From 7d092f39c83e01886d4b1f4d98a9242e5c6d50ee Mon Sep 17 00:00:00 2001
From: Young <afe.young@gmail.com>
Date: Thu, 26 Nov 2020 09:15:13 +0000
Subject: [PATCH] data.rst update

---
 docs/component/data.rst | 40 ++++++++++++++++++++--------------------
 1 file changed, 20 insertions(+), 20 deletions(-)

diff --git a/docs/component/data.rst b/docs/component/data.rst
index fda3e0db0..22565c39d 100644
--- a/docs/component/data.rst
+++ b/docs/component/data.rst
@@ -143,7 +143,7 @@ Filter
     Expression dynamic instrument filter. Filter the instruments based on a certain expression. An expression rule indicating a certain feature field is required.
     
     - `basic features filter`: rule_expression = '$close/$open>5'
-    - `cross-sectional features filter` : rule_expression = '$rank($close)<10'
+    - `cross-sectional features filter` \: rule_expression = '$rank($close)<10'
     - `time-sequence features filter`: rule_expression = '$Ref($close, 3)>100'
 
 To know more about ``Filter``, please refer to `Filter API <../reference/api.html#module-qlib.data.filter>`_.
@@ -169,11 +169,11 @@ Here are some interfaces of the ``QlibDataLoader`` class:
 - `load(instruments, start_time=None, end_time=None)`
     - This method loads the data as pd.DataFrame
     - Parameters:
-        - `instruments` : str or dict
+        - `instruments` \: str or dict
             it can either be the market name or the config file of instruments generated by InstrumentProvider.
-        - `start_time` : str
+        - `start_time` \: str
             start of the time range.
-        - `end_time` : str
+        - `end_time` \: str
             end of the time range.
     - Returns:
         - The data being loaded with type `pd.DataFrame`
@@ -181,15 +181,15 @@ Here are some interfaces of the ``QlibDataLoader`` class:
 - `load_group_df(instruments, exprs: list, names: list, start_time=None, end_time=None)`
     -  This method loads the dataframe for specific group.
     - Parameters:
-        - `instruments` : str or dict
+        - `instruments` \: str or dict
             it can either be the market name or the config file of instruments generated by InstrumentProvider.
-        - `exprs` : list
+        - `exprs` \: list
             the expressions to describe the content of the data.
-        - `names` : list
+        - `names` \: list
             the name of the data.
-        - `start_time` : str
+        - `start_time` \: str
             start of the time range.
-        - `end_time` : str
+        - `end_time` \: str
             end of the time range.
     - Returns:
         - The queried data in type `pd.DataFrame`.
@@ -220,7 +220,7 @@ Here are some important interfaces that ``DataHandlerLP`` provides:
 - `__init__(instruments=None, start_time=None, end_time=None, data_loader: Tuple[dict, str, DataLoader] = None, infer_processors=[], learn_processors=[], process_type=PTYPE_A, **kwargs)`
     - Initialization of the class.
     - Parameters:
-        - `infer_processors` : list
+        - `infer_processors` \: list
             - list of <description info> of processors to generate data for inference
             - example of <description info>:
 
@@ -238,7 +238,7 @@ Here are some important interfaces that ``DataHandlerLP`` provides:
                     "DropnaFeature"
                 3) object instance of Processor
 
-        - `learn_processors` : list
+        - `learn_processors` \: list
             similar to infer_processors, but for generating data for learning models
 
         - `process_type`: str
@@ -253,13 +253,13 @@ Here are some important interfaces that ``DataHandlerLP`` provides:
 - `fetch(selector: Union[pd.Timestamp, slice, str] = slice(None, None), level: Union[str, int] = "datetime", col_set=DataHandler.CS_ALL, data_key: str = DK_I)`    
     - This method fetches data from underlying data source
     - Parameters:
-        - `selector` : Union[pd.Timestamp, slice, str]
+        - `selector` \: Union[pd.Timestamp, slice, str]
             describe how to select data by index.
-        - `level` : Union[str, int]
+        - `level` \: Union[str, int]
             which index level to select the data.
-        - `col_set` : str
+        - `col_set` \: str
             select a set of meaningful columns.(e.g. features, columns).
-        - `data_key` : str
+        - `data_key` \: str
             The data to fetch:  DK_*.
     - Returns:
         - The retrieved results in the type: `pd.DataFrame`.
@@ -267,9 +267,9 @@ Here are some important interfaces that ``DataHandlerLP`` provides:
 - `get_cols(col_set=DataHandler.CS_ALL, data_key: str = DK_I)`   
     - This method gets the column names.
     - Parameters:
-        - `col_set` : str
+        - `col_set` \: str
             select a set of meaningful columns.(e.g. features, columns).
-        - `data_key` : str
+        - `data_key` \: str
             the data to fetch:  DK_*.
     - Returns:
         - A list of column names.
@@ -356,7 +356,7 @@ The ``DatasetH`` class is the `dataset` with `Data Handler`. Here is the most im
 - `prepare(segments: Union[List[str], Tuple[str], str, slice], col_set=DataHandler.CS_ALL, data_key=DataHandlerLP.DK_I, **kwargs)`
     - This method prepares the data for learning and inference.
     - Parameters:
-        - `segments` : Union[List[str], Tuple[str], str, slice]
+        - `segments` \: Union[List[str], Tuple[str], str, slice]
             Describe the scope of the data to be prepared
             Here are some examples:
 
@@ -364,9 +364,9 @@ The ``DatasetH`` class is the `dataset` with `Data Handler`. Here is the most im
 
             - ['train', 'valid']
 
-        - `col_set` : str
+        - `col_set` \: str
             The col_set will be passed to self._handler when fetching data.
-        - `data_key` : str
+        - `data_key` \: str
             The data to fetch:  DK_*
             Default is DK_I, which indicate fetching data for **inference**.