A simple, easy, customizable Gymnasium environment for trading.

OpenAI gym environments for training RL Agents on @OpenBB-finance Data

DQN stock-trading agent with a custom Gymnasium environment and yfinance data.

Flexible reinforcement learning environment for simulating financial trading scenarios. Based on gymnasium

Gimnasium environment focus on trading strategies

Reinforcement Learning trading bot for NQ futures using PPO + Gymnasium

A stock trading agent using Proximal Policy Optimization (PPO) with Gymnasium and Stable-Baselines3.

Production-ready Multi-Agent RL Trading System using PyTorch, Stable-Baselines3, Gymnasium & Streamlit. Simulates trading, trains PPO/A2C/DQN agents, tracks ROI, cash, trades, learns multi-stock policies. Ideal for quant prototyping, strategy research, and RL engineering demos!

Autonomous trading agent using Reinforcement Learning (PPO/DQN) to execute data driven strategies in financial markets. Built with Gymnasium, Stable-Baselines3, and integrated with real time data APIs.

A Reinforcement Learning (PPO) based Statistical Arbitrage bot for Pairs Trading. Utilizes Engle-Granger cointegration tests and custom Gymnasium environments to optimize for risk-adjusted returns (Sharpe Ratio).

An autonomous trading agent powered by Deep Reinforcement Learning (PPO). Trained on 8 years of market data using a custom Gymnasium environment with advanced technical indicators (RSI, MACD, SMA) for optimized risk-adjusted returns.

Custom Gymnasium RL environment for portfolio management across 5 synthetic assets with realistic trading costs and volatility. PPO agent trained with Stable-Baselines3 achieved 23% return improvement over baselines while maintaining 90% diversification consistency.

Deep Q-Network trading bot that downloads Yahoo Finance data for the top 100 S&P 500 stocks, trains a multi-asset Gymnasium environment with stable-baselines3, logs to TensorBoard, and saves an evaluation-ready model.

An autonomous AI trading agent using Deep Reinforcement Learning (PPO). Unlike rule-based bots, it learns profitable strategies via trial-and-error using technical indicators (RSI, SMA). Built with Stable-Baselines3, Gymnasium, and yfinance. Includes full training and backtesting pipelines.

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Trading environments for Gymnasium

Gymnasium Style Environemts for Trading

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A Python project that simulates stock trading using reinforcement learning (PPO) with Gymnasium and Stable Baselines3.

Train a simple reinforcement learning agent in stock trading simulation.

Financial markets are constantly changing and many companies now leverage AI in their attempts to beat the market. Have a try at it yourself as you train a simple reinforcement learning agent in Python using Gymnasium. You will then explore the decisions that the agent made and analyze its performance.

Distributed Reinforcement Learning trading bot using gymnasium and stable-baseline3

Deep Reinforcement Learning portfolio optimization with Gym Trading Env Gymnasium environment

Reinforcement learning based trading agent for HDFC Bank using PPO and a custom Gymnasium environment.

Applying reinforcement learning techniques to Bitcoin trading. I use Gymnasium library to test my program

Reinforcement Learning trading agent built with PPO (Stable-Baselines3) in a custom Gymnasium environment to optimize trading decisions using market data.

Exploring RL methods to solve a gymnasium trading environment. Final Project for a graduate course in Reinforcement Learning.

A realistic Gymnasium environment for Crypto Trading using Deep Reinforcement Learning (DRL) with stochastic slippage and market impact models.

Reinforcement learning environment for optimal trade execution — Gymnasium + Stable-Baselines3 + Almgren-Chriss market impact model

Dockerized reinforcement learning framework for training PPO trading agents on historical 1-minute BTC candles. Defines a deterministic Gymnasium trading environment, reward shaping, offline evaluation, and exports versioned models for use in Sentinel Core.