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A deep reinforcement learning framework for generating formulaic alpha factors for quantitative investment, powered by GFlowNet, implemented in Python&PyTorch.
TradeMaster is an open-source platform for quantitative trading empowered by reinforcement learning 🔥 ⚡ 🌈
Code for the ICLR 2024 spotlight paper: "Learning to Act without Actions" (introducing Latent Action Policies)
A curated list of practical financial machine learning tools and applications.
🪄 Create rich visualizations with AI
GPT4 based personalized ArXiv paper assistant bot
A curated list of resources about generative flow networks (GFlowNets).
GFlowNet library specialized for graph & molecular data
The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery 🧑🔬
Curated list of project-based tutorials
MlFinLab helps portfolio managers and traders who want to leverage the power of machine learning by providing reproducible, interpretable, and easy to use tools.
Meta labeling is a method of determining the size of the bet.
⏳ ChatLog: Recording and Analysing ChatGPT Across Time
[ICML 2024 Best Paper] Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution (https://arxiv.org/abs/2310.16834)
Rainbow: Combining Improvements in Deep Reinforcement Learning
This is the official implementation of Multi-Agent PPO (MAPPO).
quantitative trading with Javascript, Python, C++, PineScript, Blockly, MyLanguage(麦语言)
Generating sets of formulaic alpha (predictive) stock factors via reinforcement learning.
The Cradle framework is a first attempt at General Computer Control (GCC). Cradle supports agents to ace any computer task by enabling strong reasoning abilities, self-improvment, and skill curatio…
PyTorch implementation of FQF, IQN and QR-DQN.
A Collection of Variational Autoencoders (VAE) in PyTorch.
