Hi FinRL team,
Thanks for maintaining one of the most important open-source projects for financial reinforcement learning. The data, environment, agent, and trading workflow in FinRL has been very helpful for people exploring AI in quantitative finance.
I am building a related but more research-workstation-oriented project: Factor Lab.
Factor Lab focuses on China A-share factor research:
- factor library and factor scoring;
- profit-gap / event-driven stock pools;
- AI-generated roundtable research reports;
- historical stock pool review and strategy replay.
Compared with FinRL, Factor Lab does not focus on reinforcement learning agents directly. It focuses more on candidate generation and research explanation before a trading model is trained or deployed.
Would love to exchange ideas on:
- whether factor-generated stock pools can serve as upstream candidate universes for RL models;
- how to evaluate event-driven signals such as profit-gap candidates;
- whether AI research reports can help explain or debug model decisions;
- how to bridge human-readable research workflows and automated trading agents.
Project link: https://www.afactorlab.com/
Hi FinRL team,
Thanks for maintaining one of the most important open-source projects for financial reinforcement learning. The data, environment, agent, and trading workflow in FinRL has been very helpful for people exploring AI in quantitative finance.
I am building a related but more research-workstation-oriented project: Factor Lab.
Factor Lab focuses on China A-share factor research:
Compared with FinRL, Factor Lab does not focus on reinforcement learning agents directly. It focuses more on candidate generation and research explanation before a trading model is trained or deployed.
Would love to exchange ideas on:
Project link: https://www.afactorlab.com/