Trade by models:

1. Sample Model and Config running:

freqtrade trade --strategy EmaEngAI --strategy-path freqtrade/templates --config config_examples/config_freqai.example.json --freqaimodel GridRegressionDTMultiTarget --freqaimodel-path freqtrade/freqai/prediction_models/ 

2. SVM:

• Don't use SVM.I've tested before ; RMSE was very high.

3. Pytorch:

• DO NOT CREATE ANOTHER CLASS OR FILE AT freqai/torch PASS. IT'S ONLY ACCEPT ONE FILE AS A MODEL WITH THE EXACT NAME FOR CLASS AND FILE PyTorchMLPModel

backtesting:

1. download the data:

freqtrade download-data --exchange binance   --timerange 20230420-20230820 --timeframes  5m    --trading-mode futures --config config_examples/config_freqai.example.json

2. run backtesting:

freqtrade backtesting -s EmaEngAI --strategy-path freqtrade/templates  --config config_examples/config_freqai.example.json   --timerange 20230601-20230701 --freqaimodel GridRegressionDTMultiTarget --freqaimodel-path freqtrade/freqai/prediction_models/ 

hyperparameter optimazer:

1. download data:

freqtrade download-data --exchange binance --timeframes 3m  --timerange 20230801-20230904 --pairs BTC/USDT:USDT ETH/USDT:USDT XRP/USDT:USDT  --erase --trading-mode futures

2. run optimazer:

freqtrade hyperopt -s EmaEngAI --strategy-path freqtrade/templates --config config_examples/config_freqai.example.json --hyperopt-loss OnlyProfitHyperOptLoss -e 40 --timerange 20230501-20230601 --freqaimodel GridRegressionDTMultiTarget --freqaimodel-path freqtrade/freqai/prediction_models/ 

plot the backtest:

1. download data:

freqtrade download-data --exchange binance --timeframes 1w  --timerange 20230601-20230701    --trading-mode futures --config config_examples/config_freqai.example.json

2. Run thic command:

freqtrade plot-dataframe --strategy BBvwap --strategy-path freqtrade/templates   --userdir  user_data/  -c  config_examples/config_freqai.example.json  --timerange 20230601-20230701 --pairs BTC/USDT:USDT 

• Be mindful of the train_period_days and backtest_period_days settings when running backtests and hyperparameter optimizations in FreqAI. The sum of train_period_days and backtest_period_days must not exceed the specified time range.

• Remove all - pycache - file before backtesting and hyperopt:

find . -type d -name "__pycache__" -exec rm -r {} \; ; rm -rf user_data/* ; rm -rf trad*