Symbolic Regression is an unusual form of machine learning that builds a model using mathematical expressions (+, -, *, %, etc.) that best fits your dataset. Unlike neural networks and traditional ML algorithms, no AI program is needed to make predictions with it because it is just basic math which can be calculaed in Python or any similar language.

To test Symbolic Regression, 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 Symbolic Regression 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 Symbolic Regression Google Colab notebook: Pickstocks.com - Machine Learning. It uses our open-source GitHub repo at Pickstocks GitHub. You can also change the data or AI strategy yourself if you want and then backtest it using our repo.