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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.