Options Trading

A Hybrid Architecture for Options Wheel Strategy Decisions: LLM-Generated Bayesian Networks for Transparent Trading ArXiv ID: 2512.01123 “View on arXiv” Authors: Xiaoting Kuang, Boken Lin Abstract Large Language Models (LLMs) excel at understanding context and qualitative nuances but struggle with the rigorous and transparent reasoning required in high-stakes quantitative domains such as financial trading. We propose a model-first hybrid architecture for the options “wheel” strategy that combines the strengths of LLMs with the robustness of a Bayesian Network. Rather than using the LLM as a black-box decision-maker, we employ it as an intelligent model builder. For each trade decision, the LLM constructs a context-specific Bayesian network by interpreting current market conditions, including prices, volatility, trends, and news, and hypothesizing relationships among key variables. The LLM also selects relevant historical data from an 18.75-year, 8,919-trade dataset to populate the network’s conditional probability tables. This selection focuses on scenarios analogous to the present context. The instantiated Bayesian network then performs transparent probabilistic inference, producing explicit probability distributions and risk metrics to support decision-making. A feedback loop enables the LLM to analyze trade outcomes and iteratively refine subsequent network structures and data selection, learning from both successes and failures. Empirically, our hybrid system demonstrates effective performance on the wheel strategy. Over nearly 19 years of out-of-sample testing, it achieves a 15.3% annualized return with significantly superior risk-adjusted performance (Sharpe ratio 1.08 versus 0.62 for market benchmarks) and dramatically lower drawdown (-8.2% versus -60%) while maintaining a 0% assignment rate through strategic option rolling. Crucially, each trade decision is fully explainable, involving on average 27 recorded decision factors (e.g., volatility level, option premium, risk indicators, market context). ...

DeltaHedge: A Multi-Agent Framework for Portfolio Options Optimization ArXiv ID: 2509.12753 “View on arXiv” Authors: Feliks Bańka, Jarosław A. Chudziak 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. ...

High-Frequency Options Trading | With Portfolio Optimization ArXiv ID: 2408.08866 “View on arXiv” Authors: Unknown Abstract This paper explores the effectiveness of high-frequency options trading strategies enhanced by advanced portfolio optimization techniques, investigating their ability to consistently generate positive returns compared to traditional long or short positions on options. Utilizing SPY options data recorded in five-minute intervals over a one-month period, we calculate key metrics such as Option Greeks and implied volatility, applying the Binomial Tree model for American options pricing and the Newton-Raphson algorithm for implied volatility calculation. Investment universes are constructed based on criteria like implied volatility and Greeks, followed by the application of various portfolio optimization models, including Standard Mean-Variance and Robust Methods. Our research finds that while basic long-short strategies centered on implied volatility and Greeks generally underperform, more sophisticated strategies incorporating advanced Greeks, such as Vega and Rho, along with dynamic portfolio optimization, show potential in effectively navigating the complexities of the options market. The study highlights the importance of adaptability and responsiveness in dynamic portfolio strategies within the high-frequency trading environment, particularly under volatile market conditions. Future research could refine strategy parameters and explore less frequently traded options, offering new insights into high-frequency options trading and portfolio management. ...

Deep Learning for Options Trading: An End-To-End Approach ArXiv ID: 2407.21791 “View on arXiv” Authors: Unknown Abstract We introduce a novel approach to options trading strategies using a highly scalable and data-driven machine learning algorithm. In contrast to traditional approaches that often require specifications of underlying market dynamics or assumptions on an option pricing model, our models depart fundamentally from the need for these prerequisites, directly learning non-trivial mappings from market data to optimal trading signals. Backtesting on more than a decade of option contracts for equities listed on the S&P 100, we demonstrate that deep learning models trained according to our end-to-end approach exhibit significant improvements in risk-adjusted performance over existing rules-based trading strategies. We find that incorporating turnover regularization into the models leads to further performance enhancements at prohibitively high levels of transaction costs. ...