Reference Point

Train Accuracy: 99.94%
Test Accuracy: 77.88%
              precision    recall  f1-score   support

         0.0       0.79      0.87      0.83       362
         1.0       0.70      0.63      0.67       243
         2.0       0.83      0.79      0.81       281

    accuracy                           0.78       886
   macro avg       0.77      0.76      0.77       886
weighted avg       0.78      0.78      0.78       886

The above results are what I am currently getting with my XGBoost model, using a test size of 10%. As previously mentioned, the classes are

0 – Sell
1 – Do nothing
2 – Buy

Those scores aren’t too bad, but I’ll work on improving them. I’m posting here for the record. One think I might try is to use log returns instead of returns. Finance people seem to prefer those. Apparently they are more normally distributed than regular returns, which is considered a good thing.

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