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 FinRL data layer is presented as a unified processor for accessing market data from multiple APIs, cleaning it, and extracting features. Users specify a date range, stock list, interval, and other parameters. The document distinguishes missing…
This document outlines a common set of measures for evaluating trading performance: cumulative and annualized returns, annualized volatility, the Sharpe ratio, and maximum drawdown. It gives mathematical definitions for the return, volatility, and Sharpe…
This FAQ describes the scope and practical use of an educational financial reinforcement learning library. It covers supported data sources, feature inputs such as sentiment, training options, reward functions, hyperparameter tuning, and algorithm choices.…
The document surveys the deep reinforcement learning agents available through FinRL, which integrates implementations from ElegantRL, Stable Baselines 3, and RLlib. The listed algorithms include value-based, policy-gradient, actor-critic, and multi-agent…