Shawn's starred repositories
UnbiasedGBM
repository for Unbiased Gradient Boosting Decision Tree with Unbiased Feature Importance
ultralytics
NEW - YOLOv8 🚀 in PyTorch > ONNX > OpenVINO > CoreML > TFLite
pytorch-lightning
Pretrain, finetune and deploy AI models on multiple GPUs, TPUs with zero code changes.
transformer
Transformer: PyTorch Implementation of "Attention Is All You Need"
soft-actor-critic.pytorch
PyTorch implementation of Soft Actor-Critic(SAC).
Deep-Reinforcement-Learning-Algorithms-with-PyTorch
PyTorch implementations of deep reinforcement learning algorithms and environments
qlib
Qlib is an AI-oriented quantitative investment platform that aims to realize the potential, empower research, and create value using AI technologies in quantitative investment, from exploring ideas to implementing productions. Qlib supports diverse machine learning modeling paradigms. including supervised learning, market dynamics modeling, and RL.
stable-baselines3
PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
invalid-action-masking
Source Code for A Closer Look at Invalid Action Masking in Policy Gradient Algorithms
machine-learning-for-trading
Code for Machine Learning for Algorithmic Trading, 2nd edition.
mbt_gym
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.
ppo-implementation-details
The source code for the blog post The 37 Implementation Details of Proximal Policy Optimization
stable-baselines3-contrib
Contrib package for Stable-Baselines3 - Experimental reinforcement learning (RL) code
reinforcement_learning_oe
The work aims to explore Value based, Deep Reinforcment Learning (Deep Q-Learning and Double Deep Q-Learning) for the problem of Optimal Trade Execution. The problem of Optimal Trade Execution aims to find the the optimal "path" of executing a stock order, or in other words the number of shares to be executed at different steps given a time constraint, such that the price impact from the market is minimised and consequently revenue from executing a stock order maximised.
China-software-copyright
Chinese software copyright application template document