enhancements
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crypto/gogo2/_notes.md
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ensure we use GPU if available to train faster. during training we need to have RL loop that looks at streaming data, and retrospective backtesting/training on predictions. sincr the start of the traing we're only loosing. implement robust penalty and analysis when closing a loosing trade and improve the reward function.
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2025-03-10 12:11:28,651 - INFO - Initialized environment with 500 candles
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C:\Users\popov\miniforge3\Lib\site-packages\torch\nn\modules\transformer.py:385: UserWarning: enable_nested_tensor is True, but self.use_nested_tensor is False because encoder_layer.self_attn.batch_first was not True(use batch_first for better inference performance)
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warnings.warn(
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main.py:1105: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead.
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self.scaler = amp.GradScaler()
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C:\Users\popov\miniforge3\Lib\site-packages\torch\amp\grad_scaler.py:132: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available. Disabling.
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warnings.warn(
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2025-03-10 12:11:30,927 - INFO - Starting training for 1000 episodes...
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2025-03-10 12:11:30,927 - INFO - Starting training on device: cpu
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2025-03-10 12:11:30,928 - ERROR - Training failed: 'TradingEnvironment' object has no attribute 'initialize_price_predictor'
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2025-03-10 12:11:30,928 - INFO - Exchange connection closed
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Backend tkagg is interactive backend. Turning interactive mode on.
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crypto/gogo2/main.py
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crypto/gogo2/main.py
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