A library of reinforcement learning components and agents
TF-Agents: A reliable, scalable and easy to use TensorFlow library for Contextual Bandits and Reinforcement Learning.
(Eng. Incl.) 股票AI操盘手：包含股票知识、策略实例、机器学习、深度学习、C++部署和聚宽实例代码等，可以方便学习、模拟及实盘交易
AKShare is an elegant and simple financial data interface library for Python, built for human beings! 开源财经数据接口库
Declarative statistical visualization library for Python
📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.
Official TensorFlow implementation of the paper "Automating Reinforcement Learning with Example-based Resets"
A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
A collection of practical resources by the AI4Finance Foundation
Scalable and Elastic Deep Reinforcement Learning Using PyTorch. Please star. 🔥
FinRL: Financial Reinforcement Learning Framework. Please star. 🔥
FinRL-Meta: A Universe for Data-Driven Financial Reinforcement Learning. 🔥
Hyperparameter tuning for humans
Notebooks, resources and references accompanying the book Machine Learning for Algorithmic Trading
Financial Markets Data Visualization using Matplotlib
Multi-Joint dynamics with Contact. A general purpose physics simulator.
Implementations of a large collection of reinforcement learning algorithms.
Quantitative analysis, strategies and backtests
Backtest and live trading in Python
An open source framework that provides a simple, universal API for building distributed applications. Ray is packaged with RLlib, a scalable reinforcement learning library, and Tune, a scalable hyperparameter tuning library.
Source codes for the book "Reinforcement Learning: Theory and Python Implementation"
WATERMELON: Multi-Agent Reinforcement Learning Based Algorithmic Stock Trading System with GUI Application
Packaged deep reinforcement learning algorithms in tensorflow 2.x
股票行情数据 东方财富交易接口 股票自动交易
In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.
Algorithmic trading and quantitative trading open source platform to develop trading robots (stock markets, forex, crypto, bitcoins, and options).
Tensorflow 2 Reinforcement Learning Cookbook, published by Packt
TensorLayerX: A Unified Deep Learning and Reinforcement Learning Framework for All Hardwares, Backends and OS.
An elegant PyTorch deep reinforcement learning library.