"""评估股票池:dbbardata 分块直读 → AlphaLab 格式 polars DataFrame.

口径(spec):主板+创业板(前缀 60/00/30,剔科创68/北交/ETF),退市股保留,
120 交易日预热(次新 + 因子窗口)。不做 ST 过滤(无 point-in-time 名称史,
当前名回溯过滤=前视偏差,比不过滤更糟)。
"""
import random
import sqlite3
from datetime import datetime, timedelta

import polars as pl

STOCK_PREFIXES = ("60", "00", "30")
WARMUP_BARS = 120
_CHUNK = 300          # 分块 IN 查询每块 symbol 数(控瞬时内存)
_LOOKBACK_DAYS = 300  # start 前缓冲(覆盖最大 60 日窗口 + 节假日)
_FORWARD_DAYS = 45    # end 后缓冲(覆盖 10 日前瞻收益)

_COLS = "symbol, exchange, datetime, volume, turnover, open_interest, open_price, high_price, low_price, close_price"


def load_universe_bars(
    vnpy_db: str,
    start: str,
    end: str,
    symbols: list[str] | None = None,
    limit: int | None = None,
) -> pl.DataFrame:
    """读评估窗(含前后缓冲)全A日线 → AlphaLab 格式(含 vwap/bar_idx)."""
    lookback_start = (
        datetime.strptime(start, "%Y-%m-%d") - timedelta(days=_LOOKBACK_DAYS)
    ).strftime("%Y-%m-%d")
    forward_end = (
        datetime.strptime(end, "%Y-%m-%d") + timedelta(days=_FORWARD_DAYS)
    ).strftime("%Y-%m-%d")

    if symbols is None:
        conn = sqlite3.connect(vnpy_db, timeout=60)
        try:
            # 无过滤 DISTINCT symbol 走 (symbol,...) 前导索引顺序流式扫——
            # 带 WHERE(interval/datetime/LIKE)的版本会退化为 26G 全表扫(NAS 实测>5min)。
            # 前缀在 Python 侧滤;interval='d'/窗口过滤由下方分块数据查询天然承担
            # (无日线数据的 symbol 返回 0 行,不进最终 df,语义不变)。
            cur = conn.execute("SELECT DISTINCT symbol FROM dbbardata")
            symbols = [r[0] for r in cur if str(r[0]).startswith(STOCK_PREFIXES)]
        finally:
            conn.close()

    if not symbols:
        return _empty_alpha_df()
    if limit is not None and limit < len(symbols):
        symbols = sorted(random.Random(42).sample(symbols, limit))

    chunks: list[pl.DataFrame] = []
    for i in range(0, len(symbols), _CHUNK):
        part = symbols[i : i + _CHUNK]
        ph = ",".join("?" * len(part))
        conn = sqlite3.connect(vnpy_db, timeout=30)
        conn.execute("PRAGMA busy_timeout=30000")
        try:
            cur = conn.execute(
                f"SELECT {_COLS} FROM dbbardata "
                f"WHERE interval='d' AND datetime>=? AND datetime<=? AND symbol IN ({ph})",
                (lookback_start, forward_end, *part),
            )
            rows = cur.fetchall()
        finally:
            conn.close()
        if rows:
            chunks.append(pl.DataFrame(
                rows,
                schema={"symbol": pl.Utf8, "exchange": pl.Utf8, "dt": pl.Utf8,
                        "volume": pl.Float64, "turnover": pl.Float64, "open_interest": pl.Float64,
                        "open": pl.Float64, "high": pl.Float64, "low": pl.Float64, "close": pl.Float64},
                orient="row",
            ))
    if not chunks:
        return _empty_alpha_df()

    df = pl.concat(chunks)
    df = (
        df.with_columns(
            pl.col("dt").str.slice(0, 10).str.to_datetime("%Y-%m-%d").alias("datetime"),
            (pl.col("symbol") + "." + pl.col("exchange")).alias("vt_symbol"),
            pl.when(pl.col("volume") > 0)
            .then(pl.col("turnover") / pl.col("volume"))
            .otherwise(None)
            .alias("vwap"),
        )
        .sort(["vt_symbol", "datetime"])
        .with_columns(pl.int_range(pl.len()).over("vt_symbol").alias("bar_idx"))
        .select(["vt_symbol", "datetime", "open", "high", "low", "close",
                 "volume", "turnover", "open_interest", "vwap", "bar_idx"])
    )
    return df


def evaluation_filter(df: pl.DataFrame, start: str, end: str) -> pl.DataFrame:
    """IC 评估行集:窗口内 + 预热期已过."""
    from datetime import datetime as _dt
    return df.filter(
        (pl.col("datetime") >= _dt.strptime(start, "%Y-%m-%d"))
        & (pl.col("datetime") <= _dt.strptime(end, "%Y-%m-%d"))
        & (pl.col("bar_idx") >= WARMUP_BARS)
    )


def _empty_alpha_df() -> pl.DataFrame:
    return pl.DataFrame(schema={
        "vt_symbol": pl.Utf8, "datetime": pl.Datetime,
        "open": pl.Float64, "high": pl.Float64, "low": pl.Float64, "close": pl.Float64,
        "volume": pl.Float64, "turnover": pl.Float64, "open_interest": pl.Float64,
        "vwap": pl.Float64, "bar_idx": pl.Int64,
    })