リーク対策およびリスク更新を行った株価予測手法の概要

30営業日版
          import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import yfinance as yf

from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error, mean_absolute_error

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Input, LSTM, Dense, Dropout
from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau

# =========================
# 0) 再現性
# =========================
SEED = 42
np.random.seed(SEED)
tf.random.set_seed(SEED)

# =========================
# 日本語フォント（Mac対応）
# =========================
def set_japanese_font():
    mac_fonts = ['Hiragino Sans', 'AppleGothic', 'ヒラギノ角ゴシック']
    for f in mac_fonts:
        try:
            plt.rcParams['font.family'] = f
            return
        except Exception:
            continue
    print("⚠️ 日本語フォントが見つかりません（英数字のみ表示）。")

set_japanese_font()

# =========================
# 1) データ取得（end=Noneで最新まで）
# =========================
ticker = "7267.T"
start_date = "2020-01-01"
end_date = None  # ←最新まで

raw = yf.download(ticker, start=start_date, end=end_date, auto_adjust=False, progress=False)
if raw.empty:
    raise RuntimeError("株価データの取得に失敗しました（空）。")

def normalize_ohlcv(df: pd.DataFrame, ticker: str) -> pd.DataFrame:
    """
    yfinance が MultiIndex 列で返す場合でも、Open/High/Low/Close/Volume を単層に正規化する
    """
    df = df.copy()

    # MultiIndex -> 単層へ
    if isinstance(df.columns, pd.MultiIndex):
        # 例: ('Close','7267.T') のような形を想定
        if ticker in df.columns.get_level_values(-1):
            df = df.xs(ticker, axis=1, level=-1)
        else:
            # 念のため最初のティッカーに落とす
            first = df.columns.get_level_values(-1)[0]
            df = df.xs(first, axis=1, level=-1)

    # 必須列チェック＋Series化
    need = ["Open", "High", "Low", "Close", "Volume"]
    for col in need:
        if col not in df.columns:
            raise RuntimeError(f"必要な列 {col} が見つかりません。現在の列: {list(df.columns)}")
        if isinstance(df[col], pd.DataFrame):
            df[col] = df[col].iloc[:, 0]
        df[col] = pd.to_numeric(df[col], errors="coerce")

    df = df.dropna(subset=["Close"]).copy()
    return df

df = normalize_ohlcv(raw, ticker)

# =========================
# 2) テクニカル計算（学習用：履歴分）
#    EMA12/EMA26 -> MACD -> RSI(14)
# =========================
df["EMA12"] = df["Close"].ewm(span=12, adjust=False).mean()
df["EMA26"] = df["Close"].ewm(span=26, adjust=False).mean()
df["MACD"] = df["EMA12"] - df["EMA26"]
df["Signal"] = df["MACD"].ewm(span=9, adjust=False).mean()

delta = df["Close"].diff()
gain = delta.where(delta > 0, 0.0).rolling(14).mean()
loss = (-delta.where(delta < 0, 0.0)).rolling(14).mean()
rs = gain / (loss + 1e-12)
df["RSI"] = 100 - (100 / (1 + rs))

df["log_close"] = np.log(df["Close"])
df["log_ret"] = df["log_close"].diff()
df["y_next_logret"] = df["log_ret"].shift(-1)

df = df.dropna().copy()

# =========================
# 3) 特徴量（リークしないもののみ）
# =========================
df["ema12_ratio"] = df["EMA12"] / df["Close"] - 1.0
df["ema26_ratio"] = df["EMA26"] / df["Close"] - 1.0
df["rsi01"] = (df["RSI"] / 100.0).clip(0, 1)

feature_cols = ["log_ret", "ema12_ratio", "ema26_ratio", "MACD", "Signal", "rsi01"]
X_all = df[feature_cols].values.astype(np.float32)
y_all = df["y_next_logret"].values.astype(np.float32).reshape(-1, 1)
dates = df.index
close_series = df["Close"].copy()

# =========================
# 4) 時系列分割（リーク無し）
# =========================
test_ratio = 0.2
n_total = len(df)
split = int(n_total * (1 - test_ratio))

X_train_raw = X_all[:split]
y_train_raw = y_all[:split]
X_test_raw  = X_all[split:]
y_test_raw  = y_all[split:]

# =========================
# 5) スケーリング（必ずtrainでfit）
# =========================
x_scaler = MinMaxScaler()
X_train_scaled = x_scaler.fit_transform(X_train_raw)
X_test_scaled  = x_scaler.transform(X_test_raw)

y_scaler = MinMaxScaler()
y_train_scaled = y_scaler.fit_transform(y_train_raw).flatten()
y_test_scaled  = y_scaler.transform(y_test_raw).flatten()

# =========================
# 6) シーケンス化
# =========================
def make_seq_1step(Xs, ys, time_step):
    X_seq, y_seq = [], []
    for i in range(len(Xs) - time_step):
        X_seq.append(Xs[i:i + time_step])
        y_seq.append(ys[i + time_step])
    return np.array(X_seq), np.array(y_seq)

time_step = 60
X_train, y_train = make_seq_1step(X_train_scaled, y_train_scaled, time_step)

X_test_for_seq = np.vstack([X_train_scaled[-time_step:], X_test_scaled])
y_test_for_seq = np.concatenate([y_train_scaled[-time_step:], y_test_scaled])

X_test, y_test = make_seq_1step(X_test_for_seq, y_test_for_seq, time_step)
test_dates = dates[split:][0:len(y_test)]

# =========================
# 7) モデル
# =========================
def build_model(time_step, n_features):
    m = Sequential([
        Input(shape=(time_step, n_features)),
        LSTM(64, return_sequences=True),
        Dropout(0.2),
        LSTM(64),
        Dropout(0.2),
        Dense(32, activation="relu"),
        Dense(1)
    ])
    m.compile(optimizer=tf.keras.optimizers.Adam(1e-3), loss=tf.keras.losses.Huber())
    return m

model = build_model(time_step, X_train.shape[2])

es = EarlyStopping(monitor="val_loss", patience=12, restore_best_weights=True)
rlr = ReduceLROnPlateau(monitor="val_loss", factor=0.5, patience=5, min_lr=1e-5)

model.fit(
    X_train, y_train,
    validation_split=0.1,
    epochs=300,
    batch_size=32,
    callbacks=[es, rlr],
    verbose=0,
    shuffle=False
)

# =========================
# 8) テスト評価（真の精度）
# =========================
pred_test_scaled = model.predict(X_test, verbose=0).flatten()

pred_test_logret = y_scaler.inverse_transform(pred_test_scaled.reshape(-1, 1)).flatten()
true_test_logret = y_scaler.inverse_transform(y_test.reshape(-1, 1)).flatten()

rmse_ret = float(np.sqrt(mean_squared_error(true_test_logret, pred_test_logret)))
mae_ret  = float(mean_absolute_error(true_test_logret, pred_test_logret))
resid_std = float(np.std(true_test_logret - pred_test_logret))

prev_prices = close_series.shift(1).loc[test_dates].to_numpy()
true_prices = close_series.loc[test_dates].to_numpy()
pred_prices = prev_prices * np.exp(pred_test_logret)

rmse_price = float(np.sqrt(mean_squared_error(true_prices, pred_prices)))
mae_price  = float(mean_absolute_error(true_prices, pred_prices))

# =========================
# 9) 未来30営業日予測（リーク無し）
# =========================
alpha12 = 2 / (12 + 1)
alpha26 = 2 / (26 + 1)
alpha9  = 2 / (9 + 1)

horizon = 30
last_close = float(df["Close"].iloc[-1])
last_ema12 = float(df["EMA12"].iloc[-1])
last_ema26 = float(df["EMA26"].iloc[-1])
last_signal = float(df["Signal"].iloc[-1])

last14 = df["Close"].iloc[-15:]
d14 = last14.diff().dropna()
avg_gain = float(d14.where(d14 > 0, 0.0).mean())
avg_loss = float((-d14.where(d14 < 0, 0.0)).mean())

def compute_rsi_from_avgs(avg_gain, avg_loss):
    rs = avg_gain / (avg_loss + 1e-12)
    rsi = 100 - (100 / (1 + rs))
    return float(np.clip(rsi, 0, 100))

X_all_scaled = x_scaler.transform(X_all)
seq = X_all_scaled[-time_step:].copy()

future_prices = []
upper_band = []
lower_band = []

p = last_close

y_train_logret = y_train_raw.flatten()
lo, hi = np.quantile(y_train_logret, [0.01, 0.99])

for t in range(1, horizon + 1):
    x_in = seq.reshape(1, time_step, len(feature_cols))

    pred_scaled = float(model.predict(x_in, verbose=0)[0, 0])

    # ★MinMax外挿チェック（0〜1外に出ると暴走しやすい）
    raw_pred_scaled = pred_scaled
    # ★応急処置：0〜1にクリップ
    pred_scaled = float(np.clip(pred_scaled, 0.0, 1.0))

    pred_logret = float(y_scaler.inverse_transform(np.array([[pred_scaled]], dtype=np.float32))[0, 0])

    # ★追加①：学習分布に収める（分位クリップ）
    pred_logret = float(np.clip(pred_logret, lo, hi))

    # ★追加②：平均回帰（ドリフト抑制）
    pred_logret *= 0.7
    if t <= 5:
        print(f"[t={t}] raw_scaled={raw_pred_scaled:.4f} clipped={pred_scaled:.4f} logret={pred_logret:+.5f} p={p:.2f}")

       #★価格更新は if の外に出す（全日で必要）
    p = float(p * np.exp(pred_logret))
        # EMA/MACD/Signal 更新
    last_ema12 = float(alpha12 * p + (1 - alpha12) * last_ema12)
    last_ema26 = float(alpha26 * p + (1 - alpha26) * last_ema26)
    macd = float(last_ema12 - last_ema26)
    last_signal = float(alpha9 * macd + (1 - alpha9) * last_signal)

    # RSI更新
    prev_p = float(p / np.exp(pred_logret))
    change = p - prev_p
    gain_t = max(change, 0.0)
    loss_t = max(-change, 0.0)
    n = 14
    avg_gain = (avg_gain * (n - 1) + gain_t) / n
    avg_loss = (avg_loss * (n - 1) + loss_t) / n
    rsi01 = float(compute_rsi_from_avgs(avg_gain, avg_loss) / 100.0)

    # 特徴量を再構成
    ema12_ratio = float(last_ema12 / p - 1.0)
    ema26_ratio = float(last_ema26 / p - 1.0)

    feat = np.array([pred_logret, ema12_ratio, ema26_ratio, macd, last_signal, rsi01], dtype=np.float32)
    feat_scaled = x_scaler.transform(feat.reshape(1, -1))[0]

    seq = np.vstack([seq[1:], feat_scaled])

    future_prices.append(p)

    sigma_t = resid_std * np.sqrt(t)
    upper_band.append(p * np.exp(sigma_t))
    lower_band.append(p * np.exp(-sigma_t))

future_prices = np.array(future_prices, dtype=float)
upper_band = np.array(upper_band, dtype=float)
lower_band = np.array(lower_band, dtype=float)

future_dates = pd.bdate_range(df.index[-1] + pd.tseries.offsets.BDay(1), periods=horizon)

# =========================
# 10) 可視化＆保存
# =========================
plt.figure(figsize=(14, 8))
plt.plot(df.index, df["Close"].values, label="実株価", linewidth=1)
plt.plot(test_dates, pred_prices, label="テスト期間の予測（1日先）", linewidth=2)
plt.plot(future_dates, future_prices, marker="o", markersize=3.5, linewidth=2, label=f"未来{horizon}営業日予測")
plt.fill_between(future_dates, lower_band, upper_band, alpha=0.25, label="予測バンド（logret誤差±1σ）")

plt.title(f"{ticker} 真の未来予測（リーク無し）: テスト検証 + 未来{horizon}営業日")
plt.xlabel("日付")
plt.ylabel("株価（円）")
plt.grid(True)
plt.legend(loc="best")

out = os.path.expanduser("~/Desktop/7267_latest_30d.png")
plt.savefig(out, dpi=150)
plt.close()

# =========================
# 11) 結果表示
# =========================
print("========== 真のテスト精度（過去で検証） ==========")
print(f"期間: {test_dates[0].date()} 〜 {test_dates[-1].date()}（テスト{len(test_dates)}日）")
print(f"リターンRMSE: {rmse_ret:.6f}")
print(f"リターンMAE : {mae_ret:.6f}")
print(f"リターン誤差σ: {resid_std:.6f}（バンド用）")
print(f"価格RMSE（参考）: {rmse_price:.2f} 円")
print(f"価格MAE（参考） : {mae_price:.2f} 円")

print("\n========== 未来予測 ==========")
print(f"データ最終日（起点日）: {df.index[-1].date()}")
print(f"起点の終値: {last_close:.2f} 円")
print(f"予測期間: {future_dates[0].date()} 〜 {future_dates[-1].date()}（{horizon}営業日）")
print(f"最終日の予測: {future_prices[-1]:.2f} 円")
print(f"最終日のバンド: {lower_band[-1]:.2f} 円 〜 {upper_band[-1]:.2f} 円")
print(f"✅ 保存しました: {out}")
      

短期7営業日版
          import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import yfinance as yf

from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error, mean_absolute_error

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Input, LSTM, Dense, Dropout
from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau

# =========================
# 0) 再現性
# =========================
SEED = 42
np.random.seed(SEED)
tf.random.set_seed(SEED)

# =========================
# 日本語フォント（Mac対応）
# =========================
def set_japanese_font():
    mac_fonts = ['Hiragino Sans', 'AppleGothic', 'ヒラギノ角ゴシック']
    for f in mac_fonts:
        try:
            plt.rcParams['font.family'] = f
            return
        except Exception:
            continue
    print("⚠️ 日本語フォントが見つかりません（英数字のみ表示）。")

set_japanese_font()

# =========================
# 1) データ取得（end=Noneで最新まで）
# =========================
ticker = "7267.T"
start_date = "2020-01-01"
end_date = None  # ←最新まで

raw = yf.download(ticker, start=start_date, end=end_date, auto_adjust=False, progress=False)
if raw.empty:
    raise RuntimeError("株価データの取得に失敗しました（空）。")

def normalize_ohlcv(df: pd.DataFrame, ticker: str) -> pd.DataFrame:
    """
    yfinance が MultiIndex 列で返す場合でも、Open/High/Low/Close/Volume を単層に正規化する
    """
    df = df.copy()

    # MultiIndex -> 単層へ
    if isinstance(df.columns, pd.MultiIndex):
        # 例: ('Close','7267.T') のような形を想定
        if ticker in df.columns.get_level_values(-1):
            df = df.xs(ticker, axis=1, level=-1)
        else:
            # 念のため最初のティッカーに落とす
            first = df.columns.get_level_values(-1)[0]
            df = df.xs(first, axis=1, level=-1)

    # 必須列チェック＋Series化
    need = ["Open", "High", "Low", "Close", "Volume"]
    for col in need:
        if col not in df.columns:
            raise RuntimeError(f"必要な列 {col} が見つかりません。現在の列: {list(df.columns)}")
        if isinstance(df[col], pd.DataFrame):
            df[col] = df[col].iloc[:, 0]
        df[col] = pd.to_numeric(df[col], errors="coerce")

    df = df.dropna(subset=["Close"]).copy()
    return df

df = normalize_ohlcv(raw, ticker)

# =========================
# 2) テクニカル計算（学習用：履歴分）
#    EMA12/EMA26 -> MACD -> RSI(14)
# =========================
df["EMA12"] = df["Close"].ewm(span=12, adjust=False).mean()
df["EMA26"] = df["Close"].ewm(span=26, adjust=False).mean()
df["MACD"] = df["EMA12"] - df["EMA26"]
df["Signal"] = df["MACD"].ewm(span=9, adjust=False).mean()

delta = df["Close"].diff()
gain = delta.where(delta > 0, 0.0).rolling(14).mean()
loss = (-delta.where(delta < 0, 0.0)).rolling(14).mean()
rs = gain / (loss + 1e-12)
df["RSI"] = 100 - (100 / (1 + rs))

df["log_close"] = np.log(df["Close"])
df["log_ret"] = df["log_close"].diff()
df["y_next_logret"] = df["log_ret"].shift(-1)

df = df.dropna().copy()

# =========================
# 3) 特徴量（リークしないもののみ）
# =========================
df["ema12_ratio"] = df["EMA12"] / df["Close"] - 1.0
df["ema26_ratio"] = df["EMA26"] / df["Close"] - 1.0
df["rsi01"] = (df["RSI"] / 100.0).clip(0, 1)

feature_cols = ["log_ret", "ema12_ratio", "ema26_ratio", "MACD", "Signal", "rsi01"]
X_all = df[feature_cols].values.astype(np.float32)
y_all = df["y_next_logret"].values.astype(np.float32).reshape(-1, 1)
dates = df.index
close_series = df["Close"].copy()

# =========================
# 4) 時系列分割（リーク無し）
# =========================
test_ratio = 0.2
n_total = len(df)
split = int(n_total * (1 - test_ratio))

X_train_raw = X_all[:split]
y_train_raw = y_all[:split]
X_test_raw  = X_all[split:]
y_test_raw  = y_all[split:]

# =========================
# 5) スケーリング（必ずtrainでfit）
# =========================
x_scaler = MinMaxScaler()
X_train_scaled = x_scaler.fit_transform(X_train_raw)
X_test_scaled  = x_scaler.transform(X_test_raw)

y_scaler = MinMaxScaler()
y_train_scaled = y_scaler.fit_transform(y_train_raw).flatten()
y_test_scaled  = y_scaler.transform(y_test_raw).flatten()

# =========================
# 6) シーケンス化
# =========================
def make_seq_1step(Xs, ys, time_step):
    X_seq, y_seq = [], []
    for i in range(len(Xs) - time_step):
        X_seq.append(Xs[i:i + time_step])
        y_seq.append(ys[i + time_step])
    return np.array(X_seq), np.array(y_seq)

time_step = 60
X_train, y_train = make_seq_1step(X_train_scaled, y_train_scaled, time_step)

X_test_for_seq = np.vstack([X_train_scaled[-time_step:], X_test_scaled])
y_test_for_seq = np.concatenate([y_train_scaled[-time_step:], y_test_scaled])

X_test, y_test = make_seq_1step(X_test_for_seq, y_test_for_seq, time_step)
test_dates = dates[split:][0:len(y_test)]

# =========================
# 7) モデル
# =========================
def build_model(time_step, n_features):
    m = Sequential([
        Input(shape=(time_step, n_features)),
        LSTM(64, return_sequences=True),
        Dropout(0.2),
        LSTM(64),
        Dropout(0.2),
        Dense(32, activation="relu"),
        Dense(1)
    ])
    m.compile(optimizer=tf.keras.optimizers.Adam(1e-3), loss=tf.keras.losses.Huber())
    return m

model = build_model(time_step, X_train.shape[2])

es = EarlyStopping(monitor="val_loss", patience=12, restore_best_weights=True)
rlr = ReduceLROnPlateau(monitor="val_loss", factor=0.5, patience=5, min_lr=1e-5)

model.fit(
    X_train, y_train,
    validation_split=0.1,
    epochs=300,
    batch_size=32,
    callbacks=[es, rlr],
    verbose=0,
    shuffle=False
)

# =========================
# 8) テスト評価（真の精度）
# =========================
pred_test_scaled = model.predict(X_test, verbose=0).flatten()

pred_test_logret = y_scaler.inverse_transform(pred_test_scaled.reshape(-1, 1)).flatten()
true_test_logret = y_scaler.inverse_transform(y_test.reshape(-1, 1)).flatten()

rmse_ret = float(np.sqrt(mean_squared_error(true_test_logret, pred_test_logret)))
mae_ret  = float(mean_absolute_error(true_test_logret, pred_test_logret))
resid_std = float(np.std(true_test_logret - pred_test_logret))

prev_prices = close_series.shift(1).loc[test_dates].to_numpy()
true_prices = close_series.loc[test_dates].to_numpy()
pred_prices = prev_prices * np.exp(pred_test_logret)

rmse_price = float(np.sqrt(mean_squared_error(true_prices, pred_prices)))
mae_price  = float(mean_absolute_error(true_prices, pred_prices))

# =========================
# 9) 未来7営業日予測（リーク無し）
# =========================
alpha12 = 2 / (12 + 1)
alpha26 = 2 / (26 + 1)
alpha9  = 2 / (9 + 1)

horizon = 7  # ★ 30 → 7 に変更

last_close = float(df["Close"].iloc[-1])
last_ema12 = float(df["EMA12"].iloc[-1])
last_ema26 = float(df["EMA26"].iloc[-1])
last_signal = float(df["Signal"].iloc[-1])

# RSI初期化（直近14日の平均gain/loss）
last14 = df["Close"].iloc[-15:]
d14 = last14.diff().dropna()
avg_gain = float(d14.where(d14 > 0, 0.0).mean())
avg_loss = float((-d14.where(d14 < 0, 0.0)).mean())

def compute_rsi_from_avgs(avg_gain, avg_loss):
    rs = avg_gain / (avg_loss + 1e-12)
    rsi = 100 - (100 / (1 + rs))
    return float(np.clip(rsi, 0, 100))
# ★学習分布の分位（予測リターンを常識範囲に収める）
y_train_logret = y_train_raw.flatten()
lo, hi = np.quantile(y_train_logret, [0.01, 0.99])

# ★平均回帰（7日版は弱め推奨。30日版より大きめ）
DRIFT = 0.90   # 0.85〜0.95で調整
# 入力シーケンス（最後のtime_step分）
X_all_scaled = x_scaler.transform(X_all)
seq = X_all_scaled[-time_step:].copy()

future_prices = []
upper_band = []
lower_band = []

p = last_close

for t in range(1, horizon + 1):
    x_in = seq.reshape(1, time_step, len(feature_cols))
    pred_scaled = float(model.predict(x_in, verbose=0)[0, 0])

    # ★MinMax外挿対策（0〜1の範囲に収める）
    pred_scaled = float(np.clip(pred_scaled, 0.0, 1.0))

    pred_logret = float(y_scaler.inverse_transform(np.array([[pred_scaled]], dtype=np.float32))[0, 0])

    # ★学習分布に収める（分位クリップ）
    pred_logret = float(np.clip(pred_logret, lo, hi))

    # ★平均回帰（ドリフト抑制）
    pred_logret *= DRIFT 
    # 価格更新
    p = float(p * np.exp(pred_logret))

    # EMA/MACD/Signal 更新
    last_ema12 = float(alpha12 * p + (1 - alpha12) * last_ema12)
    last_ema26 = float(alpha26 * p + (1 - alpha26) * last_ema26)
    macd = float(last_ema12 - last_ema26)
    last_signal = float(alpha9 * macd + (1 - alpha9) * last_signal)

    # RSI更新
    prev_p = float(p / np.exp(pred_logret))
    change = p - prev_p
    gain_t = max(change, 0.0)
    loss_t = max(-change, 0.0)
    n = 14
    avg_gain = (avg_gain * (n - 1) + gain_t) / n
    avg_loss = (avg_loss * (n - 1) + loss_t) / n
    rsi01 = float(compute_rsi_from_avgs(avg_gain, avg_loss) / 100.0)

    # 特徴量を再構成して次の入力へ
    ema12_ratio = float(last_ema12 / p - 1.0)
    ema26_ratio = float(last_ema26 / p - 1.0)

    feat = np.array([pred_logret, ema12_ratio, ema26_ratio, macd, last_signal, rsi01], dtype=np.float32)
    feat_scaled = x_scaler.transform(feat.reshape(1, -1))[0]

    seq = np.vstack([seq[1:], feat_scaled])

    future_prices.append(p)

    # バンド（logret誤差σ×√t）
    sigma_t = resid_std * np.sqrt(t)
    upper_band.append(p * np.exp(sigma_t))
    lower_band.append(p * np.exp(-sigma_t))

future_prices = np.array(future_prices, dtype=float)
upper_band = np.array(upper_band, dtype=float)
lower_band = np.array(lower_band, dtype=float)

future_dates = pd.bdate_range(df.index[-1] + pd.tseries.offsets.BDay(1), periods=horizon)

# =========================
# 10) 可視化＆保存（7日版）
# =========================
plt.figure(figsize=(14, 8))
plt.plot(df.index, df["Close"].values, label="実株価", linewidth=1)
plt.plot(test_dates, pred_prices, label="テスト期間の予測（1日先）", linewidth=2)
plt.plot(future_dates, future_prices, marker="o", markersize=4, linewidth=2, label=f"未来{horizon}営業日予測")
plt.fill_between(future_dates, lower_band, upper_band, alpha=0.25, label="予測バンド（logret誤差±1σ）")

plt.title(f"{ticker} 真の未来予測（リーク無し）: テスト検証 + 未来{horizon}営業日")
plt.xlabel("日付")
plt.ylabel("株価（円）")
plt.grid(True)
plt.legend(loc="best")

# 常に同じ名前で保存（HTML用）
out = os.path.expanduser("~/Desktop/7267_latest_7d.png")
plt.savefig(out, dpi=150)
plt.close()
#自動で日時付き保存
# out = os.path.expanduser(f"~/Desktop/{ticker}_true_forecast_{horizon}d_{pd.Timestamp.now().strftime('%Y%m%d_%H%M')}.png")

# =========================
# 11) 未来予測の表示（7日版）
# =========================
print("\n========== 未来予測 ==========")
print(f"データ最終日（起点日）: {df.index[-1].date()}")
print(f"起点の終値: {last_close:.2f} 円")
print(f"予測期間: {future_dates[0].date()} 〜 {future_dates[-1].date()}（{horizon}営業日）")
print(f"最終日の予測: {future_prices[-1]:.2f} 円")
print(f"最終日のバンド: {lower_band[-1]:.2f} 円 〜 {upper_band[-1]:.2f} 円")
print(f"✅ 保存しました: {out}")