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DeltaHedge: A Multi-Agent Framework for Portfolio Options Optimization

Author

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  • Feliks Ba'nka

    (Warsaw University of Technology, Faculty of Electronics and Information Technology)

  • Jaros{l}aw A. Chudziak

    (Warsaw University of Technology)

Abstract

In volatile financial markets, balancing risk and return remains a significant challenge. Traditional approaches often focus solely on equity allocation, overlooking the strategic advantages of options trading for dynamic risk hedging. This work presents DeltaHedge, a multi-agent framework that integrates options trading with AI-driven portfolio management. By combining advanced reinforcement learning techniques with an ensembled options-based hedging strategy, DeltaHedge enhances risk-adjusted returns and stabilizes portfolio performance across varying market conditions. Experimental results demonstrate that DeltaHedge outperforms traditional strategies and standalone models, underscoring its potential to transform practical portfolio management in complex financial environments. Building on these findings, this paper contributes to the fields of quantitative finance and AI-driven portfolio optimization by introducing a novel multi-agent system for integrating options trading strategies, addressing a gap in the existing literature.

Suggested Citation

  • Feliks Ba'nka & Jaros{l}aw A. Chudziak, 2025. "DeltaHedge: A Multi-Agent Framework for Portfolio Options Optimization," Papers 2509.12753, arXiv.org.
    • Handle: RePEc:arx:papers:2509.12753
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    File URL: http://arxiv.org/pdf/2509.12753
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    References listed on IDEAS

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    1. Zhenhan Huang & Fumihide Tanaka, 2021. "MSPM: A Modularized and Scalable Multi-Agent Reinforcement Learning-based System for Financial Portfolio Management," Papers 2102.03502, arXiv.org, revised Feb 2022.
    2. Wee Ling Tan & Stephen Roberts & Stefan Zohren, 2024. "Deep Learning for Options Trading: An End-To-End Approach," Papers 2407.21791, arXiv.org.
    3. Chavas, Jean-Paul & Li, Jian & Wang, Linjie, 2024. "Option Pricing Revisited: The Role of Price Volatility and Dynamics," 2024 Annual Meeting, July 28-30, New Orleans, LA 343544, Agricultural and Applied Economics Association.
    4. Chavas, Jean-Paul & Li, Jian & Wang, Linjie, 2024. "Option pricing revisited: The role of price volatility and dynamics," Journal of Commodity Markets, Elsevier, vol. 33(C).
    5. Cox, John C. & Ross, Stephen A. & Rubinstein, Mark, 1979. "Option pricing: A simplified approach," Journal of Financial Economics, Elsevier, vol. 7(3), pages 229-263, September.
    6. Zhenhan Huang & Fumihide Tanaka, 2022. "MSPM: A modularized and scalable multi-agent reinforcement learning-based system for financial portfolio management," PLOS ONE, Public Library of Science, vol. 17(2), pages 1-24, February.
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