Machine Learning
10 lessons in this track so far, in the order we suggest reading them.
Machine Learning for Trading
AdvancedMachine Learning in TradingAn honest guide to machine learning in trading: where it helps, why it often fails on market data, the main model types and a sound workflow for using it safely.AdvancedSupervised vs Unsupervised LearningSupervised learning trains models on examples with known answers. Learn regression versus classification, how to define trading targets and labels, and key pitfalls.AdvancedRegression and Classification ModelsHow classification models predict up or down moves and trade outcomes. Learn logistic regression, probability thresholds, precision, recall and confusion matrices.AdvancedRandom Forests and Gradient BoostingRandom forests and gradient boosted trees are strong models for tabular trading data. Learn how they work, key settings, feature importance and overfitting risks.AdvancedNeural Networks and Deep LearningNeural networks power deep learning, from LSTMs to transformers. Learn how they work, where they help in trading, especially with text and images, and their risks.AdvancedFeature EngineeringFeatures are the inputs that give trading models a chance. Learn the main feature families, how to make them stationary and comparable, and how to avoid leakage.AdvancedModel Evaluation and Cross-ValidationStandard cross validation leaks future data in time series. Learn time series splits, purging and embargo, combinatorial purged cross validation and good practice.AdvancedWalk-Forward Validation and Preventing OverfittingWalk forward validation retrains a model on a rolling or expanding window and tests it on the next period, just as it would be used live. Learn setup and choices.AdvancedOnline LearningOnline learning updates a model with each new observation instead of retraining in batches. Learn how it works, forgetting factors, drift detection and its risks.AdvancedReinforcement LearningReinforcement learning trains agents to act by rewarding good outcomes. Learn how it applies to trading and execution, how rewards are designed and why it is hard.