# DeltaHedge: A Multi-Agent Framework for Portfolio Options Optimization

arXiv: https://arxiv.org/abs/2509.12753 · HTML: https://mlsanity.com/paper/2509.12753/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 42 references that are in the map.
- First posted on arXiv 16 Sep 2025.

## Builds on (most cited first, 15 of 15)

- [Large Language Model based Multi-Agents: A Survey of Progress and Challenges](https://mlsanity.com/paper/2402.01680/) (2024)
- [Transformers in Time Series: A Survey](https://mlsanity.com/paper/2202.07125/) (2022)
- [Time Series Forecasting With Deep Learning: A Survey](https://mlsanity.com/paper/2004.13408/) (2020)
- [FinBERT: Financial Sentiment Analysis with Pre-trained Language Models](https://mlsanity.com/paper/1908.10063/) (2019)
- [A Multimodal Foundation Agent for Financial Trading: Tool-Augmented, Diversified, and Generalist](https://mlsanity.com/paper/2402.18485/) (2024)
- [Deep Hedging](https://mlsanity.com/paper/1802.03042/) (2018)
- [FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance](https://mlsanity.com/paper/2011.09607/) (2020)
- [MAPS: Multi-agent Reinforcement Learning-based Portfolio Management System](https://mlsanity.com/paper/2007.05402/) (2020)
- [MSPM: A Modularized and Scalable Multi-Agent Reinforcement Learning-based System for Financial Portfolio Management](https://mlsanity.com/paper/2102.03502/) (2021)
- [Combining Transformer based Deep Reinforcement Learning with Black-Litterman Model for Portfolio Optimization](https://mlsanity.com/paper/2402.16609/) (2024)
- [Fast Agent-Based Simulation Framework with Applications to Reinforcement Learning and the Study of Trading Latency Effects](https://mlsanity.com/paper/2008.07871/) (2020)
- [Developing A Multi-Agent and Self-Adaptive Framework with Deep Reinforcement Learning for Dynamic Portfolio Risk Management](https://mlsanity.com/paper/2402.00515/) (2024)
- [Applying Informer for Option Pricing: A Transformer-Based Approach](https://mlsanity.com/paper/2506.05565/) (2025)
- [Hedging Beyond the Mean: A Distributional Reinforcement Learning Perspective for Hedging Portfolios with Structured Products](https://mlsanity.com/paper/2407.10903/) (2024)
- [Deep Learning for Options Trading: An End-To-End Approach](https://mlsanity.com/paper/2407.21791/) (2024)

## Built on by (most cited first, 3 of 3)

- [On Evaluating Loss Functions for Stock Ranking: An Empirical Analysis With Transformer Model](https://mlsanity.com/paper/2510.14156/) (2025)
- [From Natural Language to Executable Option Strategies via Large Language Models](https://mlsanity.com/paper/2603.16434/) (2026)
- [MultiHedge: Adaptive Coordination via Retrieval-Augmented Control](https://mlsanity.com/paper/2604.24905/) (2026)

## Abstract (arXiv)

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.