Forex Data for Machine Learning: Preprocessing Guide

Machine learning is the new frontier in forex. From LSTMs to Random Forests, traders are trying to find an edge in the noise. But if you have ever trained a model, you know that 80% of the work is just cleaning the data. When working with forex machine learning data, your preprocessing steps will determine whether your model actually learns or just memorizes noise.

Feature Engineering: Beyond OHLC

Raw price data is rarely enough for a neural network. You need to create "features" that provide context. Common additions include RSI, MACD, or even custom volatility measures. However, the most important feature is often time itself. Adding "Hour of Day" or "Day of Week" can help a model understand that volatility is higher during the London-New York overlap than it is during the Asian session.

When you get 25 years of data from historicalforexprices.com, you have a massive playground for feature engineering. With 66 currency pairs, you can also create "cross-pair" features. For example, the strength of the Dollar Index (DXY) is a powerful input for any EUR/USD model.

Normalization and Scaling

Machine learning models, especially deep learning ones, hate raw forex prices. A price of 1.0850 means nothing to them. You need to normalize the data. Common methods include "Min-Max Scaling" or "Standardization" (Z-score). For time series, I often prefer "Percent Change" or "Log Returns." This makes the data stationary, which is a requirement for many algorithms.

Train/Test Splits for Time Series

This is where most beginners fail. You cannot use a random split for forex machine learning data. If you use a random 80/20 split, your model will "see" the future during training, leading to impossible results. You must use a "Walk-Forward" or "Time-Based" split. Train on years 1-10, test on year 11. Then train on 1-11, test on year 12. This is why having 25 years of data from historicalforexprices.com is so critical; it gives you enough "years" to do multiple walk-forward cycles without running out of data.

Quality Over Quantity

A sophisticated model will find and exploit every tiny error in your dataset. If your data has a gap that was filled with an average price, the model will "learn" that average, which doesn't exist in the real world. This is why I always use historicalforexprices.com. Their data is clean, consistent, and professional-grade, ensuring that your machine learning efforts are spent on model architecture rather than fixing broken CSV files. For serious forex machine learning data, there is no substitute for high-quality history.

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