liuyu/qlib
1
0
Fork 0
mirror of https://github.com/microsoft/qlib.git synced 2026-07-10 22:36:55 +08:00
Code Activity

Improve the style of documentation (#1132)

Browse Source 
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>
This commit is contained in:
YaOzI
2022-07-07 19:42:27 +08:00
committed by GitHub
parent e62684eddf
commit 1dededa33f
29 changed files with 400 additions and 411 deletions
Download Patch File Download Diff File Expand all files Collapse all files

12
docs/introduction/introduction.rst
View File

@@ -3,7 +3,7 @@
===============================
Introduction
===================
============
.. image:: ../_static/img/logo/white_bg_rec+word.png
:align: center
@@ -13,8 +13,8 @@ Introduction
With ``Qlib``, users can easily try their ideas to create better Quant investment strategies.
Framework
===================
=========
.. image:: ../_static/img/framework.svg
:align: center
@@ -27,7 +27,7 @@ At the module level, Qlib is a platform that consists of above components. The c
Name Description
======================== ==============================================================================
`Infrastructure` layer `Infrastructure` layer provides underlying support for Quant research.
`DataServer` provides high-performance infrastructure for users to manage
`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.
@@ -35,13 +35,13 @@ Name Description
`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
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* )
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