Machine Learning - We use the raw price data (open/high/low/close) plus technical indicators such as Moving Average, Bollinger Bands, RSI, and more, to create an AI model that generates buy and sell predictions. The other strategies on this page then try to improve on this basic model.
X In A Row - Only enter the trade when the stock goes up or down X number of bars in a row.
Big Move - Only enter the trade when the stock has made a big recent up or down move.
Symbolic Regression - A type of genetic programming that tries to find the best mathematical expression to represent the data. The resulting model is a plain mathematical formula that can be easily used to make predictions.
Evolutionary Algorithms - Inspired by Darwin's theories about nature and humans, it mimics biological evolution. A random group of possible solutions is initially created and with each new generation, the best solutions fight for survival until eventually a winner is chosen.
AutoML - Dozens of different machine learning methods are tested automatically to see which one obtains the best accuracy.
Dynamic Time Warping - A method based on the theory that history repeats itself, where it finds time periods in the historical data similar to the current one, to predict if the same thing will happen again.
Chart Pattern Detection - A more modern version of Dynamic Time Warping where image recognition (computer vision) is used to detect historical chart patterns that are similar to the current one.
mathematical formula, no machine learning program needs to be installed to run it.
Price Bar Length - What time period works best for the stock quotes? Should we trade every minute? Every hour? Every day?
Reinforcement Learning (RL) - Turns stock trading into a game, where the AI wins if it makes trades that are profitable. It learns to buy and sell whenever it thinks is best, just like a human would.
Pair Trading - Out of a basket of 30 stocks, the AI predicts the best and worst stock. We then buy the best stock and sell short the worst stock. This helps hedge against the randomness of the overall market move.
Data Cleaning - We experiment with different ways of handling the stock price data, such as how to remove outliers, how to handle missing prices, and how to deal with imbalanced data.
Social Media Signals - We create an indicator derived from postings about a stock on Twitter, Facebook, Instagram, and TikTok, using a machine learning technique called sentiment analysis to decide if each posting is good, bad, or neutral.