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>
microsoft · Sep 24, 2020 · de9e13b · de9e13b
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diff --git a/README.md b/README.md
diff --git a/docs/_static/img/analysis/analysis_model_IC.png b/docs/_static/img/analysis/analysis_model_IC.png
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diff --git a/docs/_static/img/analysis/analysis_model_long_short.png b/docs/_static/img/analysis/analysis_model_long_short.png
diff --git a/docs/_static/img/analysis/analysis_model_monthly_IC.png b/docs/_static/img/analysis/analysis_model_monthly_IC.png
diff --git a/docs/_static/img/analysis/analysis_model_top_bottom_turnover.png b/docs/_static/img/analysis/analysis_model_top_bottom_turnover.png
diff --git a/docs/_static/img/analysis/cumulative_return_buy.png b/docs/_static/img/analysis/cumulative_return_buy.png
diff --git a/docs/_static/img/analysis/cumulative_return_buy_minus_sell.png b/docs/_static/img/analysis/cumulative_return_buy_minus_sell.png
diff --git a/docs/_static/img/analysis/cumulative_return_hold.png b/docs/_static/img/analysis/cumulative_return_hold.png
diff --git a/docs/_static/img/analysis/cumulative_return_sell.png b/docs/_static/img/analysis/cumulative_return_sell.png
diff --git a/docs/_static/img/analysis/rank_label_buy.png b/docs/_static/img/analysis/rank_label_buy.png
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diff --git a/docs/_static/img/analysis/rank_label_sell.png b/docs/_static/img/analysis/rank_label_sell.png
diff --git a/docs/_static/img/analysis/report.png b/docs/_static/img/analysis/report.png
diff --git a/docs/_static/img/analysis/risk_analysis_annual.png b/docs/_static/img/analysis/risk_analysis_annual.png
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diff --git a/docs/_static/img/analysis/risk_analysis_max_drawdown.png b/docs/_static/img/analysis/risk_analysis_max_drawdown.png
diff --git a/docs/_static/img/analysis/risk_analysis_mdd.png b/docs/_static/img/analysis/risk_analysis_mdd.png
diff --git a/docs/_static/img/analysis/risk_analysis_sharpe.png b/docs/_static/img/analysis/risk_analysis_sharpe.png
diff --git a/docs/_static/img/analysis/risk_analysis_std.png b/docs/_static/img/analysis/risk_analysis_std.png
diff --git a/docs/_static/img/analysis/score_ic.png b/docs/_static/img/analysis/score_ic.png
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diff --git a/docs/_static/img/logo/yel_bg_rec+word.png b/docs/_static/img/logo/yel_bg_rec+word.png
diff --git a/docs/_static/img/logo/yellow_bg_rec+word .png b/docs/_static/img/logo/yellow_bg_rec+word .png
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diff --git a/docs/advanced/alpha.rst b/docs/advanced/alpha.rst
@@ -45,7 +45,7 @@ Example
 
 Users can use ``Data Handler`` to build formulaic alphas `MACD` in qlib:
 
-.. note:: Users need to initialize ``Qlib`` with `qlib.init` first.  Please refer to `initialization <initialization.rst>`_.
+.. note:: Users need to initialize ``Qlib`` with `qlib.init` first.  Please refer to `initialization <../start/initialization.html>`_.
 
 .. code-block:: python
 

diff --git a/docs/advanced/server.rst b/docs/advanced/server.rst
@@ -0,0 +1,28 @@
+.. _server:
+=================================
+``Online`` & ``Offline`` mode
+=================================
+.. currentmodule:: qlib
+
+
+Introduction
+=============
+
+``Qlib`` supports ``Online`` mode and ``Offline`` mode. Only the ``Offline`` mode is introduced in this document. 
+
+The ``Online`` mode is designed to solve the following problems:
+
+- Manage the data in a centralized way. Users don't have to manage data of different versions.
+- Reduce the amount of cache to be generated.
+- Make the data can be accessed in a remote way.
+
+Qlib-Server
+===============
+
+``Qlib-Server`` is the assorted server system for ``Qlib``, which utilizes ``Qlib`` for basic calculations and provides extensive server system and cache mechanism. With QLibServer, the data provided for ``Qlib`` can be managed in a centralized manner. With ``Qlib-Server``, users can use ``Qlib`` in ``Online`` mode. 
+
+
+
+Reference
+=================
+If users are interested in ``Qlib-Server`` and ``Online`` mode, please refer to `Qlib-Server Project <https://github.com/microsoft/qlib-server>`_ and `Qlib-Server Document <https://qlib-server.readthedocs.io/en/latest/>`_.
diff --git a/docs/component/backtest.rst b/docs/component/backtest.rst
@@ -7,7 +7,7 @@ Intraday Trading: Model&Strategy Testing
 Introduction
 ===================
 
-``Intraday Trading`` is designed to test models and strategies, which help users to check the performance of custom model/strategy.
+``Intraday Trading`` is designed to test models and strategies, which help users to check the performance of a custom model/strategy.
 
 
 .. note::
@@ -19,26 +19,26 @@ Introduction
 Example
 ===========================
 
-Users need to generate a prediction score(a pandas DataFrame) with MultiIndex<instrument, datetime> and a `score` column. And users need to assign a strategy used in backtest, if strategy is not assigned,
+Users need to generate a `prediction score`(a pandas DataFrame) with MultiIndex<instrument, datetime> and a `score` column. And users need to assign a strategy used in backtest, if strategy is not assigned,
 a `TopkDropoutStrategy` strategy with `(topk=50, n_drop=5, risk_degree=0.95, limit_threshold=0.0095)` will be used.
-If ``Strategy`` module is not user's interested part, `TopkDropoutStrategy` is enough. 
+If ``Strategy`` module is not users' interested part, `TopkDropoutStrategy` is enough. 
 
-The simple example with default strategy is as follows.
+The simple example of the default strategy is as follows.
 
 .. code-block:: python
 
     from qlib.contrib.evaluate import backtest
     # pred_score is the prediction score
     report, positions = backtest(pred_score, topk=50, n_drop=0.5, verbose=False, limit_threshold=0.0095)
 
-To know more about backtesting with specific strategy, please refer to `Strategy <strategy.html>`_.
+To know more about backtesting with a specific strategy, please refer to `Strategy <strategy.html>`_.
 
 To know more about the prediction score `pred_score` output by ``Model``, please refer to `Interday Model: Model Training & Prediction <model.html>`_.
 
 Prediction Score
 -----------------
 
-The prediction score is a pandas DataFrame. Its index is <instrument(str), datetime(pd.Timestamp)> and it must
+The `prediction score` is a pandas DataFrame. Its index is <instrument(str), datetime(pd.Timestamp)> and it must
 contains a `score` column.
 
 A prediction sample is shown as follows.
@@ -67,37 +67,44 @@ The backtest results are in the following form:
 
 .. code-block:: python
 
-    sub_bench mean    0.000662
-              std     0.004487
-              annual  0.166720
-              sharpe  2.340526
-              mdd    -0.080516
-    sub_cost  mean    0.000577
-              std     0.004482
-              annual  0.145392
-              sharpe  2.043494
-              mdd    -0.083584
+                                                      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
+
+
+
+- `excess_return_without_cost`
+    - `mean`
+        Mean value of the `CAR` (cumulative abnormal return) without cost
+    - `std`
+        The `Standard Deviation` of `CAR` (cumulative abnormal return) without cost.
+    - `annualized_return`
+        The `Annualized Rate` of `CAR` (cumulative abnormal return) without cost.
+    - `information_ratio`
+        The `Information Ratio` without cost. please refer to `Information Ratio – IR <https://www.investopedia.com/terms/i/informationratio.asp>`_.
+    - `max_drawdown`
+        The `Maximum Drawdown` of `CAR` (cumulative abnormal return) without cost, please refer to `Maximum Drawdown (MDD) <https://www.investopedia.com/terms/m/maximum-drawdown-mdd.asp>`_.
+
+- `excess_return_with_cost`
+    - `mean`
+        Mean value of the `CAR` (cumulative abnormal return) series with cost
+    - `std`
+        The `Standard Deviation` of `CAR` (cumulative abnormal return) series with cost.
+    - `annualized_return`
+        The `Annualized Rate` of `CAR` (cumulative abnormal return) with cost.
+    - `information_ratio`
+        The `Information Ratio` with cost. please refer to `Information Ratio – IR <https://www.investopedia.com/terms/i/informationratio.asp>`_.
+    - `max_drawdown`
+        The `Maximum Drawdown` of `CAR` (cumulative abnormal return) with cost, please refer to `Maximum Drawdown (MDD) <https://www.investopedia.com/terms/m/maximum-drawdown-mdd.asp>`_.
 
-- `sub_bench`
-    Returns of the portfolio without deduction of fees
-
-- `sub_cost`
-    Returns of the portfolio with deduction of fees
-
-- `mean`
-    Mean value of the returns sequence(difference sequence of assets).
-
-- `std`
-    Standard deviation of the returns sequence(difference sequence of assets).
-
-- `annual`
-    Average annualized returns of the portfolio.
-
--  `ir`
-    Information Ratio, please refer to `Information Ratio – IR <https://www.investopedia.com/terms/i/informationratio.asp>`_.
-
-- `mdd`
-    Maximum Drawdown, please refer to `Maximum Drawdown (MDD) <https://www.investopedia.com/terms/m/maximum-drawdown-mdd.asp>`_.
 
 
 Reference