aiqbench analysis
This research introduces LiveOption, a novel evaluation framework designed specifically to test LLM agents in the complex environment of option trading, where nonlinear payoffs and multi-leg strategies differ significantly from standard equity markets. By providing reproducible environments with standardized protocols, the study highlights that current AI agents often fail to achieve competitive returns when subjected to realistic execution constraints and structural decision-making challenges. The work establishes a critical testbed for advancing machine learning applications in financial derivatives beyond simple directional prediction.
LiveOption: Evaluating LLM Agents in Structured Option Trading with Nonlinear Payoffs
arXiv:2609.33470v1 Announce Type: new Abstract: Large language models (LLMs) and multi-agent systems (MAS) have shown promise in financial decision-making, yet existing evaluations focus on equity trading and primarily assess directional prediction, overlooking the structural complexity of derivative markets. Option trading introduces fundamentally different challenges, including nonlinear payoffs and multi-leg strategy construction, requiring structured decisions rather than simple directional bets. We introduce LiveOption, an evaluation framework for LLM-based agents in option trading. LiveOption formulates the problem as structured sequential decision-making under realistic execution and capital constraints, and provides a reproducible environment with standardized interaction protocols. The framework includes three task suites covering portfolio overlays, event-driven earnings trading, and 0DTE intraday trading. We further propose a hierarchical metric suite that evaluates action validity, decision quality, risk characteristics, and outcome-level performance. Experiments show that current agents often fail to achieve competitive returns in most scenarios. LiveOption offers a principled testbed for evaluating structured decision-making beyond outcome-based metrics.
Abstract:Large language models (LLMs) and multi-agent systems (MAS) have shown promise in financial decision-making, yet existing evaluations focus on equity trading and primarily assess directional prediction, overlooking the structural complexity of derivative markets. Option trading introduces fundamentally different challenges, including nonlinear payoffs and multi-leg strategy construction, requiring structured decisions rather than simple directional bets. We introduce LiveOption, an evaluation framework for LLM-based agents in option trading. LiveOption formulates the problem as structured sequential decision-making under realistic execution and capital constraints, and provides a reproducible environment with standardized interaction protocols. The framework includes three task suites covering portfolio overlays, event-driven earnings trading, and 0DTE intraday trading. We further propose a hierarchical metric suite that evaluates action validity, decision quality, risk characteristics, and outcome-level performance. Experiments show that current agents often fail to achieve competitive returns in most scenarios. LiveOption offers a principled testbed for evaluating structured decision-making beyond outcome-based metrics.
Artificial Intelligence (cs.AI); Computational Finance (q-fin.CP)
arXiv:2609.33470 [cs.AI]
(or arXiv:2609.33470v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.33470 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 246 words
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