Tag
This paper introduces EvolveTrade, a framework that treats a trading agent's system prompt as a self-evolving policy to improve performance by dynamically tuning it based on feedback from trading decisions and outcomes.
FPILOT is a plugin inference-time optimization framework for RL trading agents that leverages price forecasts without retraining, yielding consistent improvements in returns and risk-adjusted metrics on the TradeMaster DJ30 benchmark.
This paper introduces SHARP, a neuro-symbolic framework for financial trading agents that uses structured, human-auditable rubrics for policy optimization to improve robustness and transparency in noisy market environments.
This paper introduces TradingAgents, a multi-agent LLM framework that simulates real-world trading firms to improve stock trading performance. It utilizes specialized agents for analysis and risk management, demonstrating superior results in cumulative returns and Sharpe ratio compared to baselines.