gogo2/_notes.md

https://github.com/mexcdevelop/mexc-api-sdk/blob/main/README.md#test-new-order

python mexc_tick_visualizer.py --symbol BTC/USDT --interval 1.0 --candle 60

& 'C:\Users\popov\miniforge3\python.exe' 'c:\Users\popov.cursor\extensions\ms-python.debugpy-2024.6.0-win32-x64\bundled\libs\debugpy\adapter/../..\debugpy\launcher' '51766' '--' 'main.py' '--mode' 'live' '--demo' 'false' '--symbol' 'ETH/USDT' '--timeframe' '1m' '--leverage' '50'

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.

add 1h and 1d OHLCV data to let the model have the price action context

2025-03-10 12:11:28,651 - INFO - Initialized environment with 500 candles 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) warnings.warn( main.py:1105: FutureWarning: torch.cuda.amp.GradScaler(args...) is deprecated. Please use torch.amp.GradScaler('cuda', args...) instead. self.scaler = amp.GradScaler() 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. warnings.warn( 2025-03-10 12:11:30,927 - INFO - Starting training for 1000 episodes... 2025-03-10 12:11:30,927 - INFO - Starting training on device: cpu 2025-03-10 12:11:30,928 - ERROR - Training failed: 'TradingEnvironment' object has no attribute 'initialize_price_predictor' 2025-03-10 12:11:30,928 - INFO - Exchange connection closed Backend tkagg is interactive backend. Turning interactive mode on.