Skip to content

Latest commit

 

History

History
14 lines (13 loc) · 3.11 KB

FinRL_papers.md

File metadata and controls

14 lines (13 loc) · 3.11 KB

FinRL Ecosystem: papers

Papers by the Columbia research team can be found at Google Scholar.

Papers by the AI4Finance community are listed as follows:

Title Conference Link Citations Year
FinRL-Meta: A Universe of Near-Real Market Environments for Data-Driven Deep Reinforcement Learning in Quantitative Finance NeurIPS 2021 Data-Centric AI Workshop paper: https://arxiv.org/abs/2112.06753 ;
code: https://github.com/AI4Finance-Foundation/FinRL-Meta
2 2021
Explainable deep reinforcement learning for portfolio management: An empirical approach ICAIF 2021 : ACM International Conference on AI in Finance paper: https://arxiv.org/abs/2111.03995;
code: https://github.com/AI4Finance-Foundation/FinRL
1 2021
FinRL-Podracer: High performance and scalable deep reinforcement learning for quantitative finance ICAIF 2021 : ACM International Conference on AI in Finance paper: https://arxiv.org/abs/2111.05188;
code: https://github.com/AI4Finance-Foundation/FinRL_Podracer
2 2021
FinRL: Deep reinforcement learning framework to automate trading in quantitative finance ICAIF 2021 : ACM International Conference on AI in Finance paper: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3955949;
code: https://github.com/AI4Finance-Foundation/FinRL
7 2021
FinRL: A deep reinforcement learning library for automated stock trading in quantitative finance NeurIPS 2020 Deep RL Workshop paper: https://arxiv.org/abs/2011.09607;
code: https://github.com/AI4Finance-Foundation/FinRL
25 2020
Deep reinforcement learning for automated stock trading: An ensemble strategy ICAIF 2020 : ACM International Conference on AI in Finance paper: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3690996;
repo: https://github.com/AI4Finance-Foundation/Deep-Reinforcement-Learning-for-Automated-Stock-Trading-Ensemble-Strategy-ICAIF-2020;
code: https://github.com/AI4Finance-Foundation/FinRL-Meta/blob/master/tutorials/2-Advance/FinRL_Ensemble_StockTrading_ICAIF_2020/FinRL_Ensemble_StockTrading_ICAIF_2020.ipynb
46 2020
Multi-agent reinforcement learning for liquidation strategy analysis ICML 2019 Workshop on AI in Finance: Applications and Infrastructure for Multi-Agent Learning paper: https://arxiv.org/abs/1906.11046;
repo: https://github.com/AI4Finance-Foundation/Liquidation-Analysis-using-Multi-Agent-Reinforcement-Learning-ICML-2019;
code: https://github.com/AI4Finance-Foundation/FinRL-Meta/blob/master/tutorials/2-Advance/execution_optimizing/execution_optimizing.ipynb
19 2019
Practical deep reinforcement learning approach for stock trading NeurIPS 2018 Workshop on Challenges and Opportunities for AI in Financial Services paper: https://arxiv.org/abs/1811.07522;
code: https://github.com/AI4Finance-Foundation/DQN-DDPG_Stock_Trading
87 2018