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 vnpy DB 读取（统一数据源）。
    parquet 仅作原始备份，不再读取。返回类型保持 pd.DataFrame（cta_engine 消费不变）。

    Args:
        code: 指数代码，带交易所前缀，如 "sh000300"（沪深300）、"sz399001"（深证成指）
        start: 起始日期（str "YYYY-MM-DD" / date / datetime）
        end: 结束日期（str "YYYY-MM-DD" / date / datetime）
        cfg: 数据配置对象

    Returns:
        pd.DataFrame: date/open/high/low/close/volume 列；无数据返回空 DataFrame。
    """
    from vnpy.trader.database import get_database  # lazy：避免模块 import 依赖数据库驱动

    # 前缀解析交易所（指数不能用 guess_exchange：000300 以 0 开头会被误判成 SZSE，
    # 但 000300 实际属于 SSE）。sh → SSE，sz → SZSE。
    symbol = code[2:]
    exchange = Exchange.SSE if code.startswith("sh") else Exchange.SZSE

    # 日期归一化：str → parse, date → combine, datetime → as-is
    def _to_dt(s, is_start: bool) -> datetime:
        if isinstance(s, datetime):
            return s
        if isinstance(s, date):
            return datetime.combine(s, datetime.min.time() if is_start else datetime.max.time())
        return datetime.strptime(s, "%Y-%m-%d")

    start_dt = _to_dt(start, True)
    end_dt = _to_dt(end, False)

    # 配置 vnpy DB（与 read_db_daily 同模式）
    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()
    bars = db.load_bar_data(
        symbol=symbol,
        exchange=exchange,
        interval=Interval.DAILY,
        start=start_dt,
        end=end_dt,
    )

    if not bars:
        return pd.DataFrame(columns=["date", "open", "high", "low", "close", "volume"])

    df = pd.DataFrame([{
        "date": b.datetime,
        "open": b.open_price,
        "high": b.high_price,
        "low": b.low_price,
        "close": b.close_price,
        "volume": b.volume,
    } for b in bars])
    return df.sort_values("date").reset_index(drop=True)