FinRL-X is a next-generation, AI-native quantitative trading infrastructure that redefines how researchers and practitioners build, test, and deploy algorithmic trading strategies. FinRL-X is not just ...
I am a user of FinRL and have benefited greatly from both the FinRL framework and your previous publications. Thank you very much for your excellent work and open-source contributions to the community ...
He serves as a senior PC member for NeurIPS, ICML, ICLR, AAAI, IJCAI, AISTATS, and ICAIF. He organized FinRL Competition at ACM ICAIF 2023/2024/2025, the First/Second Workshop on Quantum Tensor ...
Abstract: This paper advances the FinRL-DeepSeek framework by introducing a market-adaptive modeling approach to improve automated stock trading agents. While FinRL-DeepSeek integrates stock prices ...
Xiao-Yang Liu is a Ph.D. candidate in the Department of Electrical Engineering at Columbia University. He received his M.S. in electrical engineering from Columbia University in 2018 and his Ph.D. in ...
BASF Nederland B.V., Arnhem, Netherlands. Understanding and predicting stock price developments and their causes has always been desirable in the financial world. Nowadays, most financial institutions ...
"I visualise a time when we will be to robots what dogs are to humans, and Iām rooting for the machines." ā Claude Shannon "We can only see a short distance ahead, but we can see plenty there that ...
The article discusses the application of deep reinforcement learning in automating stock market trading. FinRL serves as a specialised library tailored for developing and testing trading strategies ...
A Deep Reinforcement Learning Library for Automated Trading in Quantitative Finance. NeurIPS 2020. Please star. š„ FinRL: A Deep Reinforcement Learning Library for Quantitative Finance FinRL is an ...
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