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53 lines
2.9 KiB
ReStructuredText
53 lines
2.9 KiB
ReStructuredText
===============================
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``Qlib``: Quantitative Platform
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===============================
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Introduction
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===================
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.. image:: ../_static/img/logo/white_bg_rec+word.png
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:align: center
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``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.
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With ``Qlib``, users can easily try their ideas to create better Quant investment strategies.
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Framework
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===================
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.. image:: ../_static/img/framework.svg
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:align: center
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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.
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======================== ==============================================================================
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Name Description
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======================== ==============================================================================
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`Infrastructure` layer `Infrastructure` layer provides underlying support for Quant research.
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`DataServer` provides high-performance infrastructure for users to manage
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and retrieve raw data. `Trainer` provides flexible interface to control
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the training process of models which enable algorithms controlling the
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training process.
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`Workflow` layer `Workflow` layer covers the whole workflow of quantitative investment.
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`Information Extractor` extracts data for models. `Forecast Model` focuses
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on producing all kinds of forecast signals (e.g. *alpha*, risk) for other
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modules. With these signals `Decision Generator` will generate the target
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trading decisions(i.e. portfolio, orders) to be executed by `Execution Env`
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(i.e. the trading market). There may be multiple levels of `Trading Agent`
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and `Execution Env` (e.g. an *order executor trading agent and intraday
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order execution environment* could behave like an interday trading
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environment and nested in *daily portfolio management trading agent and
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interday trading environment* )
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`Interface` layer `Interface` layer tries to present a user-friendly interface for the underlying
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system. `Analyser` module will provide users detailed analysis reports of
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forecasting signals, portfolios and execution results
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======================== ==============================================================================
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- The modules with hand-drawn style are under development and will be released in the future.
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- The modules with dashed borders are highly user-customizable and extendible.
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