Machine Learning Classification of Price Extrema Based on Market Microstructure and Price Action Features. A Case Study of S&P500 E-mini Futures

The study introduces an automated trading system for S\&P500 E-mini futures (ES) based on state-of-the-art machine learning. Concretely: we extract a set of scenarios from the tick market data to train the models and further use the predictions to statistically assess the soundness of the approach. We define the scenarios from the local extrema of the price action. Price extrema is a commonly traded pattern, however, to the best of our knowledge, there is no study presenting a pipeline for automated classification and profitability evaluation. Additionally, we evaluate the approach in the simulated trading environment on the historical data. Our study is filling this gap by presenting a broad evaluation of the approach supported by statistical tools which make it generalisable to unseen data and comparable to other approaches.