Forecasting Forex Time Series with LightGBM and Engineered Features
Summary
The article shows how a conventional LightGBM model can be adapted to forex time-series forecasting by encoding temporal information as features. It discusses lagged OHLC values, rolling statistics, date and calendar attributes, differencing, and transformations intended to address stationarity. It also introduces the Augmented Dickey-Fuller test and explains why stationary targets can be useful when training some forecasting models.
The workflow trains LightGBM regression and classification models, exports a classifier to ONNX, and integrates model inference into a MetaTrader trading robot for Strategy Tester evaluation. The article contrasts this feature-engineering approach with models designed specifically for sequential data, noting that classical models need temporal structure supplied explicitly. It describes implementation steps and testing in the platform, but the supplied material does not establish that the forecast is profitable or reliable out of sample. Model performance remains dependent on feature choices, data handling, and evaluation design.
Key ideas
- Lagged market values provide conventional machine-learning models with information about prior bars.
- Rolling statistics and calendar features can represent recent volatility, trend, and seasonal effects.
- Differencing and stationarity checks can help construct a target variable for forecasting.
- The article trains LightGBM regression and classification models and deploys inference through ONNX in a trading robot.
- Feature engineering enables temporal inputs but does not by itself demonstrate predictive or trading performance.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.