Strategy: AutoML

AutoML (automted machine learning) automatically does most of what a data scientist would do to create the best model for your data. The AutoML program cleans the data, creates new features, and then tries many different algorithms to increase your model's accuracy. It often also handles HPO (hyperparameter optimization) and ensembing (combining multiple models into one big model).

To test AutoML, we ran a backtest, which means we simulated how much profit it would have made if we had traded it for real using historical data. This is a good indication of how well AutoML strategy would do in live trading for real.

Results: In our backtest, the AI model's accuracy (up or down prediction) was ______.
The profit/loss backtest had a Profit Factor (PF) of ____ and is shown in the table and chart below. A PF greater than 1 means it would have made a profit. This backtest does not include commissions or the bid/ask spread.




You can run this backtest yourself in our AutoML Google Colab notebook: Pickstocks.com - AutoML. It uses our open-source GitHub repo at Pickstocks GitHub.