AI-Powered Energy Algorithmic Trading: Integrating Hidden Markov Models with Neural Networks
Tiago Monteiro
TL;DR
This paper presents a novel AI-driven trading system combining Hidden Markov Models and neural networks with portfolio optimization, demonstrating high returns and risk management during volatile periods in energy stocks.
Contribution
It introduces a dual-model approach integrating HMM and neural networks with Black-Litterman optimization for energy stock trading, a novel combination in quantitative finance.
Findings
Achieved 83% return during COVID period
Attained a Sharpe ratio of 0.77
Effective risk management during volatile markets
Abstract
In quantitative finance, machine learning methods are essential for alpha generation. This study introduces a new approach that combines Hidden Markov Models (HMM) and neural networks, integrated with Black-Litterman portfolio optimization. During the COVID period (2019-2022), this dual-model approach achieved a 83% return with a Sharpe ratio of 0.77. It incorporates two risk models to enhance risk management, showing efficiency during volatile periods. The methodology was implemented on the QuantConnect platform, which was chosen for its robust framework and experimental reproducibility. The system, which predicts future price movements, includes a three-year warm-up to ensure proper algorithm function. It targets highly liquid, large-cap energy stocks to ensure stable and predictable performance while also considering broker payments. The dual-model alpha system utilizes log returns…
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Taxonomy
TopicsStock Market Forecasting Methods