release-0.5.0 (#1)

* init commit

* change the version number

* rich the docs&fix cache docs

* update index readme

* Modify cache class name

* Modify sharpe to information_ratio

* Modify Group- to Group

* add the description of graphical results & fix the backtest docs

* fix docs in details

* update docs

* Update introduction.rst

* Update README.md

* Update introduction.rst

* Update introduction.rst

* Update introduction.rst

* Update installation.rst

* Update installation.rst

* Update initialization.rst

* Update getdata.rst

* Update integration.rst

* Update initialization.rst

* Update getdata.rst

* Update estimator.rst

Modify some typos.

* Update README.md

Modify the typos.

* Update initialization.rst

* Update data.rst

* Update report.rst

* Update estimator.rst

* Update cumulative_return.py

* Update model.rst

* Update rank_label.py

* Update cumulative_return.py

* Update strategy.rst

* Update getdata.rst

* Update backtest.rst

* Update integration.rst

* Update getdata.rst

* Update introduction.rst

* Update introduction.rst

* Update README.md

* Update report.rst

* Update integration.rst

Fix typos

* Update installation.rst

Fix typos

* Update getdata.rst

* Update initialization.rst

Fix typos.

* add quick start docs&fix detials

* fix estimator docs & fix strategy docs

* fix the cahce in data.rst

* update documents

* Fix Corr && Rsquare

* fix data retrival example to csi300 & fix a data bug

* fix filter bug

* Fix data collector

* Modift model args

* add the log & fix README.md\quick.rst

* add enviroment depend & add intoduction of qlib-server online mode

* fix image center fomat & set log_only of docs is True

* fix README.md format

* update data preparation & readme logo image

* get_data support version

* Modify analysis names

* Modify analysis graph

* update report.rst & data.rst

* commmit estimator for merge

* minimal requirements

* Update README.md

* Update README.md

* Update README.md

* Update README.md

* Update README.md

* Update README.md

* Update README.md

* Update READEME.md

* Update READEME.md

* update estimator

* Fix doc urls

* fix get_data.py docstring

* update test_get_data.py

* Upate docs

* Upate docs

* Upate docs

Co-authored-by: bxdd <bxddream@gmail.com>
Co-authored-by: zhupr <zhu.pengrong@foxmail.com>
Co-authored-by: Wendi Li <wendili.academic@qq.com>
Co-authored-by: Dingsu Wang <dingsu.wang@gmail.com>
Co-authored-by: bxdd <45119470+bxdd@users.noreply.github.com>
Co-authored-by: cslwqxx <cslwqxx@users.noreply.github.com>

docs/introduction/introduction.rst

@@ -1,45 +1,47 @@
 ===============================
-``Qlib``: Quantitative Library
+``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 apply their favorite model to create better Quant investment strategy.
-
+With ``Qlib``, users can easily try their ideas to create better Quant investment strategies.
 
 Framework
-==================
-
+===================
+   
 .. image:: ../_static/img/framework.png
-    :alt: Framework
+    :align: center
 
 
-At module level, ``Qlib`` is a platform that consists of the above components. Each components is loose-coupling and can be used stand-alone.
+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
-======================  ========================================================================
-`Data layer`            `DataServer` focus on providing high performance infrastructure for user
-                        to retrieve and get raw data. `DataEnhancement` will preprocess the data
-                        and provide the best dataset to be fed in to the models.
+======================  ==============================================================================
+`Data layer`            `DataServer` focuses on providing high-performance infrastructure for users to
+                        manage and retrieve raw data. `DataEnhancement` will preprocess the data and 
+                        provide the best dataset to be fed into the models.
 
-`Interday Model`        `Interday model` focus on producing forecasting signals(aka. `alpha`). 
-                        Models are trained by `Model Creator` and managed by `Model Manager`.
-                        User could choose one or multiple models for forecasting. Multiple models
-                        could be combined with `Ensemble` module.
+`Interday Model`        `Interday model` focuses on producing prediction scores (aka. `alpha`). Models
+                        are trained by `Model Creator` and managed by `Model Manager`. Users could 
+                        choose one or multiple models for prediction. Multiple models could be combined
+                        with `Ensemble` module.
 
-`Interday Strategy`     `Portfolio Generator` will take forecasting signals as input and output 
-                        the orders based on current position to achieve target portfolio.
+`Interday Strategy`     `Portfolio Generator` will take prediction scores as input and output the 
+                        orders based on the current position to achieve the target portfolio.
 
 `Intraday Trading`      `Order Executor` is responsible for executing orders output by 
                         `Interday Strategy` and returning the executed results.
 
-`Analysis`              User could get detailed analysis report of forecasting signal and portfolio
+`Analysis`              Users could get a detailed analysis report of forecasting signals and portfolios
                         in this part.
-======================  ========================================================================
+======================  ==============================================================================
 
-- The modules with hand-drawn style is under development and will be released in the future.
-- The modules with dashed border is highly user-customizable and extendible.
+- 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.