TraderBench: How Robust Are AI Agents in Adversarial Capital Markets?
arXiv:2603.00285v1 Announce Type: new Abstract: Evaluating AI agents in finance faces two key challenges: static benchmarks require costly expert annotation yet miss the dynamic decision-making central to real-world trading, while LLM-based judges introduce uncontrolled variance on domain-specific tasks. We introduce TraderBench, a benchmark that addresses both issues. It combines expert-verified static tasks (knowledge retrieval, analytical reasoning) with adversarial trading simulations scored purely on realized performance-Sharpe ratio, returns, and drawdown-eliminating judge variance entirely. The framework features two novel tracks: crypto trading with four progressive market-manipulation transforms, and options derivatives scoring across P&L accuracy, Greeks, and risk management. Trading scenarios can be refreshed with new market data to prevent benchmark contamination. Evaluating 13 models (8B open-source to frontier) on ~50 tasks, we find: (1) 8 of 13 models score ~33 on crypt
arXiv:2603.00285v1 Announce Type: new Abstract: Evaluating AI agents in finance faces two key challenges: static benchmarks require costly expert annotation yet miss the dynamic decision-making central to real-world trading, while LLM-based judges introduce uncontrolled variance on domain-specific tasks. We introduce TraderBench, a benchmark that addresses both issues. It combines expert-verified static tasks (knowledge retrieval, analytical reasoning) with adversarial trading simulations scored purely on realized performance-Sharpe ratio, returns, and drawdown-eliminating judge variance entirely. The framework features two novel tracks: crypto trading with four progressive market-manipulation transforms, and options derivatives scoring across P&L accuracy, Greeks, and risk management. Trading scenarios can be refreshed with new market data to prevent benchmark contamination. Evaluating 13 models (8B open-source to frontier) on ~50 tasks, we find: (1) 8 of 13 models score ~33 on crypto with <1-point variation across adversarial conditions, exposing fixed non-adaptive strategies; (2) extended thinking helps retrieval (+26 points) but has zero impact on trading (+0.3 crypto, -0.1 options). These findings reveal that current agents lack genuine market adaptation, underscoring the need for performance-grounded evaluation in finance.
Executive Summary
This article introduces TraderBench, a novel benchmark for evaluating AI agents in finance that addresses the challenges of static benchmarks and LLM-based judges. The framework combines expert-verified static tasks with adversarial trading simulations, featuring two novel tracks: crypto trading and options derivatives. Evaluating 13 models on ~50 tasks, the study finds that current AI agents lack genuine market adaptation, underscoring the need for performance-grounded evaluation in finance. The results suggest that extended thinking helps retrieval but has zero impact on trading. The study's findings have significant implications for the development and deployment of AI agents in finance.
Key Points
- ▸ Introduction of TraderBench, a novel benchmark for evaluating AI agents in finance
- ▸ Combination of expert-verified static tasks and adversarial trading simulations
- ▸ Identification of limitations in current AI agents' market adaptation
Merits
Strength in addressing key challenges in AI evaluation
The study effectively addresses the challenges of static benchmarks and LLM-based judges, providing a more comprehensive evaluation framework.
Novel application of adversarial trading simulations
The study's use of adversarial trading simulations to assess AI agents' performance in real-world trading scenarios is a significant innovation.
Demerits
Limited scope of models and tasks evaluated
The study only evaluates 13 models on ~50 tasks, which may not be representative of the broader field of AI agents in finance.
Potential for benchmark contamination
The study acknowledges the potential for benchmark contamination due to the use of refreshed market data, which may impact the reliability of the results.
Expert Commentary
The study's findings have significant implications for the development and deployment of AI agents in finance. The use of TraderBench as a benchmark for evaluating AI agents is a significant innovation, and the results suggest that current agents lack genuine market adaptation. However, the study's limited scope and potential for benchmark contamination are limitations that should be addressed in future research. Overall, the study provides a valuable contribution to the field of AI in finance and highlights the need for more comprehensive evaluation frameworks.
Recommendations
- ✓ Future studies should evaluate a broader range of models and tasks to increase the validity of the results
- ✓ Developers and regulators should consider the need for performance-grounded evaluation when designing and assessing AI agents in finance