FinRL – Reinforcement Learning in Finance
Join the Hudson and Thames reading group: https://hudsonthames.org/reading-group/
Access the slides for this reading group here: https://drive.google.com/file/d/11Snla6sHcX2YPSxMq7AsIy9hA6JaYQrCliQbyoxin3Q/view
In this Reading Group session we explore the innovative FinRL library, which aims to democratise quantitative finance and stock trading strategy development, especially for beginners. The library is built upon deep reinforcement learning (DRL) techniques and consists of three layers: market environments, agents, and applications. It provides a comprehensive suite of tools, including diverse stock market datasets, neural network-based agent training, and robust backtesting capabilities. Moreover, FinRL incorporates crucial trading constraints like transaction costs, market liquidity, and risk aversion.
Join the Hudson and Thames reading group: https://hudsonthames.org/reading-group/
Access the slides for this reading group here: https://drive.google.com/file/d/11Snla6sHcX2YPSxMq7AsIy9hA6JaYQrCliQbyoxin3Q/view
In this Reading Group session we explore the innovative FinRL library, which aims to democratise quantitative finance and stock trading strategy development, especially for beginners. The library is built upon deep reinforcement learning (DRL) techniques and consists of three layers: market environments, agents, and applications. It provides a comprehensive suite of tools, including diverse stock market datasets, neural network-based agent training, and robust backtesting capabilities. Moreover, FinRL incorporates crucial trading constraints like transaction costs, market liquidity, and risk aversion.
