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/strategy.rst

@@ -9,9 +9,9 @@ Introduction
 
 ``Interday Strategy`` is designed to adopt different trading strategies, which means that users can adopt different algorithms to generate investment portfolios based on the prediction scores of the ``Interday Model``. Users can use the ``Interday Strategy`` in an automatic workflow by ``Estimator``, please refer to `Estimator <estimator.html>`_.  
 
-Because the componets in ``Qlib`` are designed in a loosely-coupled way, ``Interday Strategy`` can be used as a independent module also.
+Because the components in ``Qlib`` are designed in a loosely-coupled way, ``Interday Strategy`` can be used as an independent module also.
 
-``Qlib`` provides several implemented trading strategy. Also, ``Qlib`` supports costom strategy, users can customize strategies according to their own needs.
+``Qlib`` provides several implemented trading strategies. Also, ``Qlib`` supports custom strategy, users can customize strategies according to their own needs.
 
 Base Class & Interface
 ======================
@@ -27,7 +27,7 @@ Qlib provides a base class ``qlib.contrib.strategy.BaseStrategy``. All strategy
 - `generate_order_list`
     Rerturn the order list. 
 
-User can inherit `BaseStrategy` to costomize their strategy class.
+Users can inherit `BaseStrategy` to customize their strategy class.
 
 WeightStrategyBase
 --------------------
@@ -49,19 +49,18 @@ Qlib alse provides a class ``qlib.contrib.strategy.WeightStrategyBase`` that is
 - Generate the target amount of stocks from the target position.
 - Generate the order list from the target amount
 
-Users can inherit `WeightStrategyBase` and implement the inteface `generate_target_weight_position` to costomize their strategy class, which only focuses on the target positions.
+Users can inherit `WeightStrategyBase` and implement the interface `generate_target_weight_position` to customize their strategy class, which only focuses on the target positions.
 
 Implemented Strategy
 ====================
 
-Qlib provides several implemented strategy classes `TopkDropoutStrategy`.
-
+Qlib provides a implemented strategy classes named `TopkDropoutStrategy`.
 
 TopkDropoutStrategy
 ------------------
 `TopkDropoutStrategy` is a subclass of `BaseStrategy` and implement the interface `generate_order_list` whose process is as follows.
 
-- Adopt the the ``Topk-Drop`` algorithm to calculate the target amount of each stock
+- Adopt the ``Topk-Drop`` algorithm to calculate the target amount of each stock
 
     .. note::
         ``Topk-Drop`` algorithm：
@@ -70,7 +69,7 @@ TopkDropoutStrategy
         - `Drop`: The number of stocks sold on each trading day
         
         Currently, the number of held stocks is `Topk`.
-        On each trading day, the `Drop` number of held stocks with worst prediction score will be sold, and the same number of unheld stocks with best prediction score will be bought.
+        On each trading day, the `Drop` number of held stocks with the worst `prediction score` will be sold, and the same number of unheld stocks with the best `prediction score` will be bought.
         
         .. image:: ../_static/img/topk_drop.png
             :alt: Topk-Drop
@@ -103,17 +102,17 @@ Usage & Example
     # custom Strategy, refer to: TODO: Strategy API url
     strategy = TopkDropoutStrategy(**STRATEGY_CONFIG)
 
-    # pred_score is the prediction score output by Model
+    # pred_score is the `prediction score` output by Model
     report_normal, positions_normal = backtest(
         pred_score, strategy=strategy, **BACKTEST_CONFIG
     )
 
 Also, the above example has been given in ``examples\train_backtest_analyze.ipynb``.
 
-To know more about the prediction score `pred_score` output by ``Interday Model``, please refer to `Interday Model: Model Training & Prediction <model.html>`_.
+To know more about the `prediction score` `pred_score` output by ``Interday Model``, please refer to `Interday Model: Model Training & Prediction <model.html>`_.
 
 To know more about ``Intraday Trading``, please refer to `Intraday Trading: Model&Strategy Testing <backtest.html>`_.
 
 Reference
 ===================
-TO konw more about ``Interday Strategy``, please refer to `Strategy API <../reference/api.html>`_.
+To know more about ``Interday Strategy``, please refer to `Strategy API <../reference/api.html>`_.