Machine Learning Models

Traditional strategies are "rules-based" (e.g., *if RSI < 30, buy*). Machine Learning (ML) is "data-driven" (e.g., *based on the last 10 years of price/volume data, identify the statistical probability of a move in the next 1 hour*). ML models analyze vast datasets to find patterns—such as correlations between market sectors or hidden volatility signals—that a human would never notice.

• Supervised Learning: The most common approach. You train the model on historical data where the "target" (the future price or direction) is already known. Examples include Random Forests and Gradient Boosting for predicting price movements.
• Unsupervised Learning: The model looks for hidden structures in data without pre-defined labels. This is often used for Market Regime Detection (identifying if the market is trending, ranging, or in crisis).
• Reinforcement Learning: The model learns by trial and error, receiving "rewards" for profitable trades and "penalties" for losses. This is the cutting edge of trading automation.

An ML model is only as good as its data. If you feed it noisy, irrelevant, or biased data, it will produce useless predictions. Success in ML trading comes down to Feature Engineering—the ability to create clean, meaningful inputs (indicators, macro data, sentiment scores) that actually have predictive power over price.