Risk-Adjusted Performance of Random Forest Models in High-Frequency Trading

TL;DR

This study evaluates the effectiveness of technical indicators combined with random forest models in high-frequency trading, revealing limited predictive power and emphasizing the importance of adaptive features and robust testing.

Contribution

It provides an empirical analysis showing that traditional technical indicators have limited predictive value in high-frequency trading models, highlighting the dominance of price-based features.

Findings

Technical indicators account for only 14-15% of feature importance.

Models underperform compared to buy-and-hold strategies in out-of-sample tests.

Risk-adjusted metrics improve with technical indicators but do not outperform simple strategies.

Abstract

Because of the theoretical challenges posed by the Efficient Market Hypothesis to technical analysis, the effectiveness of technical indicators in high-frequency trading remains inadequately explored, particularly at the minute-level frequency, where effects of the microstructure of the market dominate. This study evaluates the integration of traditional technical indicators with random forest regression models using minute-level SPY data, analyzing 13 distinct model configurations. Our empirical results reveal a stark contrast between in-sample and out-of-sample performance, with $R^2$ values deteriorating from 0.749--0.812 during training to negative values in testing. A feature importance analysis demonstrates that primary price-based features dominate the predictions made by the model, accounting for over 60% of the importance, while established technical indicators, such as RSI and Bollinger Bands, account for only 14%--15%. Although the indicator-enhanced models achieved superior risk-adjusted metrics, with Rachev ratios between 0.919 and 0.961, they consistently underperformed a simple buy-and-hold strategy, generating returns ranging from -2.4% to -3.9%. These findings challenge conventional assumptions about the usefulness of technical indicators in algorithmic trading, suggesting that in high-frequency contexts, they may be more relevant to risk management rather than to predicting returns. For practitioners and researchers, our findings indicate that successful high-frequency trading strategies should focus on adaptive feature selection and regime-specific modeling rather than relying on traditional technical indicators, as well as indicating the critical importance of robust out-of-sample testing in the development of a model.