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Co-authored-by: you-n-g <you-n-g@users.noreply.github.com>

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Co-authored-by: Young <afe.young@gmail.com>
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Lewen Wang
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docs/introduction/introduction.rst
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@@ -15,38 +15,56 @@ With ``Qlib``, users can easily try their ideas to create better Quant investmen
Framework
=========
.. image:: ../_static/img/framework.svg
: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.
This framework may be intimidating for new users to Qlib. It tries to accurately include a lot of details of Qlib's design.
For users new to Qlib, you can skip it first and read it later.
======================== ==============================================================================
Name Description
======================== ==============================================================================
`Infrastructure` layer `Infrastructure` layer provides underlying support for Quant research.
`DataServer` provides high-performance infrastructure for users to manage
and retrieve raw data. `Trainer` provides flexible interface to control
the training process of models which enable algorithms controlling the
training process.
`Workflow` layer `Workflow` layer covers the whole workflow of quantitative investment.
`Information Extractor` extracts data for models. `Forecast Model` focuses
on producing all kinds of forecast signals (e.g. *alpha*, risk) for other
modules. With these signals `Decision Generator` will generate the target
trading decisions(i.e. portfolio, orders) to be executed by `Execution Env`
(i.e. the trading market). There may be multiple levels of `Trading Agent`
and `Execution Env` (e.g. an *order executor trading agent and intraday
order execution environment* could behave like an interday trading
environment and nested in *daily portfolio management trading agent and
interday trading environment* )
=========================== ==============================================================================
Name Description
=========================== ==============================================================================
`Infrastructure` layer `Infrastructure` layer provides underlying support for Quant research.
`DataServer` provides high-performance infrastructure for users to manage
and retrieve raw data. `Trainer` provides flexible interface to control
the training process of models which enable algorithms controlling the
training process.
`Interface` layer `Interface` layer tries to present a user-friendly interface for the underlying
system. `Analyser` module will provide users detailed analysis reports of
forecasting signals, portfolios and execution results
======================== ==============================================================================
`Learning Framework` layer The `Forecast Model` and `Trading Agent` are learnable. They are learned
based on the `Learning Framework` layer and then applied to multiple scenarios
in `Workflow` layer. The supported learning paradigms can be categorized into
reinforcement learning and supervised learning. The learning framework
leverages the `Workflow` layer as well(e.g. sharing `Information Extractor`,
creating environments based on `Execution Env`).
`Workflow` layer `Workflow` layer covers the whole workflow of quantitative investment.
Both supervised-learning-based strategies and RL-based Strategies
are supported.
`Information Extractor` extracts data for models. `Forecast Model` focuses
on producing all kinds of forecast signals (e.g. *alpha*, risk) for other
modules. With these signals `Decision Generator` will generate the target
trading decisions(i.e. portfolio, orders)
If RL-based Strategies are adopted, the `Policy` is learned in a end-to-end way,
the trading deicsions are generated directly.
Decisions will be executed by `Execution Env`
(i.e. the trading market). There may be multiple levels of `Strategy`
and `Executor` (e.g. an *order executor trading strategy and intraday order executor*
could behave like an interday trading loop and be nested in
*daily portfolio management trading strategy and interday trading executor*
trading loop)
`Interface` layer `Interface` layer tries to present a user-friendly interface for the underlying
system. `Analyser` module will provide users detailed analysis reports of
forecasting signals, portfolios and execution results
=========================== ==============================================================================
- 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.
(p.s. framework image is created with https://draw.io/)