AI for trading strategies

TL;DR

This paper compares four machine learning methods with traditional trading strategies in the crude oil market, demonstrating that machine learning can outperform conventional models when applied correctly, especially across different market phases.

Contribution

It provides a comparative analysis of machine learning models versus traditional trading strategies in crude oil, highlighting their performance in various market conditions.

Findings

Machine learning models outperform traditional strategies in crude oil trading.

Model robustness varies across high and low volatility market phases.

Further analysis needed in other markets for generalization.

Abstract

In this bachelor thesis, we show how four different machine learning methods (Long Short-Term Memory, Random Forest, Support Vector Machine Regression, and k-Nearest Neighbor) perform compared to already successfully applied trading strategies such as Cross Signal Trading and a conventional statistical time series model ARMA-GARCH. The aim is to show that machine learning methods perform better than conventional methods in the crude oil market when used correctly. A more detailed performance analysis was made, showing the performance of the different models in different market phases so that the robustness of individual models in high and low volatility phases could be examined more closely. For further investigation, these models would also have to be analyzed in other markets.