=============================== ``Qlib``: Quantitative Platform =============================== Introduction =================== .. image:: ../_static/img/logo/white_bg_rec+word.png :align: center ``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. With ``Qlib``, users can easily try their ideas to create better Quant investment strategies. Framework =================== .. image:: ../_static/img/framework.png :align: center At the module level, Qlib is a platform that consists of above components. The components are designed as loose-coupled modules and each component could be used stand-alone. ====================== ============================================================================== Name Description ====================== ============================================================================== `Data layer` `DataServer` focuses on providing high-performance infrastructure for users to manage and retrieve raw data. `DataEnhancement` will preprocess the data and provide the best dataset to be fed into the models. `Interday Model` `Interday model` focuses on producing prediction scores (aka. `alpha`). Models are trained by `Model Creator` and managed by `Model Manager`. Users could choose one or multiple models for prediction. Multiple models could be combined with `Ensemble` module. `Interday Strategy` `Portfolio Generator` will take prediction scores as input and output the orders based on the current position to achieve the target portfolio. `Intraday Trading` `Order Executor` is responsible for executing orders output by `Interday Strategy` and returning the executed results. `Analysis` Users could get a detailed analysis report of forecasting signals and portfolios in this part. ====================== ============================================================================== - The modules with hand-drawn style are under development and will be released in the future. - The modules with dashed borders are highly user-customizable and extendible.