Add stock prediction experiments: AAPL forecast + 4-version backtest comparison

- stock_forecast.py: Basic AAPL price forecast
- stock_backtest.py: V1 original price prediction backtest
- stock_backtest_optimized.py: V2 log-return + ensemble optimization
- stock_backtest_xreg.py: V3 XReg covariates (volume/RSI/SPY)
- stock_backtest_trick.py: V4 SPY trend guidance + volatility adjustment
- Visualization charts for all versions

stock_backtest.py

@@ -0,0 +1,115 @@
+import yfinance as yf
+import numpy as np
+import torch
+import timesfm
+import matplotlib
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+from datetime import datetime, timedelta
+
+# 1. 获取 AAPL 数据（到今天为止）
+print("下载 AAPL 股票数据...", flush=True)
+end = datetime.now()
+start = end - timedelta(days=365)
+df = yf.download("AAPL", start=start.strftime("%Y-%m-%d"), end=end.strftime("%Y-%m-%d"), progress=False)
+close = df["Close"].values.flatten().astype(np.float32)
+dates = df.index
+
+# 2. 分割：6/15 之前为训练集，6/16 之后为实际值
+split_date = "2026-06-15"
+split_idx = None
+for i, d in enumerate(dates):
+    if str(d.date()) <= split_date:
+        split_idx = i
+# split_idx 是 <= 6/15 的最后一个索引
+train_data = close[: split_idx + 1]
+train_dates = dates[: split_idx + 1]
+actual_data = close[split_idx + 1 :]
+actual_dates = dates[split_idx + 1 :]
+
+HORIZON = len(actual_data)
+print(f"训练数据: {len(train_data)} 天（截至 {train_dates[-1].date()}）", flush=True)
+print(f"实际数据: {HORIZON} 天（{actual_dates[0].date()} ~ {actual_dates[-1].date()}）", flush=True)
+print(f"分割日收盘价: {train_data[-1]:.2f}", flush=True)
+
+# 3. 加载模型
+print()
+print("加载 TimesFM 模型...", flush=True)
+torch.set_float32_matmul_precision("high")
+model = timesfm.TimesFM_2p5_200M_torch.from_pretrained(
+    "google/timesfm-2.5-200m-pytorch", torch_compile=False
+)
+print("模型加载完成", flush=True)
+
+# 4. 编译并预测
+model.compile(
+    timesfm.ForecastConfig(
+        max_context=512,
+        max_horizon=128,
+        normalize_inputs=True,
+        use_continuous_quantile_head=True,
+        force_flip_invariance=False,
+        infer_is_positive=True,
+        fix_quantile_crossing=True,
+    )
+)
+print(f"预测未来 {HORIZON} 个交易日...", flush=True)
+pf, qf = model.forecast(horizon=HORIZON, inputs=[train_data])
+print("预测完成！", flush=True)
+
+# 5. 计算误差指标
+actual = actual_data
+pred = pf[0][:HORIZON]
+mae = np.mean(np.abs(actual - pred))
+rmse = np.sqrt(np.mean((actual - pred) ** 2))
+mape = np.mean(np.abs((actual - pred) / actual)) * 100
+
+# 方向准确率
+actual_dir = np.diff(actual)
+pred_dir = np.diff(pred)
+dir_acc = np.mean(actual_dir * pred_dir > 0) * 100
+
+# 置信区间覆盖率
+in_80 = np.mean((actual >= qf[0, :HORIZON, 1]) & (actual <= qf[0, :HORIZON, 9])) * 100
+in_40 = np.mean((actual >= qf[0, :HORIZON, 3]) & (actual <= qf[0, :HORIZON, 7])) * 100
+
+print()
+print("=== 回测结果：6/16 ~ 6/30 预测 vs 实际 ===", flush=True)
+print()
+print("  日期          实际价    预测价    误差    误差%", flush=True)
+for i in range(HORIZON):
+    err = pred[i] - actual[i]
+    err_pct = err / actual[i] * 100
+    print(
+        f"  {actual_dates[i].date()}  {actual[i]:7.2f}  {pred[i]:7.2f}  {err:+7.2f}  {err_pct:+6.2f}%",
+        flush=True,
+    )
+
+print()
+print("=== 误差指标 ===", flush=True)
+print(f"  MAE  (平均绝对误差):     ${mae:.2f}", flush=True)
+print(f"  RMSE (均方根误差):       ${rmse:.2f}", flush=True)
+print(f"  MAPE (平均绝对百分比误差): {mape:.2f}%", flush=True)
+print(f"  方向准确率:               {dir_acc:.1f}%", flush=True)
+print(f"  80%置信区间覆盖率:        {in_80:.1f}%", flush=True)
+print(f"  40%置信区间覆盖率:        {in_40:.1f}%", flush=True)
+
+# 6. 画图
+fig, ax = plt.subplots(figsize=(14, 6))
+show_n = min(60, len(train_data))
+ax.plot(range(show_n), train_data[-show_n:], label="Historical Close", color="steelblue", linewidth=1.5)
+x_actual = range(show_n, show_n + HORIZON)
+ax.plot(x_actual, actual, label="Actual", color="forestgreen", linewidth=2, marker="o", markersize=3)
+ax.plot(x_actual, pred, label="Forecast", color="tomato", linewidth=2, linestyle="--")
+ax.fill_between(x_actual, qf[0, :HORIZON, 1], qf[0, :HORIZON, 9], alpha=0.15, color="tomato", label="80% CI")
+ax.fill_between(x_actual, qf[0, :HORIZON, 3], qf[0, :HORIZON, 7], alpha=0.3, color="tomato", label="40% CI")
+ax.axvline(x=show_n - 1, color="gray", linestyle="--", alpha=0.5, label="Forecast Start")
+ax.set_title(f"AAPL Backtest: Forecast vs Actual (MAPE={mape:.2f}%, Dir.Acc={dir_acc:.0f}%)", fontsize=14)
+ax.set_xlabel("Trading Days")
+ax.set_ylabel("Price (USD)")
+ax.legend(loc="upper left")
+plt.tight_layout()
+plt.savefig("aapl_backtest.png", dpi=150)
+print()
+print("Chart saved: aapl_backtest.png", flush=True)
+print("Done!", flush=True)