Merge remote-tracking branch 'qlib/main' into qlib_register_ops

docs/component/data.rst

@@ -126,17 +126,17 @@ After conversion, users can find their Qlib format data in the directory `~/.qli
     The arguments of `--include_fields` should correspond with the column names of CSV files. The columns names of dataset provided by ``Qlib`` should include open, close, high, low, volume and factor at least.
     
     - `open`
-        The opening price
+        The adjusted opening price
     - `close`
-        The closing price
+        The adjusted closing price
     - `high`
-        The highest price
+        The adjusted highest price
     - `low`
-        The lowest price
+        The adjusted lowest price
     - `volume`
-        The trading volume
+        The adjusted trading volume
     - `factor`
-        The Restoration factor
+        The Restoration factor. Normally, original_price = adj_price / factor 
 
     In the convention of `Qlib` data processing, `open, close, high, low, volume, money and factor` will be set to NaN if the stock is suspended. 
 
@@ -296,6 +296,7 @@ The ``Processor`` module in ``Qlib`` is designed to be learnable and it is respo
 - ``RobustZScoreNorm``: `processor` that applies robust z-score normalization.
 - ``CSZScoreNorm``: `processor` that applies cross sectional z-score normalization.
 - ``CSRankNorm``: `processor` that applies cross sectional rank normalization.
+- ``CSZFillna``: `processor` that fills N/A values in a cross sectional way by the mean of the column.
 
 Users can also create their own `processor` by inheriting the base class of ``Processor``. Please refer to the implementation of all the processors for more information (`Processor Link <https://github.com/microsoft/qlib/blob/main/qlib/data/dataset/processor.py>`_). 
 

docs/component/recorder.rst

@@ -34,8 +34,9 @@ Here is a general view of the structure of the system:
             - Recorder 2
             - ...
         - ...
-This experiment management system defines a set of interface and provided a concrete implementation based on the machine learning platform: ``MLFlow`` (`link <https://mlflow.org/>`_). 
+This experiment management system defines a set of interface and provided a concrete implementation ``MLflowExpManager``, which is based on the machine learning platform: ``MLFlow`` (`link <https://mlflow.org/>`_). 
 
+If users set the implementation of ``ExpManager`` to be ``MLflowExpManager``, they can use the command `mlflow ui` to visualize and check the experiment results. For more information, pleaes refer to the related documents `here <https://www.mlflow.org/docs/latest/cli.html#mlflow-ui>`_.
 
 Qlib Recorder
 ===================
@@ -91,7 +92,7 @@ Record Template
 
 The ``RecordTemp`` class is a class that enables generate experiment results such as IC and backtest in a certain format. We have provided three different `Record Template` class:
 
-- ``SignalRecord``: This class generates the `preidction` results of the model.
+- ``SignalRecord``: This class generates the `prediction` results of the model.
 - ``SigAnaRecord``: This class generates the `IC`, `ICIR`, `Rank IC` and `Rank ICIR` of the model.
 - ``PortAnaRecord``: This class generates the results of `backtest`. The detailed information about `backtest` as well as the available `strategy`, users can refer to `Strategy <../component/strategy.html>`_ and `Backtest <../component/backtest.html>`_.
 

docs/component/workflow.rst

@@ -103,6 +103,12 @@ After saving the config into `configuration.yaml`, users could start the workflo
 
     qrun configuration.yaml
 
+If users want to use ``qrun`` under debug mode, please use the following command:
+
+.. code-block:: bash
+
+    python -m pdb qlib/workflow/cli.py examples/benchmarks/LightGBM/workflow_config_lightgbm_Alpha158.yaml
+
 .. note:: 
 
     `qrun` will be placed in your $PATH directory when installing ``Qlib``.