arXiv Computation and Language
Quant-Arena: Benchmarking LLM Agents for Quantitative Trading Research
Read the original on arXiv Computation and Language →The Flow has not summarised this story yet — read it at arXiv Computation and Language.
Related stories
arXiv AI
Jul 20
AI Trading: Evaluating Large Language Models for Technical Market Analysis
arXiv:2607. 15414v1 Announce Type: cross Abstract: Large Language Models (LLMs) have emerged as powerful tools for processing the heterogeneous information environments of modern financial markets.
arXiv AI
Oct 5
Trading Strategy Optimization via Textual Gradient
arXiv:2610.03128v1 Announce Type: new Abstract: Quantitative trading strategy design aims to discover trading programs from historical data that remain effective in future markets, which can be viewe...
arXiv AI
Aug 13
FrontierFinance: A Challenging Benchmark for Measuring Frontier Intelligence of Finance Agents
arXiv:2608. 11683v1 Announce Type: new Abstract: AI agents are increasingly deployed for professional investment research, yet no benchmark captures the complexity of the full investor workflow.
arXiv Computation and Language
Oct 15
QuantEval: A Benchmark for Financial Quantitative Tasks in Large Language Models
arXiv:2601.08689v3 Announce Type: replace Abstract: Large Language Models (LLMs) have shown strong capabilities in finance-oriented question answering, yet their evaluation on professional quantitati...
arXiv AI
Jun 9
Beyond Agent Architecture: Execution Assumptions and Reproducibility in LLM-Based Trading Systems
arXiv:2606. 08285v1 Announce Type: new Abstract: Large language models (LLMs) and agentic systems are increasingly proposed for financial trading, yet their reported performance remains difficult to compare because studies vary in data provenance, temporal split discipline, execution timing, turnover treatment, and transaction-cost modeling.
arXiv AI
Jul 15
EVOQUANT: Self-Evolving Verifier-Guided Strategy Optimization for Robust Quantitative Trading
arXiv:2607. 12455v1 Announce Type: new Abstract: Quantitative strategy optimization remains largely manual, requiring domain experts to identify weak signals, tune risk-control rules, and repeatedly validate iterative revisions.