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71 lines
4.1 KiB
ReStructuredText
71 lines
4.1 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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This framework may be intimidating for new users to Qlib. It tries to accurately include a lot of details of Qlib's design.
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For users new to Qlib, you can skip it first and read it later.
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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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`Learning Framework` layer The `Forecast Model` and `Trading Agent` are learnable. They are learned
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based on the `Learning Framework` layer and then applied to multiple scenarios
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in `Workflow` layer. The supported learning paradigms can be categorized into
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reinforcement learning and supervised learning. The learning framework
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leverages the `Workflow` layer as well(e.g. sharing `Information Extractor`,
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creating environments based on `Execution Env`).
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`Workflow` layer `Workflow` layer covers the whole workflow of quantitative investment.
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Both supervised-learning-based strategies and RL-based Strategies
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are supported.
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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)
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If RL-based Strategies are adopted, the `Policy` is learned in a end-to-end way,
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the trading decisions are generated directly.
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Decisions will be executed by `Execution Env`
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(i.e. the trading market). There may be multiple levels of `Strategy`
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and `Executor` (e.g. an *order executor trading strategy and intraday order executor*
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could behave like an interday trading loop and be nested in
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*daily portfolio management trading strategy and interday trading executor*
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trading loop)
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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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(p.s. framework image is created with https://draw.io/)
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