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