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AI-Powered Energy Algorithmic Trading: Integrating Hidden Markov Models with Neural Networks

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  • Tiago Monteiro

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 to select the optimal state based on the historical performance. It combines state predictions with neural network outputs, which are based on historical data, to generate trading signals. This study examined the architecture of the trading system, data pre-processing, training, and performance. The full code and backtesting data are available under the QuantConnect terms.

Suggested Citation

  • Tiago Monteiro, 2024. "AI-Powered Energy Algorithmic Trading: Integrating Hidden Markov Models with Neural Networks," Papers 2407.19858, arXiv.org, revised Aug 2024.
    • Handle: RePEc:arx:papers:2407.19858
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    References listed on IDEAS

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    1. Chen, Wen-Bin & Li, Xiao-Yang & Kang, Rui, 2022. "Integration for degradation analysis with multi-source ADT datasets considering dataset discrepancies and epistemic uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    2. Lennart Oelschlager & Timo Adam, 2020. "Detecting bearish and bullish markets in financial time series using hierarchical hidden Markov models," Papers 2007.14874, arXiv.org.
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