Stock Price Prediction AI
The "Predicted Probability" strategy uses the confidence level of the AI stock trading model to decide which trades to enter. For example, what if the AI model only buys stocks where it is at least 90% confident that the price will go up? Shouldn't ingoring the low confidence predictions make a larger profit?
To test this theory, 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 the Predicted Probability 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 Predicted Probability 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.