@blackpearl Hello. I don’t use machine learning in trading, and I don’t have similar examples. If you know Python and know how to develop such systems, or if you use ChatGPT (or similar tools) for development, you should not have difficulties modifying existing examples. You will need to change the model training and prediction functions.
One of the competitive advantages of the Quantiacs platform is the ability to test machine learning models from a financial performance perspective.
I haven’t encountered similar tools. Typically, models are evaluated using metrics like F1 score and cross-validation (for example, in the classification task of predicting whether the price will rise tomorrow).
However, there are several problems:
It is unclear how much profit this model can generate. In real trading, there will be commissions, slippage, data errors, and the F1 score doesn’t account for these factors.
It is possible to inadvertently look into the future. For instance, data preprocessing techniques like standardization can leak future information into the past. If you subtract the mean or maximum value from each point in the time series, the maximum value reached in 2021 would be known in 2015, which is unacceptable.
The Quantiacs platform provides a tool for evaluating models from a financial performance perspective.
However, practice shows that finding a good machine learning model requires significant computational resources and time for training and testing. My results when testing strategies on real data have not been very good.
