自分だけのウォッチリスト分析テンプレート

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"""watchlist_template.py
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自分のウォッチリストに対して、価値評価・リスク・リターン指標をまとめて計算する。
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"""
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from __future__ import annotations
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import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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WATCHLIST_PATH = "watchlist.csv"      # Code, label
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PRICES_PATH = "prices.csv"
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STATEMENTS_PATH = "statements.csv"
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WINDOW = 252
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def latest_close(prices: pd.DataFrame) -> pd.DataFrame:
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    p = prices.sort_values(["Code", "Date"])
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    return p.groupby("Code", as_index=False).tail(1)[["Code", "C"]]
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def risk_return_metrics(prices: pd.DataFrame) -> pd.DataFrame:
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    p = prices.sort_values(["Code", "Date"]).copy()
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    p["log_ret"] = np.log(p["C"] / p.groupby("Code")["C"].shift(1))
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    p = p.dropna(subset=["log_ret"])
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    p = p.groupby("Code", group_keys=False).tail(WINDOW)
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    grouped = p.groupby("Code")
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    daily_mean = grouped["log_ret"].mean()
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    daily_std = grouped["log_ret"].std(ddof=1)
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    # 最大ドローダウン
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    def mdd(series: pd.Series) -> float:
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        eq = (1 + series).cumprod()
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        return ((eq - eq.cummax()) / eq.cummax()).min()
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    mdd_val = grouped["log_ret"].apply(mdd)
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    out = pd.DataFrame({
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        "ret_annual": daily_mean * 252,
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        "vol_annual": daily_std * np.sqrt(252),
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        "max_drawdown": mdd_val,
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    }).reset_index()
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    return out
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def fundamental_metrics(stmt: pd.DataFrame) -> pd.DataFrame:
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    # statements の銘柄コード列は J-Quants と同じく Code
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    s = stmt.sort_values(["Code", "DiscDate"])
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    latest = s.groupby("Code", as_index=False).tail(1)
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    return latest[["Code", "FEPS", "FDivAnn",
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                   "BPS", "NP", "Eq"]]
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def main() -> None:
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    watch = pd.read_csv(WATCHLIST_PATH)
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    prices = pd.read_csv(PRICES_PATH, parse_dates=["Date"])
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    stmt = pd.read_csv(STATEMENTS_PATH, parse_dates=["DiscDate"])
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    prices = prices[prices["Code"].isin(watch["Code"])]
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    stmt = stmt[stmt["Code"].isin(watch["Code"])]
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    closes = latest_close(prices)
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    risk = risk_return_metrics(prices)
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    fund = fundamental_metrics(stmt)
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    # watch / prices は Code、statements 由来の fund は Code。結合時に対応づける
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    df = (
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        watch.merge(closes, on="Code")
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        .merge(risk, on="Code", how="left")
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        .merge(fund, on="Code", how="left")
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    )
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    df["per"] = df["C"] / df["FEPS"]
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    df["pbr"] = df["C"] / df["BPS"]
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    df["dividend_yield"] = df["FDivAnn"] / df["C"]
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    df["roe"] = df["NP"] / df["Eq"]
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    cols = ["Code", "label", "C", "per", "pbr", "dividend_yield", "roe",
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            "ret_annual", "vol_annual", "max_drawdown"]
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    df = df[cols].round(4)
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    df.to_csv("watchlist_metrics.csv", index=False)
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    print(df)
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    # リスク・リターン散布図
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    fig, ax = plt.subplots(figsize=(7, 5))
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    ax.scatter(df["vol_annual"] * 100, df["ret_annual"] * 100, s=40)
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    for _, row in df.iterrows():
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        ax.annotate(row["label"], (row["vol_annual"] * 100, row["ret_annual"] * 100),
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                    fontsize=8, xytext=(4, 4), textcoords="offset points")
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    ax.set_xlabel("Volatility (annualized, %)")
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    ax.set_ylabel("Return (annualized, %)")
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    ax.set_title("Watchlist — risk vs return")
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    ax.axhline(0, color="gray", linewidth=0.8)
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    ax.grid(alpha=0.3)
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    plt.tight_layout()
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    plt.savefig("report_chart.png", dpi=120)
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    plt.close(fig)
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if __name__ == "__main__":
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    main()

   Code  label    C   per    pbr  dividend_yield   roe  ret_annual  vol_annual  max_drawdown
0  XXXX  AAA   2925.0  16.20  1.42          0.0240  0.085      0.0820      0.2210       -0.1820
1  XXXX  BBB   1820.0   9.85  0.92          0.0410  0.094     -0.0125      0.2540       -0.2510
2  XXXX  CCC   4210.0  22.45  3.10          0.0145  0.142      0.1310      0.2620       -0.2150
3  XXXX  DDD   3875.0  18.30  2.05          0.0205  0.115      0.0625      0.2050       -0.1690
4  XXXX  EEE   1245.0  11.10  1.20          0.0382  0.108      0.0915      0.1880       -0.1450

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def to_markdown_report(df: pd.DataFrame) -> str:
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    rows = df.to_markdown(index=False)
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    return f"""# Watchlist report
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{rows}
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## メモ
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- 集計期間: 直近 {WINDOW} 営業日
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- PER・PBR・配当利回りは予想値ベース、ROE は当期実績ベース
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"""
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with open("report.md", "w", encoding="utf-8") as f:
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    f.write(to_markdown_report(df))