Toward profitable energy futures trading strategies using reinforcement learning incorporating disagreement and connectedness methods enabled by large language models Article Swipe
Tianxiang Cui
,
Yujian Ye
,
Yiran Li
,
Nanjiang Du
,
Xingke Song
,
Yicheng Zhu
,
Xiaoying Yang
,
Goran Štrbac
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1016/j.egyai.2025.100562
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1016/j.egyai.2025.100562
The energy market plays a fundamental role in the global economy, shaping energy prices, inflation, and financial stability across nations. As the world transitions toward low-carbon energy solutions, optimizing trading strategies in this complex and dynamic market has become increasingly critical for investors, polic-ymakers, and energy brokers. Traditional data-driven models often struggle to capture the multifaceted and interconnected factors influencing energy markets, such as macroeconomic conditions, investor sentiment, and the accelerating shift toward decarbonization. To address these challenges, a novel framework is proposed that combines reinforcement learning with methods for analyzing disagreement and connectedness, alongside advanced natural language processing techniques, to develop trading strategies for energy markets. The proposed method integrates structured time-series data with unstructured textual data to incorporate diverse factors, including the interplay between economic influences, green energy transitions, and investor sentiment. The proposed framework also employs a chain-of-reasoning technique to classify investor types, distinguishing between sentiment-driven disagreement and cross-disagreement, and utilizes a connectedness-based method to model the interrelationships among market variables, providing a comprehensive understanding of market dynamics. As a showcase, this framework is applied to the West Texas Intermediate crude oil market, demonstrating its ability to outperform traditional price-prediction-based trading strategies. Experimental results highlight that the proposed framework delivers superior investment returns while addressing key limitations of existing models in terms of data integration and flexibility. This study underscores the potential of the proposed framework as a robust and adaptable solution for optimizing trading strategies across the broader energy market, with particular relevance to the global transition toward sustainable energy systems.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.egyai.2025.100562
- OA Status
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- 1
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4412611509Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.egyai.2025.100562Digital Object Identifier
- Title
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Toward profitable energy futures trading strategies using reinforcement learning incorporating disagreement and connectedness methods enabled by large language modelsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-07-23Full publication date if available
- Authors
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Tianxiang Cui, Yujian Ye, Yiran Li, Nanjiang Du, Xingke Song, Yicheng Zhu, Xiaoying Yang, Goran ŠtrbacList of authors in order
- Landing page
-
https://doi.org/10.1016/j.egyai.2025.100562Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.1016/j.egyai.2025.100562Direct OA link when available
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Futures contract, Social connectedness, Reinforcement learning, Energy (signal processing), Computer science, Reinforcement, Artificial intelligence, Financial economics, Psychology, Economics, Mathematics, Social psychology, StatisticsTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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