347 lines
11 KiB
Python
347 lines
11 KiB
Python
"""
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Tushare 数据封装
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提供 A 股板块、个股行情数据获取接口（使用同花顺系列接口）
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"""
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import time
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import tushare as ts
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import pandas as pd
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from typing import Dict, List, Optional
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from datetime import datetime, timedelta
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from app.utils.logger import logger
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class TushareClient:
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"""Tushare 数据客户端（同花顺系列接口）"""
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# 缓存数据，避免频繁请求
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_cache = {}
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_cache_time = {}
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_last_request_time = 0
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def __init__(self, token: str):
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"""
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初始化客户端
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Args:
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token: Tushare token
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"""
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self.token = token
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ts.set_token(token)
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self.pro = ts.pro_api()
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self.cache_ttl = 300 # 缓存5分钟
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self.request_delay = 0.5 # 请求间隔（秒）- tushare 有频率限制
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def _get_cached(self, key: str, fetch_func) -> pd.DataFrame:
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"""获取缓存数据，支持重试"""
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now = datetime.now()
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# 检查缓存
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if key in self._cache:
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cache_time = self._cache_time.get(key)
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if cache_time and (now - cache_time).seconds < self.cache_ttl:
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logger.debug(f"使用缓存数据: {key}")
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return self._cache[key]
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# 请求限流
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elapsed = now.timestamp() - self._last_request_time
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if elapsed < self.request_delay:
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time.sleep(self.request_delay - elapsed)
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# 重试逻辑
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max_retries = 3
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for attempt in range(max_retries):
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try:
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self._last_request_time = time.time()
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df = fetch_func()
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if df is not None and not df.empty:
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self._cache[key] = df
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self._cache_time[key] = now
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logger.debug(f"获取数据成功: {key}")
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return df
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except Exception as e:
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error_msg = str(e)
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# 指数退避重试
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if attempt < max_retries - 1:
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wait_time = (2 ** attempt) * 2
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logger.warning(
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f"获取数据失败 {key} (尝试 {attempt + 1}/{max_retries}): {e}，"
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f"等待 {wait_time}秒后重试..."
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)
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time.sleep(wait_time)
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continue
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logger.error(f"获取数据失败 {key}: {e}")
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break
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return pd.DataFrame()
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def get_concept_sectors(self) -> pd.DataFrame:
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"""
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获取概念板块列表
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使用 ths_index 接口，type="N" 代表概念板块
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Returns:
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概念板块列表
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"""
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def fetch():
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# ths_index - 获取同花顺概念指数列表
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return self.pro.ths_index(type='N')
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return self._get_cached('concept_sectors', fetch)
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def get_sector_daily(self, ts_code: str, start_date: str = None, end_date: str = None) -> pd.DataFrame:
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"""
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获取板块日线行情
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Args:
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ts_code: 板块指数代码（如 885823.TI）
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start_date: 开始日期 (YYYYMMDD)
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end_date: 结束日期 (YYYYMMDD)
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Returns:
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板块日线数据
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"""
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if not start_date:
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start_date = (datetime.now() - timedelta(days=30)).strftime('%Y%m%d')
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if not end_date:
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end_date = datetime.now().strftime('%Y%m%d')
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def fetch():
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# ths_daily - 获取板块指数历史行情
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return self.pro.ths_daily(
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ts_code=ts_code,
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start_date=start_date,
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end_date=end_date
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)
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return self._get_cached(f'sector_daily_{ts_code}_{end_date}', fetch)
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def get_sector_members(self, ts_code: str) -> pd.DataFrame:
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"""
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获取板块成分股
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Args:
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ts_code: 板块指数代码（如 885823.TI）
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Returns:
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成分股列表
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"""
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def fetch():
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# ths_member - 获取板块成分股
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return self.pro.ths_member(ts_code=ts_code)
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return self._get_cached(f'sector_members_{ts_code}', fetch)
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def get_stock_daily(self, ts_code: str, start_date: str = None, end_date: str = None) -> pd.DataFrame:
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"""
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获取个股日线行情
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Args:
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ts_code: 股票代码（如 000001.SZ）
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start_date: 开始日期 (YYYYMMDD)
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end_date: 结束日期 (YYYYMMDD)
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Returns:
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日线数据
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"""
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if not start_date:
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start_date = (datetime.now() - timedelta(days=30)).strftime('%Y%m%d')
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if not end_date:
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end_date = datetime.now().strftime('%Y%m%d')
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def fetch():
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# daily - 获取日线行情
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return self.pro.daily(
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ts_code=ts_code,
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start_date=start_date,
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end_date=end_date
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)
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return self._get_cached(f'stock_daily_{ts_code}_{end_date}', fetch)
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def get_stock_daily_basic(self, ts_codes: List[str], trade_date: str = None) -> pd.DataFrame:
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"""
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获取个股每日指标（包含换手率、量比等）
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Args:
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ts_codes: 股票代码列表
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trade_date: 交易日期 (YYYYMMDD)
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Returns:
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每日指标数据
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"""
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if not ts_codes:
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return pd.DataFrame()
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from datetime import datetime, timedelta
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if not trade_date:
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trade_date = datetime.now().strftime('%Y%m%d')
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def fetch():
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# daily_basic - 获取每日指标
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codes_str = ','.join(ts_codes[:300]) # 限制单次查询数量
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# 尝试获取最近3天的数据（以防当天数据未更新）
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all_data = []
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for i in range(3):
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try_date = (datetime.now() - timedelta(days=i)).strftime('%Y%m%d')
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df = self.pro.daily_basic(
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ts_code=codes_str,
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trade_date=try_date,
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fields='ts_code,trade_date,turnover_rate,volume_ratio,pe,pb'
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)
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if not df.empty:
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all_data.append(df)
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# 如果找到数据就不再尝试更早的日期
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break
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if all_data:
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return pd.concat(all_data, ignore_index=True)
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return pd.DataFrame()
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return self._get_cached(f'stock_daily_basic_{trade_date}', fetch)
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def get_stock_basic(self) -> pd.DataFrame:
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"""
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获取股票基本信息列表
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Returns:
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股票基本信息
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"""
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def fetch():
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# stock_basic - 获取股票基本信息
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return self.pro.stock_basic(
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exchange='',
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list_status='L',
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fields='ts_code,symbol,name,area,industry,list_date'
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)
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return self._get_cached('stock_basic', fetch)
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def get_realtime_data(self, ts_codes: List[str]) -> pd.DataFrame:
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"""
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获取实时行情数据（使用最新的日线数据）
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注意：tushare 不提供真正的实时数据，这里返回最新的日线数据
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注意：amount 字段单位是千元，需要 * 1000 转换为元
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Args:
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ts_codes: 股票代码列表
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Returns:
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实时行情数据（amount 单位为千元）
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"""
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if not ts_codes:
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return pd.DataFrame()
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# 获取今天的日期
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today = datetime.now().strftime('%Y%m%d')
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yesterday = (datetime.now() - timedelta(days=10)).strftime('%Y%m%d')
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def fetch():
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# 使用 daily 接口获取最近数据
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codes_str = ','.join(ts_codes[:100]) # 限制单次查询数量
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df = self.pro.daily(
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ts_code=codes_str,
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start_date=yesterday,
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end_date=today
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)
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# 只返回每个股票的最新一天数据
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if not df.empty:
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df = df.sort_values('trade_date').groupby('ts_code').tail(1)
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return df
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return self._get_cached(f'realtime_{today}', fetch)
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def get_hot_sectors(self, threshold: float = 2.0) -> pd.DataFrame:
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"""
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获取异动板块（一次性获取所有板块的最新行情）
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Args:
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threshold: 涨跌幅阈值（%）
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Returns:
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异动板块数据
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"""
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try:
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# 1. 获取所有概念板块
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sectors_df = self.get_concept_sectors()
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if sectors_df.empty:
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logger.warning("获取概念板块列表失败")
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return pd.DataFrame()
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logger.info(f"获取到 {len(sectors_df)} 个概念板块")
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# 2. 获取今天的日期
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today = datetime.now().strftime('%Y%m%d')
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yesterday = (datetime.now() - timedelta(days=10)).strftime('%Y%m%d')
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# 3. 批量获取板块行情（为了效率，限制数量）
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hot_sectors = []
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max_sectors = 100 # 最多检查100个板块
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for idx, row in sectors_df.head(max_sectors).iterrows():
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ts_code = row['ts_code']
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name = row.get('name', '')
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try:
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# 获取板块最新行情
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daily_df = self.pro.ths_daily(
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ts_code=ts_code,
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start_date=yesterday,
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end_date=today
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)
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if daily_df.empty:
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continue
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# 获取最新一天的数据
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latest = daily_df.sort_values('trade_date').iloc[-1]
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# 检查涨跌幅 - 注意列名是 pct_change 不是 pct_chg
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change_pct = float(latest.get('pct_change', 0))
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if change_pct >= threshold:
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hot_sectors.append({
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'ts_code': ts_code,
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'name': name,
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'change_pct': change_pct,
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'change': float(latest.get('change', 0)), # 涨跌额
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'close': float(latest.get('close', 0)),
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'amount': float(latest.get('amount', 0)), # 成交额（元）
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'volume': float(latest.get('vol', 0)), # 成交量（手）
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'turnover_rate': float(latest.get('turnover_rate', 0)), # 换手率
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'trade_date': str(latest.get('trade_date', ''))
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})
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except Exception as e:
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logger.debug(f"获取板块 {name} 行情失败: {e}")
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continue
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result_df = pd.DataFrame(hot_sectors)
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if not result_df.empty:
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result_df = result_df.sort_values('change_pct', ascending=False)
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return result_df
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except Exception as e:
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logger.error(f"获取异动板块失败: {e}")
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return pd.DataFrame()
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# 全局单例
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_tushare_client: Optional[TushareClient] = None
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def get_tushare_client(token: str = None) -> Optional[TushareClient]:
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"""获取 Tushare 客户端单例"""
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global _tushare_client
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if _tushare_client is None:
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if not token:
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return None
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_tushare_client = TushareClient(token)
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return _tushare_client
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