Hi, how can we know the IS Sharpe will be when develop my strategy ?
For example, the backtest_ml prints Sharpe at the end, but as I know, it is the Sharpe of the last day of trading.
Many strategies developed locally have Sharpe > 1.0 but when submitted are filtered by IS Sharpe.
I am starting with a LSTM example model (In the example section). I want to print the Sharpe of the model on the training set to compare its Sharpe on test set (which is printed by default). To do that, I am editting the backtester.py file (specifically backtest_ml function) (You can check I added line 172-226 in the editted file for printing the Sharpe of the model on training set in the backtester.py file attached with this post).
But I got an unexpected result that I think I was doing st wrong : Sharpe of the training is 0.89 and Sharpe of the test set is -0.04; whereas if there was nothing changed in the backtester.py file , the Sharpe by default printed is 0.89.
My DL models are filtered by Calculation time exceed.
I have tested it on Google Colab, and the time for it to run qnt.backtest (the last cell) is about 2-3 minutes (without considering some kind of pip install pandas, qnt libraries API).
(Other cells take 0s to run).
Whereas the time for futures is up to 10 minutes.
My submission is made as follows : Click Jupyter, replace "strategy.ipynb" with my "strategy.ipynb" (delete three first cells of installing pandas, qnt libraries ... as suggested), add a new cell in "init.ipynb" : pip install torch.
Can you figure out the root of the problem and how to tackle it ? (you can use LSTM example which is analogous to my model).