PortfolioLab is a python library that enables traders to take advantage of the latest portfolio optimisation algorithms used by professionals in the industry.

MlFinLab helps portfolio managers and traders who want to leverage the power of machine learning by providing reproducible, interpretable, and easy to use tools.

Udacity Deep Reinforcement Learning Nanodegree - Continuous Control Project

Repository of demo strategies for the Blueshift platform

Modularized Implementation of Deep RL Algorithms in PyTorch

Portfolio analytics for quants, written in Python

OpenAI Baselines: high-quality implementations of reinforcement learning algorithms

Python Implementation of Reinforcement Learning: An Introduction

Exercise notebooks for CVND.

AI learning to walk in gym's BipedalWalker environment.

random search, hill climbing, policy gradient

A fast Evolution Strategy implementation in Python

Code for the paper "Evolution Strategies as a Scalable Alternative to Reinforcement Learning"

Prioritized Experience Replay (PER) implementation in PyTorch

Supplemental material for Udacity's "Writing READMEs" course

The Unity Machine Learning Agents Toolkit (ML-Agents) is an open-source project that enables games and simulations to serve as environments for training intelligent agents using deep reinforcement …

Code for the Deep Learning with PyTorch lesson

Tensors and Dynamic neural networks in Python with strong GPU acceleration

Code for How To Create A Fully Automated AI Based Trading System With Python

Systematic Trading in python

Quantitative analysis, strategies and backtests

Machine Learning for Algorithmic Trading, Second Edition - published by Packt

Learning to trade under the reinforcement learning framework

Repo for the Deep Reinforcement Learning Nanodegree program

A toolkit for developing and comparing reinforcement learning algorithms.

Experimental solutions to selected exercises from the book [Advances in Financial Machine Learning by Marcos Lopez De Prado]