An open source reinforcement learning framework for training, evaluating, and deploying robust trading agents.

Trading and Backtesting environment for training reinforcement learning agent or simple rule base algo.

This project uses reinforcement learning on stock market and agent tries to learn trading. The goal is to check if the agent can learn to read tape. The project is dedicated to hero in life great Jesse Livermore.

Deep Reinforcement Learning based Trading Agent for Bitcoin

Code for the paper "FinRL-DeepSeek: LLM-Infused Risk-Sensitive Reinforcement Learning for Trading Agents" arXiv:2502.07393

An environment to high-frequency trading agents under reinforcement learning

Every day, millions of traders around the world are trying to make money by trading stocks. These days, physical traders are also being replaced by automated trading robots. Algorithmic trading market has experienced significant growth rate and large number of firms are using it. I have tried to build a Deep Q-learning reinforcement agent model to do automated stock trading.

This repository provides the code for a Reinforcement Learning trading agent with its trading environment that works with both simulated and historical market data. This was inspired by OpenAI Gym framework.

mbt_gym is a module which provides a suite of gym environments for training reinforcement learning (RL) agents to solve model-based high-frequency trading problems such as market-making and optimal execution. The module is set up in an extensible way to allow the combination of different aspects of different models. It supports highly efficient implementations of vectorized environments to allow faster training of RL agents.

A custom MARL (multi-agent reinforcement learning) environment where multiple agents trade against one another (self-play) in a zero-sum continuous double auction. Ray [RLlib] is used for training.

A trading agent AI is an artificial intelligence system that uses computational intelligence methods such as machine learning and deep reinforcement learning to automatically discover, implement, and fine-tune strategies for autonomous adaptive automated trading in financial markets

This program trains an agent: StarTrader to trade like a human using a deep reinforcement learning algorithm: deep deterministic policy gradient (DDPG) learning algorithm.

This project uses Deep Reinforcement Learning (DRL) to develop and evaluate stock trading strategies. By implementing agents like PPO, A2C, DDPG, SAC, and TD3 in a realistic trading environment with transaction costs, it aims to optimize trading decisions based on return, volatility, and Sharpe ratio.

Training an Agent to make automated trading decisions in a simulated stochastic market environment using Reinforcement Learning or Deep Q-Learning

This is a repo for deep reinforcement learning in trading. I used value based double DQN variant for single stock trading. The agent learn to make decision between selling, holding and buying stock with fixed amount based on the reward returned from the environment.

A Crypto-trading reinforcement learning that explores the potential of using RL agents on a trading environment with multiple markets.

GPU-Accelerated Multi-Agent Reinforcement Learning for High-Frequency Trading

This Reinforcement learning agent uses Policy-Gradient method to trade the market

Stock trading strategies play a critical role in investment. However, it is challenging to design a profitable strategy in a complex and dynamic stock market. In this paper, we propose a deep ensemble reinforcement learning scheme that automatically learns a stock trading strategy by maximizing investment return. We train a deep reinforcement learning agent and obtain an ensemble trading strategy using the three actor-critic based algorithms: Proximal Policy Optimization (PPO), Advantage Actor Critic (A2C), and Deep Deterministic Policy Gradient (DDPG). The ensemble strategy inherits and integrates the best features of the three algorithms, thereby robustly adjusting to different market conditions. In order to avoid the large memory consumption in training networks with continuous action space, we employ a load-on-demand approach for processing very large data. We test our algorithms on the 30 Dow Jones stocks which have adequate liquidity. The performance of the trading agent with different reinforcement learning algorithms is evaluated and compared with both the Dow Jones Industrial Average index and the traditional min-variance portfolio allocation strategy. The proposed deep ensemble scheme is shown to outperform the three individual algorithms and the two baselines in terms of the risk-adjusted return measured by the Sharpe ratio.

This project is part of my internship at ULiege on Deep RL in stock market trading

Building an Agent to Trade with Reinforcement Learning

A trading bitcoin agent was created with deep reinforcement learning implementations.

A tutorial for training a reinforcement learning agent how to trade on a basic sine curve using TensorTrade and Ray.

This MLOps project productionizes a Deep Reinforcement Learning agent with a scalable, distributed data streaming infrastructure using Kafka and Ray. A thorough walkthrough of the code is described in this article on medium: https://ryanraymartin.medium.com/deep-reinforcement-learning-for-stock-trading-with-kafka-and-rllib-d738b9634675

A reinforcement learning agent trade eight different coins for several months using just an initial budget in cash.

Quantized Forex binary options trading strategies via reinforcement learning agents leveraging high-frequency tick data and custom backtesting infrastructure.

A trading agent that uses deep reinforcement learning to trade Ethereum.

Training and evaluation of Deep Reinforcement Learning cruptocurrency trading agent.

This is a Deep Reinforcement Learning project designed to trade ES Emini Footprint data in Sierra Charts

Baselines of reinforcement learning trading agents for China stock market