gym-fx is an open-source Python library that implements a simulated Forex trading environment using the OpenAI Gym interface. It offers support for multiple currency pairs, integration of historical price feeds, technical indicators, and fully customizable reward functions. By providing a standardized API, gym-fx simplifies the process of benchmarking and developing reinforcement learning algorithms for algorithmic trading. Users can configure market slippage, transaction costs, and observation spaces to closely mimic live trading scenarios, facilitating robust strategy development and evaluation.

Prediction Market Agent Tooling provides a modular architecture for creating autonomous prediction market trading agents. It offers connectors for major platforms like Augur and Polymarket, a library of reusable strategy templates, real-time data feeds, a robust backtesting engine, and built-in performance analytics. Users can rapidly prototype algorithms, simulate historical market conditions, and deploy live agents with monitoring utilities, making it ideal for both researchers and quantitative traders.

AI Hedge Fund 5zu provides a complete pipeline for quantitative trading: a customizable environment for simulating multiple asset classes, reinforcement learning–based agent modules, backtesting utilities, real-time market data integration, and risk management tools. Users can configure data sources, define reward functions, train agents on historical data, and evaluate performance across key financial metrics. The framework supports modular strategy development and can be extended to live broker APIs for deploying production-level trading bots.