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import tushare as ts
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import pandas as pd
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from typing import List, Optional
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from datetime import datetime
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from app.services.datasource.base import DataSourceBase
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from app.config import settings
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class TushareSource(DataSourceBase):
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"""Tushare 数据源适配器"""
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def __init__(self, config: dict):
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super().__init__(config)
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self.name = "tushare"
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self.pro = None
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self._token = config.get("token", settings.TUSHARE_TOKEN)
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def initialize(self) -> bool:
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"""初始化 Tushare 连接"""
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try:
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ts.set_token(self._token)
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self.pro = ts.pro_api()
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# 简单测试连接
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self.pro.trade_cal(exchange="DCE", start_date="20240101", end_date="20240105")
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self._initialized = True
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return True
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except Exception as e:
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self._initialized = False
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raise e
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def _format_date(self, date_str: str) -> str:
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"""将 YYYY-MM-DD 转换为 YYYYMMDD"""
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return date_str.replace("-", "")
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def get_contract_list(self, exchange: Optional[str] = None) -> List[dict]:
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"""获取期货合约列表"""
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if not self._initialized:
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self.initialize()
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# Tushare 获取合约信息
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df = self.pro.fut_basic(
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exchange=exchange or "CFFEX", # 需要分别查询每个交易所
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fut_type="1", # 1=标准合约
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fut_series=""
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)
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results = []
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if df is not None and not df.empty:
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for _, row in df.iterrows():
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results.append({
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"symbol": row.get("symbol", ""),
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"exchange": self._map_exchange(row.get("exchange", "")),
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"name": row.get("name", ""),
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"product": row.get("underlying_symbol", ""),
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"multiplier": int(row.get("contract_multiplier", 10)) if row.get("contract_multiplier") else 10,
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"price_tick": float(row.get("price_tick", 0)) if row.get("price_tick") else None,
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"expire_date": self._parse_date(row.get("delist_date", "")),
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"is_active": row.get("list_status", "") == "L",
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})
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return results
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def _map_exchange(self, exchange: str) -> str:
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"""交易所代码映射"""
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mapping = {
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"CFFEX": "CFFEX",
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"SHFE": "SHFE",
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"DCE": "DCE",
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"CZCE": "ZCE", # Tushare 用 CZCE，我们统一用 ZCE
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"INE": "INE",
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"GFEX": "GFEX",
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}
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return mapping.get(exchange, exchange)
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def _parse_date(self, date_str: str) -> Optional[datetime]:
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"""解析日期字符串"""
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if not date_str:
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return None
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try:
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return datetime.strptime(str(date_str), "%Y%m%d")
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except Exception:
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return None
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def get_kline_daily(
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self,
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symbol: str,
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start_date: str,
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end_date: str
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) -> pd.DataFrame:
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"""获取日K线数据"""
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if not self._initialized:
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self.initialize()
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start = self._format_date(start_date)
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end = self._format_date(end_date)
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df = self.pro.fut_daily(
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ts_code=symbol,
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start_date=start,
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end_date=end
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)
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if df is None or df.empty:
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return pd.DataFrame()
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# 统一列名
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df = df.rename(columns={
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"trade_date": "trade_date",
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"open": "open",
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"high": "high",
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"low": "low",
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"close": "close",
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"vol": "volume",
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"amount": "turnover",
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"oi": "open_interest",
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"settle": "settle",
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"pre_settle": "pre_settle",
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})
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df["trade_date"] = pd.to_datetime(df["trade_date"])
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return df[["trade_date", "open", "high", "low", "close", "volume", "turnover", "open_interest", "settle", "pre_settle"]]
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def get_kline_weekly(
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self,
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symbol: str,
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start_date: str,
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end_date: str
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) -> pd.DataFrame:
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"""
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获取周K线数据
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Tushare 没有直接的周K接口，通过日K聚合
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"""
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daily_df = self.get_kline_daily(symbol, start_date, end_date)
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if daily_df.empty:
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return pd.DataFrame()
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# 按周聚合
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daily_df = daily_df.set_index("trade_date")
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weekly = daily_df.resample("W-FRI").agg({
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"open": "first",
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"high": "max",
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"low": "min",
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"close": "last",
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"volume": "sum",
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"turnover": "sum",
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"open_interest": "last",
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}).dropna()
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weekly = weekly.reset_index()
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weekly = weekly.rename(columns={"trade_date": "trade_date"})
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return weekly
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def get_kline_intraday(
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self,
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symbol: str,
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period: str,
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start_date: str,
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end_date: str
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) -> pd.DataFrame:
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"""
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获取分钟级K线数据
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Tushare 的 fut_mins 接口
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"""
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if not self._initialized:
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self.initialize()
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# Tushare fut_mins 接口
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start = self._format_date(start_date)
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end = self._format_date(end_date)
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# 分钟数映射
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freq_map = {"5m": "5", "15m": "15", "30m": "30", "60m": "60"}
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freq = freq_map.get(period, "5")
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try:
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df = self.pro.fut_mins(
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ts_code=symbol,
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freq=freq,
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start_date=start,
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end_date=end
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)
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except Exception:
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# 部分交易所可能不支持分钟数据
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return pd.DataFrame()
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if df is None or df.empty:
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return pd.DataFrame()
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df = df.rename(columns={
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"trade_time": "trade_time",
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"open": "open",
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"high": "high",
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"low": "low",
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"close": "close",
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"vol": "volume",
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"amount": "turnover",
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"oi": "open_interest",
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})
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df["trade_time"] = pd.to_datetime(df["trade_time"])
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return df[["trade_time", "open", "high", "low", "close", "volume", "turnover", "open_interest"]]
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