"""複合動能＋品質（8 輪迭代最終版）：跑到最新資料的版本（/strategies/ai-prompt-backtest-strategy 的數字來源）。

條件與 /blog/ai-prompt-backtest-strategy 文中「8 輪迭代最終策略程式碼」相同，只是不截斷結束日：
- 近 3 個月（63 日）與近 6 個月（126 日）漲幅都大於 0
- ROE 大於 10%、月營收年增大於 0、股價大於 10 元
- 依 6 個月漲幅取前 10 檔等權，每季換股，sim() 預設手續費 0.1425% 與證交稅 0.3%

統計口徑：日夏普 = 日報酬平均 / 標準差 × √252，不扣無風險利率；
0050 含息 = etl:adj_close 還原價買進持有，與策略同一段期間。
執行後會在目前目錄輸出 data.csv（淨值曲線）與 metrics.json（統計）。

    pip install finlab
    python strategy.py

finlab 需要資料時會自動引導登入，不需要在程式裡填 token。
"""

import json

import pandas as pd

import finlab
from finlab import data
from finlab.backtest import sim

STRATEGY_NAME = "複合動能＋品質（8 輪迭代最終版）"
SHORT_DAYS = 63   # 3 個月動能
LONG_DAYS = 126   # 6 個月動能，也是排名依據
TOP_N = 10


def build_report():
    close = data.get("price:收盤價")
    roe = data.get("fundamental_features:ROE稅後")
    revenue_yoy = data.get("monthly_revenue:去年同月增減(%)")

    momentum_3m = close / close.shift(SHORT_DAYS) - 1
    momentum_6m = close / close.shift(LONG_DAYS) - 1
    cond_momentum = (momentum_3m > 0) & (momentum_6m > 0)
    quality = (roe > 10) & (revenue_yoy > 0) & (close > 10) & cond_momentum

    position = momentum_6m[quality].is_largest(TOP_N)
    return sim(position, resample="Q", upload=False, name=STRATEGY_NAME)


def curve_stats(curve: pd.Series) -> dict:
    curve = curve / curve.iloc[0]
    daily = curve.pct_change().dropna()
    years = (curve.index[-1] - curve.index[0]).days / 365.25
    return {
        "cagr": curve.iloc[-1] ** (1 / years) - 1,
        "daily_sharpe": daily.mean() / daily.std() * 252 ** 0.5,
        "max_drawdown": (curve / curve.cummax() - 1).min(),
        "total_return": curve.iloc[-1] - 1,
    }


def main() -> None:
    finlab.login()
    bench_all = data.get("etl:adj_close")["0050"].dropna()
    report = build_report()

    # 從第一次有部位的那天起算，0050 含息用同一段期間
    creturn = report.creturn.dropna()
    first_move = (creturn != creturn.iloc[0]).argmax()
    creturn = creturn.iloc[max(first_move - 1, 0):]
    bench = bench_all.loc[creturn.index[0]:creturn.index[-1]]

    strategy_stats = curve_stats(creturn)
    bench_stats = curve_stats(bench)
    holdings = (report.position.fillna(0) != 0).sum(axis=1)
    strategy_stats["avg_holdings"] = holdings[holdings > 0].mean()

    start, end = creturn.index[0].date(), creturn.index[-1].date()
    print(f"期間 {start} 至 {end}")
    for label, s in [("策略", strategy_stats), ("0050 含息", bench_stats)]:
        print(f"{label}：年化 {s['cagr']:.2%}，日夏普 {s['daily_sharpe']:.2f}，最大回撤 {s['max_drawdown']:.2%}")

    curves = pd.DataFrame({STRATEGY_NAME: creturn / creturn.iloc[0], "0050含息": bench / bench.iloc[0]})
    curves.index.name = "date"
    curves.round(6).to_csv("data.csv")
    metrics = {
        "window": f"{start}/{end}",
        "star": STRATEGY_NAME,
        "variants": {STRATEGY_NAME: {k: round(float(v), 6) for k, v in strategy_stats.items()}},
        "benchmark_0050": {k: round(float(v), 6) for k, v in bench_stats.items()},
        "run_date": str(pd.Timestamp.today().date()),
        "finlab_version": finlab.__version__,
        "generated_by": "static/blog/ai-prompt-backtest-strategy/validation/strategy.py",
    }
    with open("metrics.json", "w", encoding="utf-8") as f:
        json.dump(metrics, f, ensure_ascii=False, indent=2)


if __name__ == "__main__":
    main()