Agentic Quantitative Trading: A Survey of Workflows, Systems, and Evaluation

📅 2026-08-31

📈 Citations: 0

✨ Influential: 0

🤖 AI Summary

本文探讨了代理型量化交易在五个阶段的应用，指出当前系统多集中于信号发现而较少整合到其他环节，并提出未来需加强全流程整合与评估。

📝 Abstract

Quantitative trading is moving from isolated predictive models toward agentic workflows that combine reasoning, tool use, memory, and feedback. This survey reviews agentic quantitative trading across five stages: factor mining, signal discovery, portfolio construction, order execution, and risk management. We further examine agentic quant trading systems through architecture, coordination, and adaptation, while comparing benchmarks across strategy construction, offline trading, live market evaluation, and reliability assessment. Our review finds that current systems remain concentrated on signal discovery, while complete integration with portfolio construction, execution, and risk control is still uncommon. Multi-agent systems also rely heavily on aggregation despite increasingly diverse workflow structures. Benchmark evidence further shows that strong model or forecasting capability does not reliably translate into trading performance under live market conditions and reliability controls. We conclude with future directions for more complete trading workflows, stronger coordination, and evaluation matched to the capability being assessed.

Problem

Research questions and friction points this paper is trying to address.

agentic quantitative trading

integration

multi-agent systems

trading performance

Innovation

Methods, ideas, or system contributions that make the work stand out.

Agentic Quantitative Trading

Workflow Integration

Signal Discovery

Portfolio Construction