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/hidden/online.rst

@@ -77,20 +77,18 @@ If Your account was saved in "./user_data/", you can see the performance of your
 
     ...
     Result of porfolio:
-    sub_bench:
-                risk
-    mean    0.001157
-    std     0.003039
-    annual  0.289131
-    sharpe  6.017635
-    mdd    -0.013185
-    sub_cost:
-                risk
-    mean    0.000800
-    std     0.003043
-    annual  0.199944
-    sharpe  4.155963
-    mdd    -0.015517
+                                                      risk
+    excess_return_without_cost mean               0.000605
+                               std                0.005481
+                               annualized_return  0.152373
+                               information_ratio  1.751319
+                               max_drawdown      -0.059055
+    excess_return_with_cost    mean               0.000410
+                               std                0.005478
+                               annualized_return  0.103265
+                               information_ratio  1.187411
+                               max_drawdown      -0.075024
+
 
 Here 'SH000905' represents csi500 and 'SH000300' represents csi300
 

docs/hidden/tuner.rst

@@ -185,10 +185,10 @@ This part needs contain these fields:
         optim_type: max
 
 - `report_type`
-    The type of the report, str type, determines which kind of report you want to use. If you want to use the backtest result type, you can choose `pred_long`, `pred_long_short`, `pred_short`, `sub_bench` and `sub_cost`. If you want to use the model result type, you can only choose `model`.
+    The type of the report, str type, determines which kind of report you want to use. If you want to use the backtest result type, you can choose `pred_long`, `pred_long_short`, `pred_short`, `excess_return_without_cost` and `excess_return_with_cost`. If you want to use the model result type, you can only choose `model`.
 
 - `report_factor`
-    The factor you want to use in the report, str type, determines which factor you want to optimize. If your `report_type` is backtest result type, you can choose `annual`, `sharpe`, `mdd`, `mean` and `std`. If your `report_type` is model result type, you can choose `model_score` and `model_pearsonr`.
+    The factor you want to use in the report, str type, determines which factor you want to optimize. If your `report_type` is backtest result type, you can choose `annualized_return`, `information_ratio`, `max_drawdown`, `mean` and `std`. If your `report_type` is model result type, you can choose `model_score` and `model_pearsonr`.
 
 - `optim_type`
     The optimization type, str type, determines what kind of optimization you want to do. you can minimize the factor or maximize the factor, so you can choose `max`, `min` or `correlation` at this field.