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Improve the style of documentation (#1132)

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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
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parent e62684eddf
commit 1dededa33f
29 changed files with 400 additions and 411 deletions
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12
docs/introduction/introduction.rst
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@@ -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

24
docs/introduction/quick.rst
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@@ -1,10 +1,10 @@
===============================
===========
Quick Start
===============================
===========
Introduction
==============
============
This ``Quick Start`` guide tries to demonstrate
@@ -14,7 +14,7 @@ This ``Quick Start`` guide tries to demonstrate
Installation
==================
============
Users can easily intsall ``Qlib`` according to the following steps:
@@ -34,7 +34,7 @@ Users can easily intsall ``Qlib`` according to the following steps:
To known more about `installation`, please refer to `Qlib Installation <../start/installation.html>`_.
Prepare Data
==============
============
Load and prepare data by running the following code:
@@ -47,14 +47,14 @@ This dataset is created by public data collected by crawler scripts in ``scripts
To known more about `prepare data`, please refer to `Data Preparation <../component/data.html#data-preparation>`_.
Auto Quant Research Workflow
====================================
============================
``Qlib`` provides a tool named ``qrun`` to run the whole workflow automatically (including building dataset, training models, backtest and evaluation). Users can start an auto quant research workflow and have a graphical reports analysis according to the following steps:
``Qlib`` provides a tool named ``qrun`` to run the whole workflow automatically (including building dataset, training models, backtest and evaluation). Users can start an auto quant research workflow and have a graphical reports analysis according to the following steps:
- Quant Research Workflow:
- Quant Research Workflow:
- Run ``qrun`` with a config file of the LightGBM model `workflow_config_lightgbm.yaml` as following.
.. code-block::
.. code-block::
cd examples # Avoid running program under the directory contains `qlib`
qrun benchmarks/LightGBM/workflow_config_lightgbm.yaml
@@ -64,7 +64,7 @@ Auto Quant Research Workflow
The result of ``qrun`` is as follows, which is also the typical result of ``Forecast model(alpha)``. Please refer to `Intraday Trading <../component/backtest.html>`_. for more details about the result.
.. code-block:: python
risk
excess_return_without_cost mean 0.000605
std 0.005481
@@ -77,7 +77,7 @@ Auto Quant Research Workflow
information_ratio 1.187411
max_drawdown -0.075024
To know more about `workflow` and `qrun`, please refer to `Workflow: Workflow Management <../component/workflow.html>`_.
- Graphical Reports Analysis:
@@ -89,6 +89,6 @@ Auto Quant Research Workflow
Custom Model Integration
===============================================
========================
``Qlib`` provides a batch of models (such as ``lightGBM`` and ``MLP`` models) as examples of ``Forecast Model``. In addition to the default model, users can integrate their own custom models into ``Qlib``. If users are interested in the custom model, please refer to `Custom Model Integration <../start/integration.html>`_.