Qlib is an AI-oriented quantitative investment platform, which aims to realize the potential, empower the research, and create the value of AI technologies in quantitative investment.

It contains the full ML pipeline of data processing, model training, back-testing; and covers the entire chain of quantitative investment: alpha seeking, risk modeling, portfolio optimization, and order execution.

With Qlib, user can easily try ideas to create better Quant investment strategies.

For more details, please refer to the site https://github.com/microsoft/qlib and our paper "Qlib: An AI-oriented Quantitative Investment Platform".

Multi-Agent Resource Optimization (MARO) platform is an instance of Reinforcement learning as a Service (RaaS) for real-world resource optimization. It can be applied to many important industrial domains, such as container inventory management in logistics, bike repositioning in transportation, virtual machine provisioning in data centers, and asset management in finance. Besides Reinforcement Learning (RL), it also supports other planning/decision mechanisms, such as Operations Research.

More details can be found at the site https://github.com/microsoft/maro.

FOST (Forecasting open-source tool) aims to provide an easy-use tool for spatial-temporal forecasting. The users only need to organize their data into a certain format and then get the prediction results with one command. FOST automatically handles the missing and abnormal values and captures both spatial and temporal correlations efficiently.

More details can be found at the site https://github.com/microsoft/FOST.