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).

Keywords: large language models, Bayesian networks, options trading, wheel strategy, quantitative finance

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 9.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced probabilistic graphical models (Bayesian Networks) and quantitative risk metrics (Sharpe ratios, drawdowns) but grounds them in a comprehensive 19-year backtest with specific performance data, showing strong empirical execution.

  flowchart TD
    A["Research Goal: Hybrid AI for Options Wheel Trading<br>LLM understanding + Quantitative rigor"] --> B["Input: Market & Historical Data<br>Market conditions, prices, volatility, trends, news<br>18.75-year dataset: 8,919 trades"]
    B --> C["LLM-Generated Bayesian Network<br>Interprets context & builds probabilistic model<br>Selects analogous historical data"]
    C --> D["Transparent Probabilistic Inference<br>Generates explicit probability distributions<br>Calculates risk metrics (27 decision factors)"]
    D --> E["Trade Decision & Execution<br>Strategic option rolling to manage risk"]
    E --> F["Feedback Loop & Performance<br>Outcomes analyze network accuracy & refine structure"]
    F --> G["Key Findings: Superior Performance<br>15.3% Annualized Return (19-year OOS)<br>Sharpe Ratio 1.08 vs 0.62 market<br>Max Drawdown -8.2% vs -60%<br>0% Assignment Rate"]