LiveTradeBench: Seeking Real-World Alpha with Large Language Models

TL;DR

LiveTradeBench provides a realistic environment for evaluating large language models in live trading scenarios, highlighting their decision-making under real-time market uncertainty and revealing gaps in static benchmark assessments.

Contribution

We introduce LiveTradeBench, a novel live trading benchmark for LLMs that incorporates real-time data, multi-asset management, and diverse markets to evaluate decision-making under uncertainty.

Findings

High LMArena scores do not guarantee better trading results.

Models exhibit diverse portfolio styles based on risk and reasoning.

Some LLMs effectively use live signals to adapt trading decisions.

Abstract

Large language models (LLMs) achieve strong performance across benchmarks--from knowledge quizzes and math reasoning to web-agent tasks--but these tests occur in static settings, lacking real dynamics and uncertainty. Consequently, they evaluate isolated reasoning or problem-solving rather than decision-making under uncertainty. To address this, we introduce LiveTradeBench, a live trading environment for evaluating LLM agents in realistic and evolving markets. LiveTradeBench follows three design principles: (i) Live data streaming of market prices and news, eliminating dependence on offline backtesting and preventing information leakage while capturing real-time uncertainty; (ii) a portfolio-management abstraction that extends control from single-asset actions to multi-asset allocation, integrating risk management and cross-asset reasoning; and (iii) multi-market evaluation across structurally distinct environments--U.S. stocks and Polymarket prediction markets--differing in volatility, liquidity, and information flow. At each step, an agent observes prices, news, and its portfolio, then outputs percentage allocations that balance risk and return. Using LiveTradeBench, we run 50-day live evaluations of 21 LLMs across families. Results show that (1) high LMArena scores do not imply superior trading outcomes; (2) models display distinct portfolio styles reflecting risk appetite and reasoning dynamics; and (3) some LLMs effectively leverage live signals to adapt decisions. These findings expose a gap between static evaluation and real-world competence, motivating benchmarks that test sequential decision making and consistency under live uncertainty.