Stock Price Prediction AI
There are 2 ways to predict stock prices: Classification and Regression. For Clasification, the machine learning model predicts if the price will generally go up or down. For Regression, it predicts the exact price of the stock in the future and they you buy or sell based on that.
To test a Regression model, 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 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 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.