LightGBM for Trading: What the Model Can and Cannot Do
An introduction to gradient boosting for market research, with practical cautions about noisy financial data.
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LightGBM for Trading
Machine learning, data quality and realistic validation for trading models.
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Feature Engineering and Data Leakage in Trading Models
Build time-aware market features and avoid information leakage that can make a trading model look unrealistically accurate.
Read articleTime-Series Validation for Trading Models: A Practical Guide
Use chronological validation to evaluate a trading model without allowing future observations to influence past decisions.
Read articleChoosing Labels and Prediction Horizons for Market Models
Define what a model predicts, when that prediction is made and how its outcome connects to a measurable trading decision.
Read articleMonitoring LightGBM Model Drift in Market Data
Track changing inputs and model behavior over time, and define a careful review process before retraining.
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