"""營收動能分散版（前 40 檔）：跑到最新資料的版本（/strategies/risk-of-diversification 的數字來源）。

條件與 /blog/risk-of-diversification 附的 strategy.py 相同，只是不截斷結束日，起點跟本文一樣設在 2015-01-01：
- 收盤價同時站上 20、60、120 日均線，近 3 個月平均營收高於近 12 個月平均
- 20 日均量大於 500 張，依近 5 日平均日報酬取前 40 檔
- 每月 11 日後換股、單檔停損 10%，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 = "營收動能分散版（前 40 檔）"
START = "2015-01-01"
MOMENTUM_DAYS = 5
N_STOCKS = 40

def build_report():
    close = data.get("price:收盤價")
    volume = data.get("price:成交股數")
    rev = data.get("monthly_revenue:當月營收")

    momentum = (close / close.shift() - 1).rolling(MOMENTUM_DAYS).mean()
    trend = (close > close.average(60)) & (close > close.average(20)) & (close > close.average(120))
    rev_up = rev.average(3) > rev.average(12)
    liquid = volume.average(20) > 500 * 1000
    position = momentum[trend & rev_up & liquid].is_largest(N_STOCKS).loc[START:]
    return sim(position, resample="M", resample_offset="11D", stop_loss=0.1,
               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()
    # 0050 要在策略設定股票池之前取得
    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/risk-of-diversification/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()