Strategy: Time Series

Stock Price Prediction AI

A Time Series machine learning model can be used for making predictions where the data is ordered by time (temporal). Stock prices are a perfect application for this, as the previous price movements have an impact on the current value. This is different than a traditional model where it looks at the data out of order, and instead relies only on technical indicators (or other features).

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 Time Series 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 Time Series Google Colab notebook: - 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.