Deep Reinforcement Learning toolkit: record and replay cryptocurrency limit order book data & train a DDQN agent

Providing the solutions for high-frequency trading (HFT) strategies using data science approaches (Machine Learning) on Full Orderbook Tick Data.

Machine Learning in Finance: From Theory to Practice Book

Investment Research for Everyone, Everywhere.

Connect to Google Colab using SSH

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…

ABIDES: Agent-Based Interactive Discrete Event Simulation

DQN Zoo is a collection of reference implementations of reinforcement learning agents developed at DeepMind based on the Deep Q-Network (DQN) agent.

Deep Reinforcement Learning codes for study. Currently, there are only codes for algorithms: DQN, C51, QR-DQN, IQN, QUOTA.

TradeMaster is an open-source platform for quantitative trading empowered by reinforcement learning 🔥 ⚡ 🌈

OpenDILab Decision AI Engine. The Most Comprehensive Reinforcement Learning Framework B.P.

Physical Symbolic Optimization

A general-purpose, flexible, and easy-to-use simulator alongside an OpenAI Gym trading environment for MetaTrader 5 trading platform (Approved by OpenAI Gym)

The most simple, flexible, and comprehensive OpenAI Gym trading environment (Approved by OpenAI Gym)

Multi-Agent Reinforcement Learning with JAX

A step-by-step tutorial of QuantumFlow, using the MNIST sub-dataset {3,6} and the 16-2-2 neural network as an example.