Strategy: Neural Networks

Stock Price Prediction AI

Neural Networks are an advanced form of machine learning that work similar to the human brain, and are designed to learn patterns in data. Tey are primarily used for computer vision and natural language processing (NLP) tasks, but also work for tabular data (such as stock prices). They usually take longer to run and require higher-level computers.

To test a Neural Network, 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 Neural Network 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 Neural Network 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.