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crypto_predictorandClaude Sonnet 4.5 9bbdc8d42b 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>
2026-01-29 21:27:15 +08:00

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/Users/xxz/miniforge3/envs/crypto_pred_312/lib/python3.12/site-packages/torch/optim/lr_scheduler.py:62: UserWarning: The verbose parameter is deprecated. Please use get_last_lr() to access the learning rate.
warnings.warn(
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/Users/xxz/PycharmProjects/crypto_predictor/training/trainer.py:285: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
checkpoint = torch.load(self.best_model_path, map_location=self.device)
Traceback (most recent call last):
File "/Users/xxz/PycharmProjects/crypto_predictor/train_dlinear_real.py", line 163, in <module>
main()
File "/Users/xxz/PycharmProjects/crypto_predictor/train_dlinear_real.py", line 80, in main
metrics = trainer.evaluate(test_loader)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/xxz/PycharmProjects/crypto_predictor/training/trainer.py", line 289, in evaluate
metrics = evaluate_model(self.model, test_loader, self.task, self.device)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/xxz/PycharmProjects/crypto_predictor/training/evaluator.py", line 248, in evaluate_model
all_preds.extend(preds)
TypeError: 'numpy.int64' object is not iterable
ERROR conda.cli.main_run:execute(127): `conda run python train_dlinear_real.py` failed. (See above for error)
================================================================================
DLinear模型训练 - 真实Binance数据
================================================================================
[1/7] 从Binance获取BTC 1h真实数据...
✓ 使用SOCKS5代理: socks5://127.0.0.1:7890
✓ 已成功连接到Binance API
从缓存加载数据：data/cache/BTC_1h_1y_now.csv
✓ 已获取 8760 行真实数据
✓ 时间范围：2025-01-28 15:00:00 至 2026-01-28 14:00:00
[2/7] 数据预处理（技术指标 + 特征工程）...
Created 8535 sequences with shape (8535, 168, 26)
✓ 已创建序列：X形状 = (8535, 168, 26), y形状 = (8535,)
✓ 特征数量：26
✓ 标签分布：下跌(0) = 6970, 上涨(1) = 1565
[3/7] 划分训练/验证/测试集 (70/15/15)...
Train: 5974 samples
Val: 1280 samples
Test: 1281 samples
[4/7] 创建PyTorch数据加载器...
✓ 训练批次：94
✓ 验证批次：20
✓ 测试批次：21
[5/7] 创建DLinear模型...
✓ 模型已创建，可训练参数：14,675 个
[6/7] 开始训练模型...
Training on device: mps
Starting training for 50 epochs...
Model parameters: 14675
Epoch 1/50
Train loss: 0.4835
Val loss: 0.4939
LR: 0.001000
Saved best model (val_loss: 0.4939)
Epoch 2/50
Train loss: 0.4628
Val loss: 0.4833
LR: 0.001000
Saved best model (val_loss: 0.4833)
Epoch 3/50
Train loss: 0.4444
Val loss: 0.5080
LR: 0.001000
Epoch 4/50
Train loss: 0.4312
Val loss: 0.4956
LR: 0.001000
Epoch 5/50
Train loss: 0.4169
Val loss: 0.5220
LR: 0.001000
Epoch 6/50
Train loss: 0.4070
Val loss: 0.5491
LR: 0.001000
Epoch 7/50
Train loss: 0.3995
Val loss: 0.5842
LR: 0.001000
Epoch 8/50
Train loss: 0.3922
Val loss: 0.5482
LR: 0.001000
Epoch 9/50
Train loss: 0.3773
Val loss: 0.5535
LR: 0.000500
Epoch 10/50
Train loss: 0.3690
Val loss: 0.5738
LR: 0.000500
Epoch 11/50
Train loss: 0.3653
Val loss: 0.6210
LR: 0.000500
Epoch 12/50
Train loss: 0.3603
Val loss: 0.5912
LR: 0.000500
Early stopping at epoch 12
Training completed!
Best validation loss: 0.4833
Best model saved to: models/checkpoints/dlinear_btc_1h_real/best_model.pt
[7/7] 在测试集上评估模型...
Loading best model from models/checkpoints/dlinear_btc_1h_real/best_model.pt
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