"""K 线周期聚合：日线 -> 周/月/年。

生产环境用 TimescaleDB Continuous Aggregates 在库里预物化（性能）；
MVP 在应用层用 pandas resample 即可，逻辑等价、便于切换。

OHLCV 聚合规则：开=周期内首根开、高=最高、低=最低、收=末根收、量=求和。
成交额/换手率为名义量：求和（全缺则保持 None，不伪造 0）。
"""
from __future__ import annotations

import pandas as pd

from ..domain import Bar

# pandas resample 规则（周一为周首；月/年以首日对齐）
_RULES = {"1w": "W-MON", "1M": "MS", "1y": "YS"}

# 各周期的"年交易日数"（用于夏普等指标的年化）
_BARS_PER_YEAR = {"1d": 252, "1w": 52, "1M": 12, "1y": 1}


def bars_per_year(timeframe: str) -> int:
    return _BARS_PER_YEAR.get(timeframe, 252)


def _sum_or_none(s: pd.Series):
    """求和；全为 NaN 返回 None（部分缺失则忽略缺失项求和）。"""
    if s.isna().all():
        return None
    return float(s.sum())


def resample_bars(bars: list[Bar], timeframe: str) -> list[Bar]:
    """把日线 bars 聚合为目标周期；日线或未知周期原样返回。"""
    if not bars or timeframe in ("1d", "d", "day", "", None):
        return bars
    rule = _RULES.get(timeframe)
    if rule is None:
        return bars

    df = pd.DataFrame(
        [{"ts": b.ts, "open": b.open, "high": b.high, "low": b.low, "close": b.close,
          "volume": b.volume,
          "amount": b.amount if b.amount is not None else float("nan"),
          "turnover": b.turnover if b.turnover is not None else float("nan")}
         for b in bars]
    ).set_index("ts").sort_index()

    agg = (
        df.resample(rule)
        .agg({"open": "first", "high": "max", "low": "min", "close": "last",
              "volume": "sum", "amount": _sum_or_none, "turnover": _sum_or_none})
        .dropna(subset=["open"])
    )

    return [
        Bar(
            ts=ts.to_pydatetime(),
            open=float(row["open"]),
            high=float(row["high"]),
            low=float(row["low"]),
            close=float(row["close"]),
            volume=float(row["volume"]),
            amount=row["amount"] if row["amount"] == row["amount"] else None,  # NaN -> None
            turnover=row["turnover"] if row["turnover"] == row["turnover"] else None,
        )
        for ts, row in agg.iterrows()
    ]