Initial commit: BTC 1h prediction model (53.10% accuracy)

🎯 项目成果：
- 准确率: 53.10% (2026年测试集)
- 任务类型: 二分类 (预测涨/跌)
- 特征: OHLCV + RSI + MACD (9个特征)
- 训练数据: 2020-2025年BTC小时数据 (52,351序列)
- 测试数据: 2026年1-29日 (484序列)

📊 性能指标：
- 精确率: 51.79%
- 召回率: 49.36%
- F1分数: 0.5054
- 预测分布: 非常平衡 (偏差仅-11个)

🔬 实验历史：
- 尝试了5种方案 (回归/分类, 不同数据划分)
- 分类任务优于回归任务 (53.10% vs 49.5%)
- 全量训练避免了过拟合问题

📦 主要模型文件：
- models/checkpoints/dlinear_classification_2026/
  - best_model.pt (最佳模型)
  - preprocessor.pkl (预处理器)
  - test_2026_results.json (测试结果)

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>

models/__init__.py

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+"""Model implementations."""

models/base.py

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+"""Base model interface for all prediction models."""
+
+from abc import ABC, abstractmethod
+from dataclasses import dataclass
+from typing import Literal, Optional
+import torch
+import torch.nn as nn
+
+
+@dataclass
+class ModelConfig:
+    """Base configuration for all models."""
+
+    input_dim: int  # Number of input features
+    lookback: int  # Sequence length
+    task: Literal['classification', 'regression'] = 'classification'
+    hidden_dim: int = 512
+    dropout: float = 0.1
+
+
+class BasePredictor(nn.Module, ABC):
+    """Abstract base class for all prediction models."""
+
+    def __init__(self, config: ModelConfig):
+        """Initialize model with configuration.
+
+        Args:
+            config: Model configuration
+        """
+        super().__init__()
+        self.config = config
+
+    @abstractmethod
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """Forward pass.
+
+        Args:
+            x: Input tensor of shape (batch_size, lookback, input_dim)
+
+        Returns:
+            Output tensor. Shape depends on task:
+                - Classification: (batch_size, 1) with sigmoid output
+                - Regression: (batch_size, 1) with raw value
+        """
+        pass
+
+    def predict(self, x: torch.Tensor) -> torch.Tensor:
+        """Make predictions (inference mode).
+
+        Args:
+            x: Input tensor
+
+        Returns:
+            Predictions
+        """
+        self.eval()
+        with torch.no_grad():
+            return self.forward(x)
+
+    def get_loss_function(self, pos_weight=None, device='cpu') -> nn.Module:
+        """Get appropriate loss function for the task.
+
+        Args:
+            pos_weight: Weight for positive class (for imbalanced data)
+            device: Device to place tensors on
+
+        Returns:
+            Loss function
+        """
+        if self.config.task == 'classification':
+            if pos_weight is not None:
+                # 使用float32以兼容MPS (Mac M1)
+                pos_weight_tensor = torch.tensor([pos_weight], dtype=torch.float32).to(device)
+                return nn.BCEWithLogitsLoss(pos_weight=pos_weight_tensor)
+            else:
+                return nn.BCELoss()
+        else:  # regression
+            return nn.MSELoss()
+
+    def save(self, path: str):
+        """Save model checkpoint.
+
+        Args:
+            path: Path to save checkpoint
+        """
+        torch.save({
+            'model_state_dict': self.state_dict(),
+            'config': self.config
+        }, path)
+
+    @classmethod
+    def load(cls, path: str, device: Optional[str] = None):
+        """Load model from checkpoint.
+
+        Args:
+            path: Path to checkpoint
+            device: Device to load model to
+
+        Returns:
+            Loaded model
+        """
+        checkpoint = torch.load(path, map_location=device)
+        model = cls(checkpoint['config'])
+        model.load_state_dict(checkpoint['model_state_dict'])
+        return model
+
+    def to_onnx(self, path: str, dummy_input: Optional[torch.Tensor] = None):
+        """Export model to ONNX format.
+
+        Args:
+            path: Path to save ONNX model
+            dummy_input: Example input tensor. If None, will create default.
+        """
+        # Move to CPU for ONNX export (critical for M1)
+        self.cpu()
+        self.eval()
+
+        # Create dummy input if not provided
+        if dummy_input is None:
+            dummy_input = torch.randn(
+                1,
+                self.config.lookback,
+                self.config.input_dim
+            )
+
+        # Ensure dummy input is on CPU
+        dummy_input = dummy_input.cpu()
+
+        # Export to ONNX
+        torch.onnx.export(
+            self,
+            dummy_input,
+            path,
+            export_params=True,
+            opset_version=14,
+            do_constant_folding=True,
+            input_names=['input'],
+            output_names=['output'],
+            dynamic_axes={
+                'input': {0: 'batch_size'},
+                'output': {0: 'batch_size'}
+            }
+        )
+
+        print(f"Model exported to ONNX: {path}")
+
+    def count_parameters(self) -> int:
+        """Count trainable parameters.
+
+        Returns:
+            Number of trainable parameters
+        """
+        return sum(p.numel() for p in self.parameters() if p.requires_grad)
+
+
+if __name__ == "__main__":
+    # Test base class (with a dummy implementation)
+    class DummyModel(BasePredictor):
+        def __init__(self, config: ModelConfig):
+            super().__init__(config)
+            self.fc = nn.Linear(config.input_dim * config.lookback, 1)
+            if config.task == 'classification':
+                self.activation = nn.Sigmoid()
+            else:
+                self.activation = nn.Identity()
+
+        def forward(self, x):
+            batch_size = x.size(0)
+            x = x.view(batch_size, -1)
+            x = self.fc(x)
+            return self.activation(x)
+
+    # Test
+    config = ModelConfig(input_dim=10, lookback=168, task='classification')
+    model = DummyModel(config)
+
+    print(f"Parameters: {model.count_parameters()}")
+
+    # Test forward pass
+    x = torch.randn(32, 168, 10)
+    output = model(x)
+    print(f"Output shape: {output.shape}")

models/checkpoints/cross_asset_btc_to_eth/test_metrics.json

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+{
+  "mse": 5.495949517353438e-05,
+  "rmse": 0.007413467149285439,
+  "mae": 0.004826944321393967,
+  "mape": 930.6216430664062,
+  "directional_accuracy": 0.4907678594167834,
+  "backtest_total_return": -0.9745769500732422,
+  "backtest_final_capital": 254.23023986816406,
+  "backtest_sharpe_ratio": -0.26884937315369317,
+  "backtest_max_drawdown": -0.978744387626648,
+  "backtest_win_rate": 0.47166687889978354,
+  "backtest_num_trades": 3251
+}

models/checkpoints/dlinear_811_split/test_metrics.json

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+{
+  "price_mae": 4283.3626861511675,
+  "price_rmse": 4735.107859758518,
+  "price_mape": 3.928560493143656,
+  "direction_accuracy": 0.49637127578304047,
+  "true_positives": 258,
+  "true_negatives": 2341,
+  "false_positives": 255,
+  "false_negatives": 2382
+}

models/checkpoints/dlinear_btc_1h_forecast/test_metrics.json

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+{
+  "accuracy": 0.5,
+  "precision": 0.0,
+  "recall": 0.0,
+  "f1": 0.0,
+  "true_negatives": 642,
+  "false_positives": 0,
+  "false_negatives": 642,
+  "true_positives": 0,
+  "specificity": 1.0,
+  "mean_confidence": 0.0068817585706710815,
+  "backtest_total_return": -1.0,
+  "backtest_final_capital": 0.0,
+  "backtest_sharpe_ratio": -9.643650760992955,
+  "backtest_max_drawdown": NaN,
+  "backtest_win_rate": 0.0,
+  "backtest_num_trades": 0
+}

models/checkpoints/dlinear_btc_1h_forecast/test_metrics_fixed.json

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+{
+  "accuracy": 0.5070093457943925,
+  "precision": 0.5053254437869823,
+  "recall": 0.6651090342679128,
+  "f1": 0.574310692669805,
+  "true_negatives": 224,
+  "false_positives": 418,
+  "false_negatives": 215,
+  "true_positives": 427,
+  "specificity": 0.34890965732087226,
+  "mean_confidence": 0.5017203688621521,
+  "backtest_total_return": -1.0,
+  "backtest_final_capital": -0.0,
+  "backtest_sharpe_ratio": 2.314261942010565,
+  "backtest_max_drawdown": -1.0,
+  "backtest_win_rate": 0.3325545171339564,
+  "backtest_num_trades": 136
+}

models/checkpoints/dlinear_btc_2020_2025/test_metrics.json

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+{
+  "accuracy": 0.49764421240290335,
+  "precision": 0.502192108409725,
+  "recall": 0.3185035389282103,
+  "f1": 0.38979118329466356,
+  "true_negatives": 2648,
+  "false_positives": 1249,
+  "false_negatives": 2696,
+  "true_positives": 1260,
+  "specificity": 0.6794970490120605,
+  "mean_confidence": 0.49758902192115784,
+  "backtest_total_return": -1.0,
+  "backtest_final_capital": -0.0,
+  "backtest_sharpe_ratio": -2.5728736046923655,
+  "backtest_max_drawdown": NaN,
+  "backtest_win_rate": 0.16044823634279892,
+  "backtest_num_trades": 782
+}

models/checkpoints/dlinear_btc_full_data/final_metrics.json

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+{
+  "mse": 2.142045195796527e-05,
+  "rmse": 0.004628223412710893,
+  "mae": 0.0030602782499045134,
+  "mape": 131.8273162841797,
+  "directional_accuracy": 0.49732620320855614,
+  "backtest_total_return": 0.2429506778717041,
+  "backtest_final_capital": 12429.5068359375,
+  "backtest_sharpe_ratio": 0.19540257211386183,
+  "backtest_max_drawdown": -0.13858233392238617,
+  "backtest_win_rate": 0.49732620320855614,
+  "backtest_num_trades": 0
+}

models/checkpoints/dlinear_btc_regression/test_metrics.json

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+{
+  "mse": 2.382730417593848e-05,
+  "rmse": 0.004881321970116137,
+  "mae": 0.0033776722848415375,
+  "mape": 780.3690185546875,
+  "directional_accuracy": 0.5066853431809499,
+  "backtest_total_return": -0.1671363264322281,
+  "backtest_final_capital": 8328.63671875,
+  "backtest_sharpe_ratio": -0.0266775386419239,
+  "backtest_max_drawdown": -0.40377509593963623,
+  "backtest_win_rate": 0.48974914045587675,
+  "backtest_num_trades": 1061
+}

models/checkpoints/dlinear_classification_2026/test_2026_results.json

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+{
+  "accuracy": 0.53099173553719,
+  "precision": 0.5178571428571429,
+  "recall": 0.49361702127659574,
+  "f1_score": 0.505446623093682,
+  "true_positives": 116,
+  "true_negatives": 141,
+  "false_positives": 108,
+  "false_negatives": 119,
+  "n_samples": 484
+}

models/checkpoints/dlinear_predict_price/metrics.json

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+{
+  "price_mae": 528.017854278076,
+  "price_rmse": 736.6082253699151,
+  "price_mape": 0.523728600511905,
+  "direction_accuracy": 0.5022918258212375
+}

models/checkpoints/dlinear_simple_features/metrics.json

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+{
+  "mse": 2.1433252186398022e-05,
+  "rmse": 0.004629606050885758,
+  "mae": 0.00306295626796782,
+  "mape": 180.17770385742188,
+  "directional_accuracy": 0.4992360580595875,
+  "backtest_total_return": 0.226644828915596,
+  "backtest_final_capital": 12266.4482421875,
+  "backtest_sharpe_ratio": 0.18486101832170032,
+  "backtest_max_drawdown": -0.13858233392238617,
+  "backtest_win_rate": 0.4977081741787624,
+  "backtest_num_trades": 38
+}

models/dlinear/__init__.py

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+"""DLinear model implementation."""

models/dlinear/model.py

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+"""DLinear: Decomposition Linear model for time series forecasting.
+
+Reference: Are Transformers Effective for Time Series Forecasting? (DLinear)
+Paper: https://arxiv.org/abs/2205.13504
+"""
+
+import torch
+import torch.nn as nn
+from dataclasses import dataclass
+from typing import Literal
+
+from ..base import BasePredictor, ModelConfig
+
+
+@dataclass
+class DLinearConfig(ModelConfig):
+    """Configuration for DLinear model."""
+
+    moving_avg_kernel: int = 25  # Kernel size for moving average decomposition
+    individual: bool = False  # Whether to use individual linear layers per feature
+
+
+class MovingAvg(nn.Module):
+    """Moving average block for trend extraction."""
+
+    def __init__(self, kernel_size: int):
+        """Initialize moving average layer.
+
+        Args:
+            kernel_size: Size of the moving average window
+        """
+        super().__init__()
+        self.kernel_size = kernel_size
+        self.avg = nn.AvgPool1d(
+            kernel_size=kernel_size,
+            stride=1,
+            padding=kernel_size // 2,
+            count_include_pad=False
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """Apply moving average.
+
+        Args:
+            x: Input tensor of shape (batch_size, seq_len, features)
+
+        Returns:
+            Smoothed tensor
+        """
+        # Padding at both ends
+        front = x[:, 0:1, :].repeat(1, self.kernel_size // 2, 1)
+        end = x[:, -1:, :].repeat(1, (self.kernel_size - 1) // 2, 1)
+        x_padded = torch.cat([front, x, end], dim=1)
+
+        # Apply average pooling (swap dimensions for AvgPool1d)
+        x_padded = x_padded.permute(0, 2, 1)  # (batch, features, seq_len)
+        x_smoothed = self.avg(x_padded)
+        x_smoothed = x_smoothed.permute(0, 2, 1)  # (batch, seq_len, features)
+
+        # Trim to original length
+        return x_smoothed[:, :x.size(1), :]
+
+
+class SeriesDecomposition(nn.Module):
+    """Series decomposition block to extract trend and seasonal components."""
+
+    def __init__(self, kernel_size: int):
+        """Initialize decomposition layer.
+
+        Args:
+            kernel_size: Size of the moving average window for trend
+        """
+        super().__init__()
+        self.moving_avg = MovingAvg(kernel_size)
+
+    def forward(self, x: torch.Tensor) -> tuple:
+        """Decompose series into trend and seasonal components.
+
+        Args:
+            x: Input tensor of shape (batch_size, seq_len, features)
+
+        Returns:
+            Tuple of (seasonal, trend) tensors
+        """
+        trend = self.moving_avg(x)
+        seasonal = x - trend
+        return seasonal, trend
+
+
+class DLinear(BasePredictor):
+    """DLinear model: Decomposition + Linear layers for forecasting."""
+
+    def __init__(self, config: DLinearConfig):
+        """Initialize DLinear model.
+
+        Args:
+            config: Model configuration
+        """
+        super().__init__(config)
+
+        self.decomposition = SeriesDecomposition(config.moving_avg_kernel)
+
+        if config.individual:
+            # Individual linear layer for each feature
+            self.linear_seasonal = nn.ModuleList([
+                nn.Linear(config.lookback, 1)
+                for _ in range(config.input_dim)
+            ])
+            self.linear_trend = nn.ModuleList([
+                nn.Linear(config.lookback, 1)
+                for _ in range(config.input_dim)
+            ])
+        else:
+            # Shared linear layers
+            self.linear_seasonal = nn.Linear(config.lookback, 1)
+            self.linear_trend = nn.Linear(config.lookback, 1)
+
+        # Final aggregation layer
+        self.aggregation = nn.Sequential(
+            nn.Linear(config.input_dim, config.hidden_dim),
+            nn.ReLU(),
+            nn.Dropout(config.dropout),
+            nn.Linear(config.hidden_dim, 1)
+        )
+
+        # Output activation
+        # 注意：对于分类任务，使用Identity而非Sigmoid
+        # 因为BCEWithLogitsLoss内部已包含Sigmoid，避免双重激活
+        if config.task == 'classification':
+            self.output_activation = nn.Identity()  # 输出logits，由损失函数处理
+        else:
+            self.output_activation = nn.Identity()
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """Forward pass through DLinear.
+
+        Args:
+            x: Input tensor of shape (batch_size, lookback, input_dim)
+
+        Returns:
+            Output tensor of shape (batch_size, 1)
+        """
+        # Decompose into seasonal and trend
+        seasonal, trend = self.decomposition(x)
+
+        # Apply linear layers to each component
+        if self.config.individual:
+            seasonal_out = []
+            trend_out = []
+            for i in range(self.config.input_dim):
+                # Extract feature i: (batch, seq_len)
+                s_i = seasonal[:, :, i]
+                t_i = trend[:, :, i]
+
+                # Apply linear transformation: (batch, seq_len) -> (batch, 1)
+                seasonal_out.append(self.linear_seasonal[i](s_i))
+                trend_out.append(self.linear_trend[i](t_i))
+
+            # Stack outputs: (batch, input_dim)
+            seasonal_out = torch.stack(seasonal_out, dim=1).squeeze(-1)
+            trend_out = torch.stack(trend_out, dim=1).squeeze(-1)
+        else:
+            # Shared linear layer
+            # Swap dimensions: (batch, seq_len, features) -> (batch, features, seq_len)
+            seasonal = seasonal.permute(0, 2, 1)
+            trend = trend.permute(0, 2, 1)
+
+            # Apply linear: (batch, features, seq_len) -> (batch, features, 1)
+            seasonal_out = self.linear_seasonal(seasonal).squeeze(-1)
+            trend_out = self.linear_trend(trend).squeeze(-1)
+
+        # Combine seasonal and trend
+        combined = seasonal_out + trend_out  # (batch, input_dim)
+
+        # Aggregate and produce final prediction
+        output = self.aggregation(combined)  # (batch, 1)
+        output = self.output_activation(output)
+
+        return output
+
+
+if __name__ == "__main__":
+    # Test DLinear model
+    config = DLinearConfig(
+        input_dim=30,
+        lookback=168,
+        task='classification',
+        hidden_dim=512,
+        moving_avg_kernel=25
+    )
+
+    model = DLinear(config)
+    print(f"DLinear parameters: {model.count_parameters()}")
+
+    # Test forward pass
+    x = torch.randn(32, 168, 30)
+    output = model(x)
+    print(f"Output shape: {output.shape}")
+    print(f"Output range: [{output.min():.4f}, {output.max():.4f}]")
+
+    # Test ONNX export
+    model.to_onnx('/tmp/dlinear_test.onnx')
+    print("ONNX export successful!")