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/component/model.rst

@@ -6,14 +6,14 @@ Interday Model: Model Training & Prediction
 Introduction
 ===================
 
-``Interday Model`` is designed to make the prediction score about stocks. Users can use the ``Interday Model`` in an automatic workflow by ``Estimator``, please refer to `Estimator <estimator.html>`_.  
+``Interday Model`` is designed to make the `prediction score` about stocks. Users can use the ``Interday Model`` in an automatic workflow by ``Estimator``, please refer to `Estimator <estimator.html>`_.  
 
-Because the components in ``Qlib`` are designed in a loosely-coupled way, ``Interday Model`` can be used as a independent module also.
+Because the components in ``Qlib`` are designed in a loosely-coupled way, ``Interday Model`` can be used as an independent module also.
 
 Base Class & Interface
 ======================
 
-``Qlib`` provides a base class `qlib.contrib.model.base.Model <../reference/api.html#module-qlib.contrib.model.base>`_, which all models should inherit from.
+``Qlib`` provides a base class `qlib.contrib.model.base.Model <../reference/api.html#module-qlib.contrib.model.base>`_ from which all models should inherit.
 
 The base class provides the following interfaces:
 
@@ -48,7 +48,7 @@ The base class provides the following interfaces:
             
             .. note::
             
-                The number and names of the columns is determined by the data handler, please refer to `Data Handler <data.html#data-handler>`_ and `Estimator Data <estimator.html#about-data>`_.
+                The number and names of the columns are determined by the data handler, please refer to `Data Handler <data.html#data-handler>`_ and `Estimator Data <estimator.html#about-data>`_.
             
         - `y_train`, pd.DataFrame type, train label
             The following example explains the value of `y_train`:
@@ -73,7 +73,7 @@ The base class provides the following interfaces:
 
             .. note::
 
-                The number and names of the columns is determined by the ``Data Handler``, please refer to `Data Handler <data.html#data-handler>`_.
+                The number and names of the columns are determined by the ``Data Handler``, please refer to `Data Handler <data.html#data-handler>`_.
 
         - `x_valid`, pd.DataFrame type, validation feature
             The format of `x_valid` is same as `x_train`
@@ -86,7 +86,7 @@ The base class provides the following interfaces:
             `w_train` is a pandas DataFrame, whose shape and index is same as `x_train`. The float value in `w_train` represents the weight of the feature at the same position in `x_train`.
 
         - `w_train`(Optional args, default is None), pd.DataFrame type, validation weight
-            `w_train` is a pandas DataFrame, whose shape and index is same as `x_valid`. The float value in `w_train` represents the weight of the feature at the same position in `x_train`.
+            `w_train` is a pandas DataFrame, whose shape and index is the same as `x_valid`. The float value in `w_train` represents the weight of the feature at the same position in `x_train`.
 
 - `predict(self, x_test, **kwargs)`
     - Predict test data 'x_test'
@@ -115,10 +115,10 @@ For other interfaces such as `save`, `load`, `finetune`, please refer to `Model
 Example
 ==================
 
-``Qlib`` provides ``LightGBM`` and ``DNN`` models as the baseline, the following steps shows how to run`` LightGBM`` as an independent module.
+``Qlib`` provides ``LightGBM`` and ``DNN`` models as the baseline, the following steps show how to run`` LightGBM`` as an independent module.
 
-- Initialize ``Qlib`` with `qlib.init` first, please refer to `initialization <initialization.rst>`_.
-- Run the following code to get the prediction score `pred_score`
+- Initialize ``Qlib`` with `qlib.init` first, please refer to `initialization <../start/initialization.html>`_.
+- Run the following code to get the `prediction score` `pred_score`
     .. code-block:: Python
 
         from qlib.contrib.estimator.handler import QLibDataHandlerClose