote_em(stock_code)
|
|
|
|
|
|
def _get_stock_realtime_quote_em(self, stock_code: str) -> Optional[UnifiedRealtimeQuote]:
|
|
|
"""
|
|
|
获取普通 A 股实时行情数据（东方财富数据源）
|
|
|
|
|
|
数据来源：ak.stock_zh_a_spot_em()
|
|
|
优点：数据最全，含量比、换手率、市盈率、市净率、总市值、流通市值等
|
|
|
缺点：全量拉取，数据量大，容易超时/限流
|
|
|
"""
|
|
|
import akshare as ak
|
|
|
circuit_breaker = get_realtime_circuit_breaker()
|
|
|
source_key = "akshare_em"
|
|
|
|
|
|
try:
|
|
|
# 检查缓存
|
|
|
current_time = time.time()
|
|
|
if (_realtime_cache['data'] is not None and
|
|
|
current_time - _realtime_cache['timestamp'] < _realtime_cache['ttl']):
|
|
|
df = _realtime_cache['data']
|
|
|
cache_age = int(current_time - _realtime_cache['timestamp'])
|
|
|
logger.debug(f"[缓存命中] A股实时行情(东财) - 缓存年龄 {cache_age}s/{_realtime_cache['ttl']}s")
|
|
|
else:
|
|
|
# 触发全量刷新
|
|
|
logger.info(f"[缓存未命中] 触发全量刷新 A股实时行情(东财)")
|
|
|
last_error: Optional[Exception] = None
|
|
|
df = None
|
|
|
for attempt in range(1, 3):
|
|
|
try:
|
|
|
# 防封禁策略
|
|
|
self._set_random_user_agent()
|
|
|
self._enforce_rate_limit()
|
|
|
|
|
|
logger.info(f"[API调用] ak.stock_zh_a_spot_em() 获取A股实时行情... (attempt {attempt}/2)")
|
|
|
import time as _time
|
|
|
api_start = _time.time()
|
|
|
|
|
|
df = ak.stock_zh_a_spot_em()
|
|
|
|
|
|
api_elapsed = _time.time() - api_start
|
|
|
logger.info(f"[API返回] ak.stock_zh_a_spot_em 成功: 返回 {len(df)} 只股票, 耗时 {api_elapsed:.2f}s")
|
|
|
circuit_breaker.record_success(source_key)
|
|
|
break
|
|
|
except Exception as e:
|
|
|
last_error = e
|
|
|
logger.warning(f"[API错误] ak.stock_zh_a_spot_em 获取失败 (attempt {attempt}/2): {e}")
|
|
|
time.sleep(min(2 ** attempt, 5))
|
|
|
|
|
|
# 更新缓存：成功缓存数据；失败也缓存空数据，避免同一轮任务对同一接口反复请求
|
|
|
if df is None:
|
|
|
logger.error(f"[API错误] ak.stock_zh_a_spot_em 最终失败: {last_error}")
|
|
|
circuit_breaker.record_failure(source_key, str(last_error))
|
|
|
df = pd.DataFrame()
|
|
|
_realtime_cache['data'] = df
|
|
|
_realtime_cache['timestamp'] = current_time
|
|
|
logger.info(f"[缓存更新] A股实时行情(东财) 缓存已刷新，TTL={_realtime_cache['ttl']}s")
|
|
|
|
|
|
if df is None or df.empty:
|
|
|
logger.warning(f"[实时行情] A股实时行情数据为空，跳过 {stock_code}")
|
|
|
return None
|
|
|
|
|
|
# 查找指定股票
|
|
|
row = df[df['代码'] == stock_code]
|
|
|
if row.empty:
|
|
|
logger.warning(f"[API返回] 未找到股票 {stock_code} 的实时行情")
|
|
|
return None
|
|
|
|
|
|
row = row.iloc[0]
|
|
|
|
|
|
# 使用 realtime_types.py 中的统一转换函数
|
|
|
quote = UnifiedRealtimeQuote(
|
|
|
code=stock_code,
|
|
|
name=str(row.get('名称', '')),
|
|
|
source=RealtimeSource.AKSHARE_EM,
|
|
|
price=safe_float(row.get('最新价')),
|
|
|
change_pct=safe_float(row.get('涨跌幅')),
|
|
|
change_amount=safe_float(row.get('涨跌额')),
|
|
|
volume=safe_int(row.get('成交量')),
|
|
|
amount=safe_float(row.get('成交额')),
|
|
|
volume_ratio=safe_float(row.get('量比')),
|
|
|
turnover_rate=safe_float(row.get('换手率')),
|
|
|
amplitude=safe_float(row.get('振幅')),
|
|
|
open_price=safe_float(row.get('今开')),
|
|
|
high=safe_float(row.get('最高')),
|
|
|
low=safe_float(row.get('最低')),
|
|
|
pe_ratio=safe_float(row.get('市盈率-动态')),
|
|
|
pb_ratio=safe_float(row.get('市净率')),
|
|
|
total_mv=safe_float(row.get('总市值')),
|
|
|
circ_mv=safe_float(row.get('流通市值')),
|
|
|
change_60d=safe_float(row.get('60日涨跌幅')),
|
|
|
high_52w=safe_float(row.get('52周最高')),
|
|
|
low_52w=safe_float(row.get('52周最低')),
|
|
|
)
|
|
|
|
|
|
logger.info(f"[实时行情-东财] {stock_code} {quote.name}: 价格={quote.price}, 涨跌={quote.change_pct}%, "
|
|
|
f"量比={quote.volume_ratio}, 换手率={quote.turnover_rate}%")
|
|
|
return quote
|
|
|
|
|
|
except Exception as e:
|
|
|
logger.error(f"[API错误] 获取 {stock_code} 实时行情(东财)失败: {e}")
|
|
|
circuit_breaker.record_failure(source_key, str(e))
|
|
|
return None
|
|
|
|
|
|
def _get_stock_realtime_quote_sina(self, stock_code: str) -> Optional[UnifiedRealtimeQuote]:
|
|
|
"""
|
|
|
获取普通 A 股实时行情数据（新浪财经数据源）
|
|
|
|
|
|
数据来源：新浪财经接口（直连，单股票查询）
|
|
|
优点：单股票查询，负载小，速度快
|
|
|
缺点：数据字段较少，无量比/PE/PB等
|
|
|
|
|
|
接口格式：http://hq.sinajs.cn/list=sh600519,sz000001
|
|
|
"""
|
|
|
circuit_breaker = get_realtime_circuit_breaker()
|
|
|
source_key = "akshare_sina"
|
|
|
|
|
|
try:
|
|
|
import requests
|
|
|
|
|
|
# 判断市场前缀
|
|
|
if stock_code.startswith(('6', '5', '9')):
|
|
|
symbol = f"sh{stock_code}"
|
|
|
else:
|
|
|
symbol = f"sz{stock_code}"
|
|
|
|
|
|
url = f"http://hq.sinajs.cn/list={symbol}"
|
|
|
headers = {
|
|
|
'Referer': 'http://finance.sina.com.cn',
|
|
|
'User-Agent': random.choice(USER_AGENTS)
|
|
|
}
|
|
|
|
|
|
logger.info(f"[API调用] 新浪财经接口获取 {stock_code} 实时行情...")
|
|
|
|
|
|
self._enforce_rate_limit()
|
|
|
response = requests.get(url, headers=headers, timeout=10)
|
|
|
response.encoding = 'gbk'
|
|
|
|
|
|
if response.status_code != 200:
|
|
|
logger.warning(f"[API错误] 新浪接口返回状态码 {response.status_code}")
|
|
|
circuit_breaker.record_failure(source_key, f"HTTP {response.status_code}")
|
|
|
return None
|
|
|
|
|
|
# 解析数据：var hq_str_sh600519="贵州茅台,1866.000,1870.000,..."
|
|
|
content = response.text.strip()
|
|
|
if '=""' in content or not content:
|
|
|
logger.warning(f"[API返回] 新浪接口未找到 {stock_code} 数据")
|
|
|
return None
|
|
|
|
|
|
# 提取引号内的数据
|
|
|
data_start = content.find('"')
|
|
|
data_end = content.rfind('"')
|
|
|
if data_start == -1 or data_end == -1:
|
|
|
logger.warning(f"[API返回] 新浪接口数据格式异常")
|
|
|
circuit_breaker.record_failure(source_key, "数据格式异常")
|
|
|
return None
|
|
|
|
|
|
data_str = content[data_start+1:data_end]
|
|
|
fields = data_str.split(',')
|
|
|
|
|
|
if len(fields) < 32:
|
|
|
logger.warning(f"[API返回] 新浪接口数据字段不足: {len(fields)}")
|
|
|
return None
|
|
|
|
|
|
circuit_breaker.record_success(source_key)
|
|
|
|
|
|
# 新浪数据字段顺序：
|
|
|
# 0:名称 1:今开 2:昨收 3:最新价 4:最高 5:最低 6:买一价 7:卖一价
|
|
|
# 8:成交量(股) 9:成交额(元) ... 30:日期 31:时间
|
|
|
# 使用 realtime_types.py 中的统一转换函数
|
|
|
price = safe_float(fields[3])
|
|
|
pre_close = safe_float(fields[2])
|
|
|
change_pct = None
|
|
|
change_amount = None
|
|
|
if price and pre_close and pre_close > 0:
|
|
|
change_amount = price - pre_close
|
|
|
change_pct = (change_amount / pre_close) * 100
|
|
|
|
|
|
quote = UnifiedRealtimeQuote(
|
|
|
code=stock_code,
|
|
|
name=fields[0],
|
|
|
source=RealtimeSource.AKSHARE_SINA,
|
|
|
price=price,
|
|
|
change_pct=change_pct,
|
|
|
change_amount=change_amount,
|
|
|
volume=safe_int(fields[8]), # 成交量（股）
|
|
|
amount=safe_float(fields[9]), # 成交额（元）
|
|
|
open_price=safe_float(fields[1]),
|
|
|
high=safe_float(fields[4]),
|
|
|
low=safe_float(fields[5]),
|
|
|
pre_close=pre_close,
|
|
|
)
|
|
|
|
|
|
logger.info(f"[实时行情-新浪] {stock_code} {quote.name}: 价格={quote.price}, "
|
|
|
f"涨跌={quote.change_pct:.2f}%" if quote.change_pct else "")
|
|
|
return quote
|
|
|
|
|
|
except Exception as e:
|
|
|
logger.error(f"[API错误] 获取 {stock_code} 实时行情(新浪)失败: {e}")
|
|
|
circuit_breaker.record_failure(source_key, str(e))
|
|
|
return None
|
|
|
|
|
|
def _get_stock_realtime_quote_tencent(self, stock_code: str) -> Optional[UnifiedRealtimeQuote]:
|
|
|
"""
|
|
|
获取普通 A 股实时行情数据（腾讯财经数据源）
|
|
|
|
|
|
数据来源：腾讯财经接口（直连，单股票查询）
|
|
|
优点：单股票查询，负载小，包含换手率
|
|
|
缺点：无量比/PE/PB等估值数据
|
|
|
|
|
|
接口格式：http://qt.gtimg.cn/q=sh600519,sz000001
|
|
|
"""
|
|
|
circuit_breaker = get_realtime_circuit_breaker()
|
|
|
source_key = "tencent"
|
|
|
|
|
|
try:
|
|
|
import requests
|
|
|
|
|
|
# 判断市场前缀
|
|
|
if stock_code.startswith(('6', '5', '9')):
|
|
|
symbol = f"sh{stock_code}"
|
|
|
else:
|
|
|
symbol = f"sz{stock_code}"
|
|
|
|
|
|
url = f"http://qt.gtimg.cn/q={symbol}"
|
|
|
headers = {
|
|
|
'Referer': 'http://finance.qq.com',
|
|
|
'User-Agent': random.choice(USER_AGENTS)
|
|
|
}
|
|
|
|
|
|
logger.info(f"[API调用] 腾讯财经接口获取 {stock_code} 实时行情...")
|
|
|
|
|
|
self._enforce_rate_limit()
|
|
|
response = requests.get(url, headers=headers, timeout=10)
|
|
|
response.encoding = 'gbk'
|
|
|
|
|
|
if response.status_code != 200:
|
|
|
logger.warning(f"[API错误] 腾讯接口返回状态码 {response.status_code}")
|
|
|
circuit_breaker.record_failure(source_key, f"HTTP {response.status_code}")
|
|
|
return None
|
|
|
|
|
|
content = response.text.strip()
|
|
|
if '=""' in content or not content:
|
|
|
logger.warning(f"[API返回] 腾讯接口未找到 {stock_code} 数据")
|
|
|
return None
|
|
|
|
|
|
# 提取数据
|
|
|
data_start = content.find('"')
|
|
|
data_end = content.rfind('"')
|
|
|
if data_start == -1 or data_end == -1:
|
|
|
logger.warning(f"[API返回] 腾讯接口数据格式异常")
|
|
|
circuit_breaker.record_failure(source_key, "数据格式异常")
|
|
|
return None
|
|
|
|
|
|
data_str = content[data_start+1:data_end]
|
|
|
fields = data_str.split('~')
|
|
|
|
|
|
if len(fields) < 45:
|
|
|
logger.warning(f"[API返回] 腾讯接口数据字段不足: {len(fields)}")
|
|
|
return None
|
|
|
|
|
|
circuit_breaker.record_success(source_key)
|
|
|
|
|
|
# 腾讯数据字段顺序（完整）：
|
|
|
# 1:名称 2:代码 3:最新价 4:昨收 5:今开 6:成交量(手) 7:外盘 8:内盘
|
|
|
# 9-28:买卖五档 30:时间戳 31:涨跌额 32:涨跌幅(%) 33:今开 34:最高 35:最低/成交量/成交额
|
|
|
# 36:成交量(手) 37:成交额(万) 38:换手率(%) 39:市盈率 43:振幅(%)
|
|
|
# 44:流通市值(亿) 45:总市值(亿) 46:市净率 47:涨停价 48:跌停价 49:量比
|
|
|
# 使用 realtime_types.py 中的统一转换函数
|
|
|
quote = UnifiedRealtimeQuote(
|
|
|
code=stock_code,
|
|
|
name=fields[1] if len(fields) > 1 else "",
|
|
|
source=RealtimeSource.TENCENT,
|
|
|
price=safe_float(fields[3]),
|
|
|
change_pct=safe_float(fields[32]),
|
|
|
change_amount=safe_float(fields[31]) if len(fields) > 31 else None,
|
|
|
volume=safe_int(fields[6]) * 100 if fields[6] else None, # 腾讯返回的是手，转为股
|
|
|
open_price=safe_float(fields[5]),
|
|
|
high=safe_float(fields[34]) if len(fields) > 34 else None,
|
|
|
low=safe_float(fields[35].split('/')[0]) if len(fields) > 35 and '/' in str(fields[35]) else safe_float(fields[35]) if len(fields) > 35 else None,
|
|
|
pre_close=safe_float(fields[4]),
|
|
|
turnover_rate=safe_float(fields[38]) if len(fields) > 38 else None,
|
|
|
amplitude=safe_float(fields[43]) if len(fields) > 43 else None,
|
|
|
volume_ratio=safe_float(fields[49]) if len(fields) > 49 else None, # 量比
|
|
|
pe_ratio=safe_float(fields[39]) if len(fields) > 39 else None, # 市盈率
|
|
|
pb_ratio=safe_float(fields[46]) if len(fields) > 46 else None, # 市净率
|
|
|
circ_mv=safe_float(fields[44]) * 100000000 if len(fields) > 44 and fields[44] else None, # 流通市值(亿->元)
|
|
|
total_mv=safe_float(fields[45]) * 100000000 if len(fields) > 45 and fields[45] else None, # 总市值(亿->元)
|
|
|
)
|
|
|
|
|
|
logger.info(f"[实时行情-腾讯] {stock_code} {quote.name}: 价格={quote.price}, "
|
|
|
f"涨跌={quote.change_pct}%, 量比={quote.volume_ratio}, 换手率={quote.turnover_rate}%")
|
|
|
return quote
|
|
|
|
|
|
except Exception as e:
|
|
|
logger.error(f"[API错误] 获取 {stock_code} 实时行情(腾讯)失败: {e}")
|
|
|
circuit_breaker.record_failure(source_key, str(e))
|
|
|
return None
|
|
|
|
|
|
def _get_etf_realtime_quote(self, stock_code: str) -> Optional[UnifiedRealtimeQuote]:
|
|
|
"""
|
|
|
获取 ETF 基金实时行情数据
|
|
|
|
|
|
数据来源：ak.fund_etf_spot_em()
|
|
|
包含：最新价、涨跌幅、成交量、成交额、换手率等
|
|
|
|
|
|
Args:
|
|
|
stock_code: ETF 代码
|
|
|
|
|
|
Returns:
|
|
|
UnifiedRealtimeQuote 对象，获取失败返回 None
|
|
|
"""
|
|
|
import akshare as ak
|
|
|
circuit_breaker = get_realtime_circuit_breaker()
|
|
|
source_key = "akshare_etf"
|
|
|
|
|
|
try:
|
|
|
# 检查缓存
|
|
|
current_time = time.time()
|
|
|
if (_etf_realtime_cache['data'] is not None and
|
|
|
current_time - _etf_realtime_cache['timestamp'] < _etf_realtime_cache['ttl']):
|
|
|
df = _etf_realtime_cache['data']
|
|
|
logger.debug(f"[缓存命中] 使用缓存的ETF实时行情数据")
|
|
|
else:
|
|
|
last_error: Optional[Exception] = None
|
|
|
df = None
|
|
|
for attempt in range(1, 3):
|
|
|
try:
|
|
|
# 防封禁策略
|
|
|
self._set_random_user_agent()
|
|
|
self._enforce_rate_limit()
|
|
|
|
|
|
logger.info(f"[API调用] ak.fund_etf_spot_em() 获取ETF实时行情... (attempt {attempt}/2)")
|
|
|
import time as _time
|
|
|
api_start = _time.time()
|
|
|
|
|
|
df = ak.fund_etf_spot_em()
|
|
|
|
|
|
api_elapsed = _time.time() - api_start
|
|
|
logger.info(f"[API返回] ak.fund_etf_spot_em 成功: 返回 {len(df)} 只ETF, 耗时 {api_elapsed:.2f}s")
|
|
|
circuit_breaker.record_success(source_key)
|
|
|
break
|
|
|
except Exception as e:
|
|
|
last_error = e
|
|
|
logger.warning(f"[API错误] ak.fund_etf_spot_em 获取失败 (attempt {attempt}/2): {e}")
|
|
|
time.sleep(min(2 ** attempt, 5))
|
|
|
|
|
|
if df is None:
|
|
|
logger.error(f"[API错误] ak.fund_etf_spot_em 最终失败: {last_error}")
|
|
|
circuit_breaker.record_failure(source_key, str(last_error))
|
|
|
df = pd.DataFrame()
|
|
|
_etf_realtime_cache['data'] = df
|
|
|
_etf_realtime_cache['timestamp'] = current_time
|
|
|
|
|
|
if df is None or df.empty:
|
|
|
logger.warning(f"[实时行情] ETF实时行情数据为空，跳过 {stock_code}")
|
|
|
return None
|
|
|
|
|
|
# 查找指定 ETF
|
|
|
row = df[df['代码'] == stock_code]
|
|
|
if row.empty:
|
|
|
logger.warning(f"[API返回] 未找到 ETF {stock_code} 的实时行情")
|
|
|
return None
|
|
|
|
|
|
row = row.iloc[0]
|
|
|
|
|
|
# 使用 realtime_types.py 中的统一转换函数
|
|
|
# ETF 行情数据构建
|
|
|
quote = UnifiedRealtimeQuote(
|
|
|
code=stock_code,
|
|
|
name=str(row.get('名称', '')),
|
|
|
source=RealtimeSource.AKSHARE_EM,
|
|
|
price=safe_float(row.get('最新价')),
|
|
|
change_pct=safe_float(row.get('涨跌幅')),
|
|
|
change_amount=safe_float(row.get('涨跌额')),
|
|
|
volume=safe_int(row.get('成交量')),
|
|
|
amount=safe_float(row.get('成交额')),
|
|
|
volume_ratio=safe_float(row.get('量比')),
|
|
|
turnover_rate=safe_float(row.get('换手率')),
|
|
|
amplitude=safe_float(row.get('振幅')),
|
|
|
open_price=safe_float(row.get('今开')),
|
|
|
high=safe_float(row.get('最高')),
|
|
|
low=safe_float(row.get('最低')),
|
|
|
total_mv=safe_float(row.get('总市值')),
|
|
|
circ_mv=safe_float(row.get('流通市值')),
|
|
|
high_52w=safe_float(row.get('52周最高')),
|
|
|
low_52w=safe_float(row.get('52周最低')),
|
|
|
)
|
|
|
|
|
|
logger.info(f"[ETF实时行情] {stock_code} {quote.name}: 价格={quote.price}, 涨跌={quote.change_pct}%, "
|
|
|
f"换手率={quote.turnover_rate}%")
|
|
|
return quote
|
|
|
|
|
|
except Exception as e:
|
|
|
logger.error(f"[API错误] 获取 ETF {stock_code} 实时行情失败: {e}")
|
|
|
circuit_breaker.record_failure(source_key, str(e))
|
|
|
return None
|
|
|
|
|
|
def _get_hk_realtime_quote(self, stock_code: str) -> Optional[UnifiedRealtimeQuote]:
|
|
|
"""
|
|
|
获取港股实时行情数据
|
|
|
|
|
|
数据来源：ak.stock_hk_spot_em()
|
|
|
包含：最新价、涨跌幅、成交量、成交额等
|
|
|
|
|
|
Args:
|
|
|
stock_code: 港股代码
|
|
|
|
|
|
Returns:
|
|
|
UnifiedRealtimeQuote 对象，获取失败返回 None
|
|
|
"""
|
|
|
import akshare as ak
|
|
|
circuit_breaker = get_realtime_circuit_breaker()
|
|
|
source_key = "akshare_hk"
|
|
|
|
|
|
try:
|
|
|
# 防封禁策略
|
|
|
self._set_random_user_agent()
|
|
|
self._enforce_rate_limit()
|
|
|
|
|
|
# 确保代码格式正确（5位数字）
|
|
|
code = stock_code.lower().replace('hk', '').zfill(5)
|
|
|
|
|
|
logger.info(f"[API调用] ak.stock_hk_spot_em() 获取港股实时行情...")
|
|
|
import time as _time
|
|
|
api_start = _time.time()
|
|
|
|
|
|
df = ak.stock_hk_spot_em()
|
|
|
|
|
|
api_elapsed = _time.time() - api_start
|
|
|
logger.info(f"[API返回] ak.stock_hk_spot_em 成功: 返回 {len(df)} 只港股, 耗时 {api_elapsed:.2f}s")
|
|
|
circuit_breaker.record_success(source_key)
|
|
|
|
|
|
# 查找指定港股
|
|
|
row = df[df['代码'] == code]
|
|
|
if row.empty:
|
|
|
logger.warning(f"[API返回] 未找到港股 {code} 的实时行情")
|
|
|
return None
|
|
|
|
|
|
row = row.iloc[0]
|
|
|
|
|
|
# 使用 realtime_types.py 中的统一转换函数
|
|
|
# 港股行情数据构建
|
|
|
quote = UnifiedRealtimeQuote(
|
|
|
code=stock_code,
|
|
|
name=str(row.get('名称', '')),
|
|
|
source=RealtimeSource.AKSHARE_EM,
|
|
|
price=safe_float(row.get('最新价')),
|
|
|
change_pct=safe_float(row.get('涨跌幅')),
|
|
|
change_amount=safe_float(row.get('涨跌额')),
|
|
|
volume=safe_int(row.get('成交量')),
|
|
|
amount=safe_float(row.get('成交额')),
|
|
|
volume_ratio=safe_float(row.get('量比')),
|
|
|
turnover_rate=safe_float(row.get('换手率')),
|
|
|
amplitude=safe_float(row.get('振幅')),
|
|
|
pe_ratio=safe_float(row.get('市盈率')),
|
|
|
pb_ratio=safe_float(row.get('市净率')),
|
|
|
total_mv=safe_float(row.get('总市值')),
|
|
|
circ_mv=safe_float(row.get('流通市值')),
|
|
|
high_52w=safe_float(row.get('52周最高')),
|
|
|
low_52w=safe_float(row.get('52周最低')),
|
|
|
)
|
|
|
|
|
|
logger.info(f"[港股实时行情] {stock_code} {quote.name}: 价格={quote.price}, 涨跌={quote.change_pct}%, "
|
|
|
f"换手率={quote.turnover_rate}%")
|
|
|
return quote
|
|
|
|
|
|
except Exception as e:
|
|
|
logger.error(f"[API错误] 获取港股 {stock_code} 实时行情失败: {e}")
|
|
|
circuit_breaker.record_failure(source_key, str(e))
|
|
|
return None
|
|
|
|
|
|
def get_chip_distribution(self, stock_code: str) -> Optional[ChipDistribution]:
|
|
|
"""
|
|
|
获取筹码分布数据
|
|
|
|
|
|
数据来源：ak.stock_cyq_em()
|
|
|
包含：获利比例、平均成本、筹码集中度
|
|
|
|
|
|
注意：ETF/指数没有筹码分布数据，会直接返回 None
|
|
|
|
|
|
Args:
|
|
|
stock_code: 股票代码
|
|
|
|
|
|
Returns:
|
|
|
ChipDistribution 对象（最新一天的数据），获取失败返回 None
|
|
|
"""
|
|
|
import akshare as ak
|
|
|
|
|
|
# 美股没有筹码分布数据（Akshare 不支持）
|
|
|
if _is_us_code(stock_code):
|
|
|
logger.debug(f"[API跳过] {stock_code} 是美股，无筹码分布数据")
|
|
|
return None
|
|
|
|
|
|
# ETF/指数没有筹码分布数据
|
|
|
if _is_etf_code(stock_code):
|
|
|
logger.debug(f"[API跳过] {stock_code} 是 ETF/指数，无筹码分布数据")
|
|
|
return None
|
|
|
|
|
|
try:
|
|
|
# 防封禁策略
|
|
|
self._set_random_user_agent()
|
|
|
self._enforce_rate_limit()
|
|
|
|
|
|
logger.info(f"[API调用] ak.stock_cyq_em(symbol={stock_code}) 获取筹码分布...")
|
|
|
import time as _time
|
|
|
api_start = _time.time()
|
|
|
|
|
|
df = ak.stock_cyq_em(symbol=stock_code)
|
|
|
|
|
|
api_elapsed = _time.time() - api_start
|
|
|
|
|
|
if df.empty:
|
|
|
logger.warning(f"[API返回] ak.stock_cyq_em 返回空数据, 耗时 {api_elapsed:.2f}s")
|
|
|
return None
|
|
|
|
|
|
logger.info(f"[API返回] ak.stock_cyq_em 成功: 返回 {len(df)} 天数据, 耗时 {api_elapsed:.2f}s")
|
|
|
logger.debug(f"[API返回] 筹码数据列名: {list(df.columns)}")
|
|
|
|
|
|
# 取最新一天的数据
|
|
|
latest = df.iloc[-1]
|
|
|
|
|
|
# 使用 realtime_types.py 中的统一转换函数
|
|
|
chip = ChipDistribution(
|
|
|
code=stock_code,
|
|
|
date=str(latest.get('日期', '')),
|
|
|
profit_ratio=safe_float(latest.get('获利比例')),
|
|
|
avg_cost=safe_float(latest.get('平均成本')),
|
|
|
cost_90_low=safe_float(latest.get('90成本-低')),
|
|
|
cost_90_high=safe_float(latest.get('90成本-高')),
|
|
|
concentration_90=safe_float(latest.get('90集中度')),
|
|
|
cost_70_low=safe_float(latest.get('70成本-低')),
|
|
|
cost_70_high=safe_float(latest.get('70成本-高')),
|
|
|
concentration_70=safe_float(latest.get('70集中度')),
|
|
|
)
|
|
|
|
|
|
logger.info(f"[筹码分布] {stock_code} 日期={chip.date}: 获利比例={chip.profit_ratio:.1%}, "
|
|
|
f"平均成本={chip.avg_cost}, 90%集中度={chip.concentration_90:.2%}, "
|
|
|
f"70%集中度={chip.concentration_70:.2%}")
|
|
|
return chip
|
|
|
|
|
|
except Exception as e:
|
|
|
logger.error(f"[API错误] 获取 {stock_code} 筹码分布失败: {e}")
|
|
|
return None
|
|
|
|
|
|
def get_enhanced_data(self, stock_code: str, days: int = 60) -> Dict[str, Any]:
|
|
|
"""
|
|
|
获取增强数据（历史K线 + 实时行情 + 筹码分布）
|
|
|
|
|
|
Args:
|
|
|
stock_code: 股票代码
|
|
|
days: 历史数据天数
|
|
|
|
|
|
Returns:
|
|
|
包含所有数据的字典
|
|
|
"""
|
|
|
result = {
|
|
|
'code': stock_code,
|
|
|
'daily_data': None,
|
|
|
'realtime_quote': None,
|
|
|
'chip_distribution': None,
|
|
|
}
|
|
|
|
|
|
# 获取日线数据
|
|
|
try:
|
|
|
df = self.get_daily_data(stock_code, days=days)
|
|
|
result['daily_data'] = df
|
|
|
except Exception as e:
|
|
|
logger.error(f"获取 {stock_code} 日线数据失败: {e}")
|
|
|
|
|
|
# 获取实时行情
|
|
|
result['realtime_quote'] = self.get_realtime_quote(stock_code)
|
|
|
|
|
|
# 获取筹码分布
|
|
|
result['chip_distribution'] = self.get_chip_distribution(stock_code)
|
|
|
|
|
|
return result
|
|
|
|
|
|
def get_main_indices(self) -> Optional[List[Dict[str, Any]]]:
|
|
|
"""
|
|
|
获取主要指数实时行情 (新浪接口)
|
|
|
"""
|
|
|
import akshare as ak
|
|
|
|
|
|
# 主要指数代码映射
|
|
|
indices_map = {
|
|
|
'sh000001': '上证指数',
|
|
|
'sz399001': '深证成指',
|
|
|
'sz399006': '创业板指',
|
|
|
'sh000688': '科创50',
|
|
|
'sh000016': '上证50',
|
|
|
'sh000300': '沪深300',
|
|
|
}
|
|
|
|
|
|
try:
|
|
|
self._set_random_user_agent()
|
|
|
self._enforce_rate_limit()
|
|
|
|
|
|
# 使用 akshare 获取指数行情（新浪财经接口）
|
|
|
df = ak.stock_zh_index_spot_sina()
|
|
|
|
|
|
results = []
|
|
|
if df is not None and not df.empty:
|
|
|
for code, name in indices_map.items():
|
|
|
# 查找对应指数
|
|
|
row = df[df['代码'] == code]
|
|
|
if row.empty:
|
|
|
# 尝试带前缀查找
|
|
|
row = df[df['代码'].str.contains(code)]
|
|
|
|
|
|
if not row.empty:
|
|
|
row = row.iloc[0]
|
|
|
current = safe_float(row.get('最新价', 0))
|
|
|
prev_close = safe_float(row.get('昨收', 0))
|
|
|
high = safe_float(row.get('最高', 0))
|
|
|
low = safe_float(row.get('最低', 0))
|
|
|
|
|
|
# 计算振幅
|
|
|
amplitude = 0.0
|
|
|
if prev_close > 0:
|
|
|
amplitude = (high - low) / prev_close * 100
|
|
|
|
|
|
results.append({
|
|
|
'code': code,
|
|
|
'name': name,
|
|
|
'current': current,
|
|
|
'change': safe_float(row.get('涨跌额', 0)),
|
|
|
'change_pct': safe_float(row.get('涨跌幅', 0)),
|
|
|
'open': safe_float(row.get('今开', 0)),
|
|
|
'high': high,
|
|
|
'low': low,
|
|
|
'prev_close': prev_close,
|
|
|
'volume': safe_float(row.get('成交量', 0)),
|
|
|
'amount': safe_float(row.get('成交额', 0)),
|
|
|
'amplitude': amplitude,
|
|
|
})
|
|
|
return results
|
|
|
|
|
|
except Exception as e:
|
|
|
logger.error(f"[Akshare] 获取指数行情失败: {e}")
|
|
|
return None
|
|
|
|
|
|
def get_market_stats(self) -> Optional[Dict[str, Any]]:
|
|
|
"""
|
|
|
获取市场涨跌统计
|
|
|
|
|
|
数据源优先级：
|
|
|
1. 东财接口 (ak.stock_zh_a_spot_em)
|
|
|
2. 新浪接口 (ak.stock_zh_a_spot)
|
|
|
"""
|
|
|
import akshare as ak
|
|
|
|
|
|
# 优先东财接口
|
|
|
try:
|
|
|
self._set_random_user_agent()
|
|
|
self._enforce_rate_limit()
|
|
|
|
|
|
logger.info("[API调用] ak.stock_zh_a_spot_em() 获取市场统计...")
|
|
|
df = ak.stock_zh_a_spot_em()
|
|
|
if df is not None and not df.empty:
|
|
|
return self._calc_market_stats(df, change_col='涨跌幅', amount_col='成交额')
|
|
|
except Exception as e:
|
|
|
logger.warning(f"[Akshare] 东财接口获取市场统计失败: {e}，尝试新浪接口")
|
|
|
|
|
|
# 东财失败后，尝试新浪接口
|
|
|
try:
|
|
|
self._set_random_user_agent()
|
|
|
self._enforce_rate_limit()
|
|
|
|
|
|
logger.info("[API调用] ak.stock_zh_a_spot() 获取市场统计(新浪)...")
|
|
|
df = ak.stock_zh_a_spot()
|
|
|
if df is not None and not df.empty:
|
|
|
change_col = None
|
|
|
for col in ['change_percent', 'changepercent', '涨跌幅', 'trade_ratio']:
|
|
|
if col in df.columns:
|
|
|
change_col = col
|
|
|
break
|
|
|
|
|
|
amount_col = None
|
|
|
for col in ['amount', '成交额', 'trade_amount']:
|
|
|
if col in df.columns:
|
|
|
amount_col = col
|
|
|
break
|
|
|
|
|
|
if change_col:
|
|
|
return self._calc_market_stats(df, change_col=change_col, amount_col=amount_col)
|
|
|
except Exception as e:
|
|
|
logger.error(f"[Akshare] 新浪接口获取市场统计也失败: {e}")
|
|
|
|
|
|
return None
|
|
|
|
|
|
def _calc_market_stats(
|
|
|
self,
|
|
|
df: pd.DataFrame,
|
|
|
change_col: str,
|
|
|
amount_col: Optional[str] = None
|
|
|
) -> Optional[Dict[str, Any]]:
|
|
|
"""从行情 DataFrame 计算涨跌统计。"""
|
|
|
if change_col not in df.columns:
|
|
|
return None
|
|
|
|
|
|
df[change_col] = pd.to_numeric(df[change_col], errors='coerce')
|
|
|
stats = {
|
|
|
'up_count': len(df[df[change_col] > 0]),
|
|
|
'down_count': len(df[df[change_col] < 0]),
|
|
|
'flat_count': len(df[df[change_col] == 0]),
|
|
|
'limit_up_count': len(df[df[change_col] >= 9.9]),
|
|
|
'limit_down_count': len(df[df[change_col] <= -9.9]),
|
|
|
'total_amount': 0.0,
|
|
|
}
|
|
|
if amount_col and amount_col in df.columns:
|
|
|
df[amount_col] = pd.to_numeric(df[amount_col], errors='coerce')
|
|
|
stats['total_amount'] = df[amount_col].sum() / 1e8
|
|
|
return stats
|
|
|
|
|
|
def get_sector_rankings(self, n: int = 5) -> Optional[Tuple[List[Dict], List[Dict]]]:
|
|
|
"""
|
|
|
获取板块涨跌榜
|
|
|
|
|
|
数据源优先级：
|
|
|
1. 东财接口 (ak.stock_board_industry_name_em)
|
|
|
2. 新浪接口 (ak.stock_sector_spot)
|
|
|
"""
|
|
|
import akshare as ak
|
|
|
|
|
|
# 优先东财接口
|
|
|
try:
|
|
|
self._set_random_user_agent()
|
|
|
self._enforce_rate_limit()
|
|
|
|
|
|
logger.info("[API调用] ak.stock_board_industry_name_em() 获取板块排行...")
|
|
|
df = ak.stock_board_industry_name_em()
|
|
|
if df is not None and not df.empty:
|
|
|
change_col = '涨跌幅'
|
|
|
if change_col in df.columns:
|
|
|
df[change_col] = pd.to_numeric(df[change_col], errors='coerce')
|
|
|
df = df.dropna(subset=[change_col])
|
|
|
|
|
|
# 涨幅前n
|
|
|
top = df.nlargest(n, change_col)
|
|
|
top_sectors = [
|
|
|
{'name': row['板块名称'], 'change_pct': row[change_col]}
|
|
|
for _, row in top.iterrows()
|
|
|
]
|
|
|
|
|
|
bottom = df.nsmallest(n, change_col)
|
|
|
bottom_sectors = [
|
|
|
{'name': row['板块名称'], 'change_pct': row[change_col]}
|
|
|
for _, row in bottom.iterrows()
|
|
|
]
|
|
|
|
|
|
return top_sectors, bottom_sectors
|
|
|
except Exception as e:
|
|
|
logger.warning(f"[Akshare] 东财接口获取板块排行失败: {e}，尝试新浪接口")
|
|
|
|
|
|
# 东财失败后，尝试新浪接口
|
|
|
try:
|
|
|
self._set_random_user_agent()
|
|
|
self._enforce_rate_limit()
|
|
|
|
|
|
logger.info("[API调用] ak.stock_sector_spot() 获取板块排行(新浪)...")
|
|
|
df = ak.stock_sector_spot(indicator='新浪行业')
|
|
|
if df is None or df.empty:
|
|
|
return None
|
|
|
|
|
|
change_col = None
|
|
|
for col in ['涨跌幅', 'change_pct', '涨幅']:
|
|
|
if col in df.columns:
|
|
|
change_col = col
|
|
|
break
|
|
|
|
|
|
name_col = None
|
|
|
for col in ['板块', '板块名称', 'label', 'name']:
|
|
|
if col in df.columns:
|
|
|
name_col = col
|
|
|
break
|
|
|
|
|
|
if not change_col or not name_col:
|
|
|
return None
|
|
|
|
|
|
df[change_col] = pd.to_numeric(df[change_col], errors='coerce')
|
|
|
df = df.dropna(subset=[change_col])
|
|
|
top = df.nlargest(n, change_col)
|
|
|
bottom = df.nsmallest(n, change_col)
|
|
|
top_sectors = [
|
|
|
{'name': str(row[name_col]), 'change_pct': float(row[change_col])}
|
|
|
for _, row in top.iterrows()
|
|
|
]
|
|
|
bottom_sectors = [
|
|
|
{'name': str(row[name_col]), 'change_pct': float(row[change_col])}
|
|
|
for _, row in bottom.iterrows()
|
|
|
]
|
|
|
return top_sectors, bottom_sectors
|
|
|
except Exception as e:
|
|
|
logger.error(f"[Akshare] 新浪接口获取板块排行也失败: {e}")
|
|
|
return None
|
|
|
|
|
|
|
|
|
if __name__ == "__main__":
|
|
|
# 测试代码
|
|
|
logging.basicConfig(level=logging.DEBUG)
|
|
|
|
|
|
fetcher = AkshareFetcher()
|
|
|
|
|
|
# 测试普通股票
|
|
|
print("=" * 50)
|
|
|
print("测试普通股票数据获取")
|
|
|
print("=" * 50)
|
|
|
try:
|
|
|
df = fetcher.get_daily_data('600519') # 茅台
|
|
|
print(f"[股票] 获取成功，共 {len(df)} 条数据")
|
|
|
print(df.tail())
|
|
|
except Exception as e:
|
|
|
print(f"[股票] 获取失败: {e}")
|
|
|
|
|
|
# 测试 ETF 基金
|
|
|
print("\n" + "=" * 50)
|
|
|
print("测试 ETF 基金数据获取")
|
|
|
print("=" * 50)
|
|
|
try:
|
|
|
df = fetcher.get_daily_data('512400') # 有色龙头ETF
|
|
|
print(f"[ETF] 获取成功，共 {len(df)} 条数据")
|
|
|
print(df.tail())
|
|
|
except Exception as e:
|
|
|
print(f"[ETF] 获取失败: {e}")
|
|
|
|
|
|
# 测试 ETF 实时行情
|
|
|
print("\n" + "=" * 50)
|
|
|
print("测试 ETF 实时行情获取")
|
|
|
print("=" * 50)
|
|
|
try:
|
|
|
quote = fetcher.get_realtime_quote('512880') # 证券ETF
|
|
|
if quote:
|
|
|
print(f"[ETF实时] {quote.name}: 价格={quote.price}, 涨跌幅={quote.change_pct}%")
|
|
|
else:
|
|
|
print("[ETF实时] 未获取到数据")
|
|
|
except Exception as e:
|
|
|
print(f"[ETF实时] 获取失败: {e}")
|
|
|
|
|
|
# 测试港股历史数据
|
|
|
print("\n" + "=" * 50)
|
|
|
print("测试港股历史数据获取")
|
|
|
print("=" * 50)
|
|
|
try:
|
|
|
df = fetcher.get_daily_data('00700') # 腾讯控股
|
|
|
print(f"[港股] 获取成功，共 {len(df)} 条数据")
|
|
|
print(df.tail())
|
|
|
except Exception as e:
|
|
|
print(f"[港股] 获取失败: {e}")
|
|
|
|
|
|
# 测试港股实时行情
|
|
|
print("\n" + "=" * 50)
|
|
|
print("测试港股实时行情获取")
|
|
|
print("=" * 50)
|
|
|
try:
|
|
|
quote = fetcher.get_realtime_quote('00700') # 腾讯控股
|
|
|
if quote:
|
|
|
print(f"[港股实时] {quote.name}: 价格={quote.price}, 涨跌幅={quote.change_pct}%")
|
|
|
else:
|
|
|
print("[港股实时] 未获取到数据")
|
|
|
except Exception as e:
|
|
|
print(f"[港股实时] 获取失败: {e}")
|
