低股價淨值比＋F-Score＋營收成長

"""低股價淨值比＋F-Score＋營收成長：/strategies/f-score-low-pb-revenue 的數字來源。

/blog/f-score-indicator「進階版：低股價淨值比＋F-Score＋營收成長」一節的程式：
- 股價淨值比最低的 500 檔（股價淨值比 > 0）
- F-Score 至少 7 分（9 項：ROE > 0、營業現金流 > 0、ROE 比去年同季高、營業現金流 ÷ 總資產 > ROE、
  負債比率下降、流動比率上升、普通股股本沒有增加、毛利率上升、總資產週轉上升，都和去年同季比）
- 最新一個月營收比去年同月成長
- 2014-06 起每月底換股、等權，sim() 預設手續費 0.1425% 與證交稅 0.3%
- 財報用各公司實際公布日，月營收用每月 10 日公布期限

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 = "低股價淨值比＋F-Score＋營收成長"
START = "2014-06-01"
LOW_PB_N = 500
F_SCORE_MIN = 7


def f_score():
    """9 項財報檢查各得 1 分，每項都和 4 季前（去年同季）比。"""
    roe = data.get("fundamental_features:ROE稅後")
    ocf = data.get("financial_statement:營業活動之淨現金流入_流出")
    assets = data.get("financial_statement:資產總額")
    revenue = data.get("financial_statement:營業收入淨額")
    debt_ratio = data.get("fundamental_features:負債比率")
    current_ratio = data.get("fundamental_features:流動比率")
    capital = data.get("financial_statement:普通股股本")
    gross_margin = data.get("financial_statement:營業毛利") / revenue
    turnover = revenue / assets
    checks = [
        roe > 0,
        ocf > 0,
        roe > roe.shift(4),
        ocf / assets > roe / 100,
        debt_ratio < debt_ratio.shift(4),
        current_ratio > current_ratio.shift(4),
        capital <= capital.shift(4),
        gross_margin > gross_margin.shift(4),
        turnover > turnover.shift(4),
    ]
    return sum(c.astype(int) for c in checks)


def build_report():
    pb = data.get("price_earning_ratio:股價淨值比")
    revenue_yoy = data.get("monthly_revenue:去年同月增減(%)")
    score = f_score().index_str_to_date()
    position = pb[pb > 0].is_smallest(LOW_PB_N) & (score >= F_SCORE_MIN) & (revenue_yoy > 0)
    return sim(position.loc[START:], resample="M", 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%}")
    # 前後兩段：期間對半切，各自和同一段的 0050 含息比日夏普
    mid = creturn.index[0] + (creturn.index[-1] - creturn.index[0]) / 2
    for label, part in [("前段", slice(None, mid)), ("後段", slice(mid, None))]:
        s, b = curve_stats(creturn.loc[part]), curve_stats(bench.loc[part])
        print(f"{label}：策略日夏普 {s['daily_sharpe']:.2f}，0050 含息 {b['daily_sharpe']:.2f}")

    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/f-score-indicator/low-pb-revenue/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()