Qlib
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. We aim to establish QLib as a viable quant research framework in capital markets, and a platform on which we can introduce further research in AI models for capital markets. In doing so, we will support quant use cases in ESG sustainability for capital markets.
MARO
MARO (opens in new tab) (Multi-Agent Resource Optimization) platform is an instance of Reinforcement Learning as a Service (RaaS) for real-world resource optimization problems.
FOST
FOST (opens in new tab) (Forecasting Open Source Tool) is a general forecasting tool, which demonstrates our experience and advanced technology in practical forecasting domains, including temporal, spatial-temporal and hierarchical 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.
BatteryML
BatteryML (opens in new tab) is an open-source tool for machine learning on battery degradation. The performance degradation of lithium batteries is a complex electrochemical process, involving factors such as the growth of solid electrolyte interface, lithium precipitation, loss of active materials, etc. Therefore, effectively analyzing and predicting the performance degradation of lithium batteries to provide guidance for early prevention and intervention has become a crucial research topic. We hope BatteryML can empower both battery researchers and data scientists to gain deeper insights from battery degradation data and build more powerful models for accurate predictions and early interventions.