過去の株価推移をPythonで取得する：yfinance/Stooq で日経225・TOPIX・個別株を社内分析に重ねる

!pip install -q yfinanceimport yfinance as yf
# 日経225 のヒストリカル（過去5年）n225 = yf.Ticker("^N225").history(period="5y")print(n225.tail())print(f"\n期間: {n225.index.min().date()} 〜 {n225.index.max().date()}, {len(n225)} 営業日")

import matplotlib.pyplot as pltimport japanize_matplotlib  # noqa: F401
n225["MA25"]  = n225["Close"].rolling(25).mean()n225["MA200"] = n225["Close"].rolling(200).mean()
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(12, 7), sharex=True,                                gridspec_kw={"height_ratios": [3, 1]})ax1.plot(n225.index, n225["Close"],  label="終値", linewidth=1.2, color="#1f77b4")ax1.plot(n225.index, n225["MA25"],   label="25日 MA", color="#ff7f0e", linewidth=1)ax1.plot(n225.index, n225["MA200"],  label="200日 MA", color="#d62728", linewidth=1.2)ax1.set_ylabel("価格（円）"); ax1.set_title("日経225 の終値と移動平均")ax1.legend(); ax1.grid(alpha=0.3)
ax2.bar(n225.index, n225["Volume"], color="#999", width=1.0)ax2.set_ylabel("出来高"); ax2.grid(alpha=0.3)plt.tight_layout(); plt.show()

import yfinance as yfimport pandas as pdimport matplotlib.pyplot as pltimport japanize_matplotlib  # noqa: F401
tickers = {"7203.T": "トヨタ", "7201.T": "日産", "7267.T": "ホンダ"}df = yf.download(list(tickers.keys()), period="3y", auto_adjust=True)["Close"]
# 開始日=100 で正規化（リターン比較）norm = df / df.iloc[0] * 100
fig, ax = plt.subplots(figsize=(11, 5))for code, label in tickers.items():    ax.plot(norm.index, norm[code], label=label, linewidth=1.5)ax.set_title("自動車3社の株価推移（開始日=100で正規化）")ax.set_ylabel("正規化リターン"); ax.axhline(100, color="gray", linestyle="--", alpha=0.4)ax.legend(); ax.grid(alpha=0.3); plt.tight_layout(); plt.show()

!pip install -q pandas-datareaderimport pandas_datareader.data as webimport datetime as dt
end   = dt.date.today()start = end - dt.timedelta(days=365 * 5)
# Stooq のシンボル: 日経225 = ^nkx, 米ドル円 = jpyusd（逆数）n225 = web.DataReader("^nkx",   "stooq", start, end).sort_index()usd  = web.DataReader("usdjpy", "stooq", start, end).sort_index()
print("日経225:", n225.tail(3))print("\nUSD/JPY:", usd.tail(3))

# 自社売上（仮データ：実際は社内 DWH から取得）sales_monthly = pd.DataFrame({    "month": pd.date_range("2020-01", "2025-12", freq="MS"),    "sales": [...],  # 月次売上}).set_index("month")
# TOPIX 月次（^TPX は yfinance ではダメなので Stooq か日経のミラーを使う）topix = yf.Ticker("^TPX").history(period="6y")["Close"].resample("MS").last()
# 月次変化率df = pd.DataFrame({"sales": sales_monthly["sales"], "topix": topix})df_pct = df.pct_change().dropna()
fig, ax = plt.subplots(figsize=(7, 6))ax.scatter(df_pct["topix"]*100, df_pct["sales"]*100, alpha=0.6, s=50)ax.set_xlabel("TOPIX 月次変化率（%）"); ax.set_ylabel("売上 月次変化率（%）")corr = df_pct["topix"].corr(df_pct["sales"])ax.set_title(f"TOPIX × 自社売上の月次相関（r={corr:.2f}）")ax.axhline(0, color="gray", linewidth=0.5); ax.axvline(0, color="gray", linewidth=0.5)ax.grid(alpha=0.3); plt.tight_layout(); plt.show()

!pip install -q mplfinanceimport mplfinance as mpf
n225_6m = yf.Ticker("^N225").history(period="6mo")mpf.plot(n225_6m, type="candle", style="yahoo", volume=True,         mav=(5, 25), title="日経225（直近6ヶ月）",         figsize=(12, 7))