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

python main.py --mode live --symbol ETH/USDT --timeframe 1m --use-websocket

python main.py --mode live --symbol BTC/USDT --timeframe 1m --use-websocket --dashboard

http://localhost:8060

& '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'

python -c "import sys; sys.path.append('f:/projects/gogo2'); from NN.realtime_main import main; main()" --mode train --model-type cnn --framework pytorch

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.

remodel our NN architecture. we should support up to 3 pairs simultaniously. so input can be 3 pairs: each pair will have up to 5 timeframes 1s(ticks, unspecified length), 1m, 1h, 1d + one additionall. we should normalize them in a way that preserves the relations between them (one price should be normalized to the same value across all tieframes). additionally to the 5 features OHLCV we will add up to 20 additional features for various technical indcators. 1s timeframe will be streamed in realtime. the MOE model should handle all that. we still need to access latest of the CNN hidden layers in the MOe model so we can extract learned features recognition . now let's run our "NN Training Pipeline" debug config. for now we start with single pair - BTC/USD. later we'll add up to 3 pairs for context. the NN will always have only 1 "main" pair - where the buy/sell actions are applied and which price prediction is calculater for each frame. we'll also try to predict the next local extrema that will help us be profitable

python -c "import sys; sys.path.append('f:/projects/gogo2'); from NN.realtime_main import main; main()" --mode train --model-type cnn --framework pytorch python -c "import sys; sys.path.append('f:/projects/gogo2'); from NN.realtime_main import main; main()" --mode train --model-type cnn --framework pytorch --epochs 1000 python -c "import sys; sys.path.append('f:/projects/gogo2'); from NN.realtime_main import main; main()" --mode train --model-type cnn --framework pytorch --epochs 1000 --symbol BTC/USDT --timeframes 1m 5m 1h 4h --epochs 10 --batch-size 32 --window-size 20 --output-size 3 python -c "import sys; sys.path.append('f:/projects/gogo2'); from NN.realtime_main import main; main()" --mode train --model-type cnn --framework pytorch --epochs 10 --symbol BTC/USDT --timeframes 1s 1m 1h 1d --batch-size 32 --window-size 20 --output-size 3

python NN/realtime-main.py --mode train --model-type cnn --framework pytorch --symbol BTC/USDT --timeframes 1m 5m 1h 4h --epochs 10 --batch-size 32 --window-size 20 --output-size 3