Strategy: Evolutionary Algorithms

Evolutionary Algorithms search for the best way to model a dataset using a genetics and evolution. A random group of possible solutions constantly evolves to get better and better with each generation to arrive at a final solution. This "survival of the fitist" method is based on Darwin's Theory of Evolution in nature.

To test an Evolutionary Algorithm, 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 our Evolutionary Algorithms 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 Evolutionary Algorithms 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.