sanguo_vnpy_v2/sanguo_data/datareader.py

import sys
import os
from pathlib import Path

# Add real vnpy source code to sys.path
_VNPY_SRC = os.path.join(os.path.dirname(__file__), "..", "vnpy_v4.4.0")
_VNPY_SRC = os.path.abspath(_VNPY_SRC)
if _VNPY_SRC not in sys.path:
    sys.path.insert(0, _VNPY_SRC)

import pandas as pd
from datetime import datetime, date
from vnpy.trader.object import BarData
from vnpy.trader.constant import Exchange, Interval
from vnpy.trader.setting import SETTINGS

def read_parquet_daily(symbol: str, start: str, end: str, cfg, dir_key: str = "daily_dir") -> list[BarData]:
    daily_dir = Path(cfg.data_paths[dir_key])
    start_dt = datetime.strptime(start, "%Y-%m-%d")
    end_dt = datetime.strptime(end, "%Y-%m-%d")
    bars: list[BarData] = []
    prefix = "sh" if guess_exchange(symbol) == Exchange.SSE else "sz"
    for year in range(start_dt.year, end_dt.year + 1):
        f = daily_dir / str(year) / f"{prefix}{symbol}_daily.parquet"
        if not f.exists():
            continue
        df = pd.read_parquet(f)
        for _, row in df.iterrows():
            d = pd.to_datetime(row["date"])
            if start_dt <= d <= end_dt:
                bars.append(_row_to_bar(symbol, row, Interval.DAILY))
    return bars

def _row_to_bar(symbol: str, row, interval: Interval) -> BarData:
    return BarData(
        symbol=symbol,
        exchange=guess_exchange(symbol),  # Task 4 已改为 guess_exchange
        datetime=pd.to_datetime(row["date"]).to_pydatetime(),
        interval=interval,
        open_price=float(row["open"]),
        high_price=float(row["high"]),
        low_price=float(row["low"]),
        close_price=float(row["close"]),
        volume=float(row["volume"]),
        gateway_name="DATA",
    )


def guess_exchange(symbol: str) -> Exchange:
    """按代码前缀判断交易所：6/68/5x→SSE，0/3/15x→SZSE"""
    if symbol.startswith(("60", "68", "51", "56", "58")):
        return Exchange.SSE
    if symbol.startswith(("00", "30", "15")):
        return Exchange.SZSE
    return Exchange.SSE


def read_db_daily(symbol: str, start: str, end: str, cfg) -> list[BarData]:
    from vnpy.trader.database import get_database  # lazy：避免模块 import 依赖数据库驱动
    # Configure vnpy database SETTINGS before calling get_database()
    SETTINGS["database.name"] = "sqlite"
    vnpy_db = getattr(cfg, "data_paths", {}).get("vnpy_db")
    if not vnpy_db:
        raise RuntimeError("config 缺 data_paths.vnpy_db — 检查 backtest.yaml 初始化")
    SETTINGS["database.database"] = vnpy_db

    db = get_database()
    start_dt = datetime.strptime(start, "%Y-%m-%d")
    end_dt = datetime.strptime(end, "%Y-%m-%d")
    return db.load_bar_data(
        symbol=symbol,
        exchange=guess_exchange(symbol),
        interval=Interval.DAILY,
        start=start_dt,
        end=end_dt,
    )


def read_parquet_15min(symbol: str, start: str, end: str, cfg, dir_key: str = "minute_15_dir") -> list[BarData]:
    """读 15min parquet（NAS /volume1/stock/minute_kline/15min/sh{symbol}_15min.parquet）。"""
    minute_dir = Path(cfg.data_paths[dir_key])
    start_dt = datetime.strptime(start, "%Y-%m-%d")
    end_dt = datetime.strptime(end, "%Y-%m-%d")
    prefix = "sh" if guess_exchange(symbol) == Exchange.SSE else "sz"
    f = minute_dir / f"{prefix}{symbol}_15min.parquet"
    if not f.exists():
        return []
    df = pd.read_parquet(f)
    # baostock 15min 有完整时分 datetime 列；旧数据用 date 列
    time_col = ("datetime" if "datetime" in df.columns
                else ("date" if "date" in df.columns else df.columns[0]))
    bars: list[BarData] = []
    for _, row in df.iterrows():
        d = pd.to_datetime(row[time_col])
        if start_dt <= d <= end_dt:
            bars.append(BarData(
                symbol=symbol,
                exchange=guess_exchange(symbol),
                datetime=d.to_pydatetime(),
                interval=Interval.MINUTE,
                open_price=float(row["open"]),
                high_price=float(row["high"]),
                low_price=float(row["low"]),
                close_price=float(row["close"]),
                volume=float(row["volume"]),
                gateway_name="DATA",
            ))
    return bars


def read_index_daily(code: str, start, end, cfg) -> pd.DataFrame:
    """
    读指数日线数据（sh000300/sz399001 等），从 dbbardata 读取（2026-08-01 切自 vnpy DbBarData）。

    指数点位已由 sina_index_eod 灌入 dbbardata exchange=SSE（16 个中证指数含 000300 沪深300）。
    中证指数须用 sh 前缀（点位在 SSE）；深证指数 sz 前缀。cta_engine benchmark 用 sh000300。
    切 dbbardata 后不再调 get_database() → 消除覆写 vnpy SETTINGS 副作用（cta_engine.py:73/199）。

    Args:
        code: 指数代码，带交易所前缀，如 "sh000300"（沪深300）、"sz399001"（深证成指）
        start/end: 日期（str "YYYY-MM-DD" / date / datetime）
        cfg: 数据配置对象（data_paths.vnpy_db 指向含 dbbardata 表的 quant_trading.db）

    Returns:
        pd.DataFrame: date/open/high/low/close/volume 列（date 为 Timestamp）；无数据返回空 DataFrame。
    """
    import sqlite3

    # 前缀解析交易所（指数不能用 guess_exchange：000300 以 0 开头会误判 SZSE，但属 SSE）。
    symbol = code[2:]
    exchange = "SSE" if code.startswith("sh") else "SZSE"

    # 日期归一化 -> 'YYYY-MM-DD'
    def _to_str(s) -> str:
        if isinstance(s, datetime):
            return s.strftime("%Y-%m-%d")
        if isinstance(s, date):
            return s.strftime("%Y-%m-%d")
        return str(s)[:10]

    start_str = _to_str(start)
    end_str = _to_str(end)

    db_path = getattr(cfg, "data_paths", {}).get("vnpy_db")
    if not db_path:
        raise RuntimeError("config 缺 data_paths.vnpy_db — 检查 backtest.yaml 初始化")

    conn = sqlite3.connect(db_path, timeout=30)
    conn.execute("PRAGMA busy_timeout = 30000")
    try:
        # substr(datetime,1,10) 比日期规避混合格式（有纯日期有带时间，同 provider 模式）
        df = pd.read_sql(
            "SELECT datetime, open_price, high_price, low_price, close_price, volume "
            "FROM dbbardata WHERE symbol=? AND exchange=? AND interval='d' "
            "AND substr(datetime,1,10)>=? AND substr(datetime,1,10)<=? ORDER BY datetime",
            conn, params=(symbol, exchange, start_str, end_str),
        )
    finally:
        conn.close()

    if df.empty:
        return pd.DataFrame(columns=["date", "open", "high", "low", "close", "volume"])

    df = df.rename(columns={
        "datetime": "date", "open_price": "open", "high_price": "high",
        "low_price": "low", "close_price": "close",
    })
    df["date"] = pd.to_datetime(df["date"], format="mixed")
    return df[["date", "open", "high", "low", "close", "volume"]].reset_index(drop=True)