NeurIPS 2026 · Evaluation and Dataset Track

LiveOption

Evaluating LLM Agents in Structured Option Trading with Nonlinear Payoffs

LiveOption evaluates LLM agents on portfolio overlays, earnings trades and 0DTE options using historical market data.

01 / Task design

Trading tasks

Three tasks cover portfolio management, earnings announcements and same-day expiry. Each includes a recorded example.

02 / Agent design

Analysis and execution

The analysis agent proposes a trade; the execution agent selects contracts and quantities. AVSR summarizes volatility, while QOCR groups contracts by expiry and moneyness.

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Design details · Section 2.3 and Appendix G ↗
03 / QFinZero infrastructure

QFinZero infrastructure

LiveOption uses QFinZero for market data, news and events, and simulated brokerage.

LiveOption

Tasks and agents

Task rules, model inputs, decisions and evaluation.

QFinZeroService hub · REST / Python APIs
UPQMarket data
ESPNews & events
PMBPaper broker

Market and account data go to the agent. Order proposals are passed to the simulator, which records fills and account changes.

UPQ · Market data

Historical bars, option chains and Greeks for the agent and broker simulator.

Market data guide ↗

PMB · Broker simulation

Accounts, orders, fills and margin checks for simulated trading.

Trading guide ↗
04 / Results

Evaluation results

Returns, drawdowns and trading behaviour across eight models. Select a model for its metrics; table columns can be sorted.

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Some models received different inputs, including news. Account for these differences when comparing results.

Supporting resultsResult tablesExpand for metrics and sorting.

Click a column header to sort. Mean returns include 95% bootstrap intervals from 10,000 resamples.

Input differences GPT-5.6-Terra and Gemini-3-Flash used a separate gateway without news headlines in the earnings input. The other six models received headlines. This difference limits model comparisons.
Citation

How to cite

@misc{luo2026liveoptionevaluatingllmagents, title={LiveOption: Evaluating LLM Agents in Structured Option Trading with Nonlinear Payoffs}, author={Haochen Luo and Yifan Li and Binh Minh An and Xiaolong Luo and Zhengzhao Lai and Yuan Zhang and Chen Liu}, year={2026}, eprint={2609.33470}, archivePrefix={arXiv}, primaryClass={cs.AI}, url={https://arxiv.org/abs/2609.33470}, }
This paper does not provide investment advice. The methods discussed may involve substantial risk and could result in significant financial loss.