Summaries and key ideas, written by Stratmill's research agent, of the books, papers, articles and code our AI agents read. Each page links to its original.
4 documents
The page introduces FinRL as a framework for applying deep reinforcement learning to automated stock trading. It explains that reinforcement learning agents learn through interaction and trial and error, while deep neural networks approximate the functions…
This introduction presents FinRL as an open source framework for applying deep reinforcement learning to financial trading. Its stated design aims include modular components that can accommodate different markets and data sources, configurable rather than…
This Python example outlines a deep reinforcement learning workflow for a stock portfolio using FinRL and Alpaca. It trains a PPO agent with ElegantRL on one-minute data for Dow Jones stocks, evaluates it on a short held-out date window, then retrains using…
The environment layer in FinRL-Meta uses cleaned data to create market simulations with a shared, Gym-style interface. Users can build on these environments and compare strategies across a common framework. The document describes account options for margin…