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.
24 documents
This tutorial demonstrates a graph convolutional policy, GPM, inside a reinforcement-learning portfolio workflow. It loads historical stock features and a sector and industry graph, then reduces the graph to nodes within two hops of the selected portfolio…
The document presents daily portfolio rebalancing as a Markov decision process. An agent selects nonnegative weights for Dow 30 stocks, normalized to sum to one, using a state that combines a rolling covariance matrix with MACD, RSI, CCI, and ADX indicators.…
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 introductory page presents FinRL as a framework for applying deep reinforcement learning to stock trading. It directs readers to a sequence of example notebooks covering data preparation, model training, and backtesting, framing them as a way to follow…
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 tutorial outlines an end-to-end FinRL workflow for training and comparing deep reinforcement learning agents on Dow 30 equities. It describes downloading and preprocessing market data, adding technical indicators plus VIX and a turbulence measure,…
This quick-start example outlines a FinRL workflow using a stock-trading environment and a Dow 30 ticker universe. A command-line mode selects among training, testing, and trading paths. The example configures daily Yahoo Finance data, technical indicators,…
The tutorial presents a deep reinforcement learning workflow for trading a portfolio of Dow 30 stocks. It frames trading as a Markov decision process: the agent observes prices and engineered features, including MACD and RSI, then outputs per-stock actions…
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…
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…
The document explains how FinRL models automated stock trading as a Markov decision process. An agent observes market prices and features, acts in a simulated environment, receives rewards, and adjusts its policy to pursue higher cumulative reward. The…
This notebook demonstrates a portfolio optimization workflow using FinRL’s PortfolioOptimizationEnv and the EIIE policy architecture. It downloads data for ten Brazilian stocks, scales each stock’s series, trains a policy-gradient agent on an earlier period,…
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 overview presents FinRL-Meta as a framework for data-driven reinforcement learning in finance. It separates the workflow into data, market-environment, and agent layers, with interfaces that allow components to be replaced or customized. It also…
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…
This script outlines a FinRL training workflow for stock trading. It reads training data, derives the stock universe size and state-space dimensions, and configures a stock-trading environment with technical indicators, transaction costs, initial capital,…
FinRL-Meta addresses a research infrastructure problem: deep reinforcement learning has potential in finance, but researchers need realistic market environments and shared benchmarks to develop and compare methods. The document contrasts this need with…
This tutorial presents a FinRL workflow for applying deep reinforcement learning to a portfolio of Dow Jones stocks. It formulates trading as a Markov decision process: the agent observes market features and holdings, chooses buy, sell, or hold actions, and…
This tutorial outlines a paper trading workflow for a FinRL stock trading agent. It begins with installing the library and preparing Alpaca paper account credentials, then introduces a Proximal Policy Optimization agent with actor and critic networks. The…
The tutorial presents a workflow for modeling single-stock trading as a Markov decision process and training an agent with deep reinforcement learning. In its example, the agent trades Apple shares using an action space that permits buying, holding, or…
FinRL is presented as an open-source framework for researching financial reinforcement learning. Its core workflow connects market environments, deep reinforcement learning agents, and financial applications in a train-test-trade pipeline. The repository…
The document explains FinRL as a modular framework organized into market environments, deep reinforcement learning agents, and trading applications. The environment layer supplies interfaces to the agent layer; agents interact with simulated markets through…
This script describes an evaluation workflow for trained stock trading agents based on an ensemble reinforcement learning study. It loads trained A2C, DDPG, PPO, TD3, and SAC models, applies them to a stock trading environment, and records account values and…