mirror of
https://github.com/microsoft/qlib.git
synced 2026-06-06 05:51:17 +08:00
1dededa33f
This commit improves the documentation (rst files) only in the following three ways: * Aligned section headers with their underline/overline punctuation characters * Deleted all trailling whitespaces in rst files * Deleted a few trailling newlines at the end of the rst files Co-authored-by: Bingyao Liu <Bingyao.Liu@sofund.com>
53 lines
2.9 KiB
ReStructuredText
53 lines
2.9 KiB
ReStructuredText
===============================
|
|
``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.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.
|
|
|
|
|
|
|
|
======================== ==============================================================================
|
|
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* )
|
|
|
|
`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.
|