Search Results for author:
Found 3 papers, 1 papers with code
A Multimodal Foundation Agent for Financial Trading: Tool-Augmented, Diversified, and Generalist
Notably, FinAgent is the first advanced multimodal foundation agent designed for financial trading tasks.
EarnHFT: Efficient Hierarchical Reinforcement Learning for High Frequency Trading
In stage II, we construct a pool of diverse RL agents for different market trends, distinguished by return rates, where hundreds of RL agents are trained with different preferences of return rates and only a tiny fraction of them will be selected into the pool based on their profitability.
PRUDEX-Compass: Towards Systematic Evaluation of Reinforcement Learning in Financial Markets
Specifically, i) we propose AlphaMix+ as a strong FinRL baseline, which leverages mixture-of-experts (MoE) and risk-sensitive approaches to make diversified risk-aware investment decisions, ii) we evaluate 8 FinRL methods in 4 long-term real-world datasets of influential financial markets to demonstrate the usage of our PRUDEX-Compass, iii) PRUDEX-Compass together with 4 real-world datasets, standard implementation of 8 FinRL methods and a portfolio management environment is released as public resources to facilitate the design and comparison of new FinRL methods.
