About FinRL

Efficiently automate trading. We continuously develop and share codes for finance.

 

FinRL is the first open-source framework to demonstrate the great potential of applying deep reinforcement learning in quantitative finance. We help practitioners establish the development pipeline of trading strategies using deep reinforcement learning (DRL).

FinRL Ecosystem

Deep Reinforcement Learning to Automate Trading in Quantitative Finance
  • FinRL ecosystem provides a way to take advantage of large-scale financial data (market data, alternative data, indexes and labels).
  • possibly adapts to much more complex neural networks architectures and financial simulations.
  • through GPU-acceleration techniques, reduces latency for future financial simulations.

Our projects

FinRL

Framework

A full pipeline that automatically streamlines the development of algorithmic trading strategies.

ElegantRL

Algorithm

A DRL algorithm library that is developed for researchers and practitioners with finance-oriented optimizations.

FinRL-Meta

Data-Driven

A Universe of Market Environments for Financial Reinforcement Learning.

FinRL-Podracer

Cloud-Native

A high-performance and scalable solution for RLOps in finance.

PORTFOLIO

A showcase of our works

EXPERTISE

How we can be useful for you

Check Out Our Open-Source Solutions!

Open-Source software developers and programmers are the backbone of today’s humanity and humanitarian technology.

FinRL provides an open-source ecosystem that features Deep Reinforcement Learning in finance for all-level users.

Entry-level users

FinRL provides demonstrative and educational materials to help beginners such as students and entry-level professionals to walk through the DRL for finance pipeline.

Intermediate-level users

FinRL provides lightweight and scalable DRL algorithms with finance-oriented optimizations for full-stack developers and professionals.

Advanced-level users

FinRL delivers cloud-native solutions with high performance and high scalability training for investment banks and hedge funds.

Cloud-Native Solution

FinRL-Podracer can obtain a profitable trading agent in 10 minutes on an NVIDIA DGX SuperPOD cloud with 80 A100 GPUs, for a stock trend prediction task on NASDAQ-100 constituent stocks with minute-level data over 5 years.

Market Simulations

Reduce data processing burden; Reduce simulation-to-reality gap; Provide benchmark performance

OUR COLLABORATORS

World’s best research organizations
logo_size_invert

AI4Finance-Foundation

E: info@ai4finance.net

YouTubeLinkedin

AI community has accumulated an open-source code ocean over the past decade. We believe applying these intellectual and engineering properties to finance will initiate a paradigm shift from the conventional trading routine to an automated machine learning approach, even RLOps in finance.

27141638197002_.pic_hd

Columbia University

E: hy2500@columbia.edu

LinkedinYouTubeWebsite

A lot of researchers from Columbia University are collaborating with us to build an open-source community and conduct academic research.

CONTACT

Start a project with us!

OUR LOCATION

AI4Finance-Foundation

530W 120TH ST CEPSR RM 718

NEW YORK CITY, NEW YORK

10027

CALL US

+001

DROP US A LINE

ifinrl@gmail.com

Let's start a project together!

Please leave your name, email, phone and message. Thanks.

    LINKEDIN

    @ai4finance

    TWITTER

    @ai4finance

    FACEBOOK

    @ai4finance

    SLACK

    @ai4finance