Fix typos and grammar errors in docstrings and comments (microsoft#1366)

* fix gramma error in doc strings

* fix typos in exchange.py

* fix typos and gramma errors

* fix typo and rename function param to avoid shading python keyword

* remove redundant parathesis; pass kwargs to parent class

* fix pyblack

* further correction

* assign -> be assigned to

Author: qianyun210603

qlib/backtest/exchange.py

@@ -27,10 +27,10 @@
 
 class Exchange:
     # `quote_df` is a pd.DataFrame class that contains basic information for backtesting
-    # After some processing, the data will later be maintained by `quote_cls` object for faster data retriving.
+    # After some processing, the data will later be maintained by `quote_cls` object for faster data retrieving.
     # Some conventions for `quote_df`
     # - $close is for calculating the total value at end of each day.
-    #   - if $close is None, the stock on that day is reguarded as suspended.
+    #   - if $close is None, the stock on that day is regarded as suspended.
     # - $factor is for rounding to the trading unit;
     #   - if any $factor is missing when $close exists, trading unit rounding will be disabled
     quote_df: pd.DataFrame
@@ -141,7 +141,7 @@ def __init__(
         if deal_price is None:
             deal_price = C.deal_price
 
-        # we have some verbose information here. So logging is enable
+        # we have some verbose information here. So logging is enabled
         self.logger = get_module_logger("online operator")
 
         # TODO: the quote, trade_dates, codes are not necessary.
@@ -168,7 +168,7 @@ def __init__(
         self.codes = codes
         # Necessary fields
         # $close is for calculating the total value at end of each day.
-        # - if $close is None, the stock on that day is reguarded as suspended.
+        # - if $close is None, the stock on that day is regarded as suspended.
         # $factor is for rounding to the trading unit
         # $change is for calculating the limit of the stock
 
@@ -271,7 +271,7 @@ def _get_limit_type(self, limit_threshold: Union[tuple, float, None]) -> str:
             raise NotImplementedError(f"This type of `limit_threshold` is not supported")
 
     def _update_limit(self, limit_threshold: Union[Tuple, float, None]) -> None:
-        # $close is may contains NaN, the nan indicates that the stock is not tradable at that timestamp
+        # $close may contain NaN, the nan indicates that the stock is not tradable at that timestamp
         suspended = self.quote_df["$close"].isna()
         # check limit_threshold
         limit_type = self._get_limit_type(limit_threshold)
@@ -356,12 +356,12 @@ def check_stock_limit(
 
         Returns
         -------
-        True: the trading of the stock is limted (maybe hit the highest/lowest price), hence the stock is not tradable
+        True: the trading of the stock is limited (maybe hit the highest/lowest price), hence the stock is not tradable
         False: the trading of the stock is not limited, hence the stock may be tradable
         """
         # NOTE:
         # **all** is used when checking limitation.
-        # For example, the stock trading is limited in a day if every miniute is limited in a day if every miniute is limited.
+        # For example, the stock trading is limited in a day if every minute is limited in a day if every minute is limited.
         if direction is None:
             # The trading limitation is related to the trading direction
             # if the direction is not provided, then any limitation from buy or sell will result in trading limitation
@@ -385,17 +385,17 @@ def check_stock_suspended(
         # is suspended
         if stock_id in self.quote.get_all_stock():
             # suspended stocks are represented by None $close stock
-            # The $close may contains NaN,
+            # The $close may contain NaN,
             close = self.quote.get_data(stock_id, start_time, end_time, "$close")
             if close is None:
                 # if no close record exists
                 return True
             elif isinstance(close, IndexData):
-                # **any** non-NaN $close represents trading opportunity may exists
+                # **any** non-NaN $close represents trading opportunity may exist
                 #  if all returned is nan, then the stock is suspended
                 return cast(bool, cast(IndexData, close).isna().all())
             else:
-                # it is single value, make sure is is not None
+                # it is single value, make sure is not None
                 return np.isnan(close)
         else:
             # if the stock is not in the stock list, then it is not tradable and regarded as suspended
@@ -540,8 +540,8 @@ def generate_amount_position_from_weight_position(
         direction: OrderDir = OrderDir.BUY,
     ) -> dict:
         """
-        The generate the target position according to the weight and the cash.
-        NOTE: All the cash will assigned to the tradable stock.
+        Generates the target position according to the weight and the cash.
+        NOTE: All the cash will be assigned to the tradable stock.
         Parameter:
         weight_position : dict {stock_id : weight}; allocate cash by weight_position
             among then, weight must be in this range: 0 < weight < 1
@@ -639,7 +639,7 @@ def generate_order_for_target_amount_position(
         random.shuffle(sorted_ids)
         for stock_id in sorted_ids:
 
-            # Do not generate order for the nontradable stocks
+            # Do not generate order for the non-tradable stocks
             if not self.is_stock_tradable(stock_id=stock_id, start_time=start_time, end_time=end_time):
                 continue
 

qlib/contrib/data/handler.py

@@ -57,7 +57,7 @@ def __init__(
         fit_end_time=None,
         filter_pipe=None,
         inst_processor=None,
-        **kwargs,
+        **kwargs
     ):
         infer_processors = check_transform_proc(infer_processors, fit_start_time, fit_end_time)
         learn_processors = check_transform_proc(learn_processors, fit_start_time, fit_end_time)
@@ -67,7 +67,7 @@ def __init__(
             "kwargs": {
                 "config": {
                     "feature": self.get_feature_config(),
-                    "label": kwargs.get("label", self.get_label_config()),
+                    "label": kwargs.pop("label", self.get_label_config()),
                 },
                 "filter_pipe": filter_pipe,
                 "freq": freq,
@@ -82,12 +82,14 @@ def __init__(
             data_loader=data_loader,
             learn_processors=learn_processors,
             infer_processors=infer_processors,
+            **kwargs
         )
 
     def get_label_config(self):
-        return (["Ref($close, -2)/Ref($close, -1) - 1"], ["LABEL0"])
+        return ["Ref($close, -2)/Ref($close, -1) - 1"], ["LABEL0"]
 
-    def get_feature_config(self):
+    @staticmethod
+    def get_feature_config():
         # NOTE:
         # Alpha360 tries to provide a dataset with original price data
         # the original price data includes the prices and volume in the last 60 days.
@@ -99,33 +101,33 @@ def get_feature_config(self):
         names = []
 
         for i in range(59, 0, -1):
-            fields += ["Ref($close, %d)/$close" % (i)]
-            names += ["CLOSE%d" % (i)]
+            fields += ["Ref($close, %d)/$close" % i]
+            names += ["CLOSE%d" % i]
         fields += ["$close/$close"]
         names += ["CLOSE0"]
         for i in range(59, 0, -1):
-            fields += ["Ref($open, %d)/$close" % (i)]
-            names += ["OPEN%d" % (i)]
+            fields += ["Ref($open, %d)/$close" % i]
+            names += ["OPEN%d" % i]
         fields += ["$open/$close"]
         names += ["OPEN0"]
         for i in range(59, 0, -1):
-            fields += ["Ref($high, %d)/$close" % (i)]
-            names += ["HIGH%d" % (i)]
+            fields += ["Ref($high, %d)/$close" % i]
+            names += ["HIGH%d" % i]
         fields += ["$high/$close"]
         names += ["HIGH0"]
         for i in range(59, 0, -1):
-            fields += ["Ref($low, %d)/$close" % (i)]
-            names += ["LOW%d" % (i)]
+            fields += ["Ref($low, %d)/$close" % i]
+            names += ["LOW%d" % i]
         fields += ["$low/$close"]
         names += ["LOW0"]
         for i in range(59, 0, -1):
-            fields += ["Ref($vwap, %d)/$close" % (i)]
-            names += ["VWAP%d" % (i)]
+            fields += ["Ref($vwap, %d)/$close" % i]
+            names += ["VWAP%d" % i]
         fields += ["$vwap/$close"]
         names += ["VWAP0"]
         for i in range(59, 0, -1):
-            fields += ["Ref($volume, %d)/($volume+1e-12)" % (i)]
-            names += ["VOLUME%d" % (i)]
+            fields += ["Ref($volume, %d)/($volume+1e-12)" % i]
+            names += ["VOLUME%d" % i]
         fields += ["$volume/($volume+1e-12)"]
         names += ["VOLUME0"]
 
@@ -134,7 +136,7 @@ def get_feature_config(self):
 
 class Alpha360vwap(Alpha360):
     def get_label_config(self):
-        return (["Ref($vwap, -2)/Ref($vwap, -1) - 1"], ["LABEL0"])
+        return ["Ref($vwap, -2)/Ref($vwap, -1) - 1"], ["LABEL0"]
 
 
 class Alpha158(DataHandlerLP):
@@ -151,7 +153,7 @@ def __init__(
         process_type=DataHandlerLP.PTYPE_A,
         filter_pipe=None,
         inst_processor=None,
-        **kwargs,
+        **kwargs
     ):
         infer_processors = check_transform_proc(infer_processors, fit_start_time, fit_end_time)
         learn_processors = check_transform_proc(learn_processors, fit_start_time, fit_end_time)
@@ -161,7 +163,7 @@ def __init__(
             "kwargs": {
                 "config": {
                     "feature": self.get_feature_config(),
-                    "label": kwargs.get("label", self.get_label_config()),
+                    "label": kwargs.pop("label", self.get_label_config()),
                 },
                 "filter_pipe": filter_pipe,
                 "freq": freq,
@@ -176,6 +178,7 @@ def __init__(
             infer_processors=infer_processors,
             learn_processors=learn_processors,
             process_type=process_type,
+            **kwargs
         )
 
     def get_feature_config(self):
@@ -190,7 +193,7 @@ def get_feature_config(self):
         return self.parse_config_to_fields(conf)
 
     def get_label_config(self):
-        return (["Ref($close, -2)/Ref($close, -1) - 1"], ["LABEL0"])
+        return ["Ref($close, -2)/Ref($close, -1) - 1"], ["LABEL0"]
 
     @staticmethod
     def parse_config_to_fields(config):
@@ -426,4 +429,4 @@ def use(x):
 
 class Alpha158vwap(Alpha158):
     def get_label_config(self):
-        return (["Ref($vwap, -2)/Ref($vwap, -1) - 1"], ["LABEL0"])
+        return ["Ref($vwap, -2)/Ref($vwap, -1) - 1"], ["LABEL0"]

qlib/contrib/model/pytorch_adarnn.py

@@ -28,7 +28,7 @@ class ADARNN(Model):
     d_feat : int
         input dimension for each time step
     metric: str
-        the evaluate metric used in early stop
+        the evaluation metric used in early stop
     optimizer : str
         optimizer name
     GPU : str

qlib/contrib/model/pytorch_add.py

@@ -36,7 +36,7 @@ class ADD(Model):
      d_feat : int
          input dimensions for each time step
      metric : str
-         the evaluate metric used in early stop
+         the evaluation metric used in early stop
      optimizer : str
          optimizer name
      GPU : int

qlib/contrib/model/pytorch_alstm.py

@@ -30,7 +30,7 @@ class ALSTM(Model):
     d_feat : int
         input dimension for each time step
     metric: str
-        the evaluate metric used in early stop
+        the evaluation metric used in early stop
     optimizer : str
         optimizer name
     GPU : int

qlib/contrib/model/pytorch_alstm_ts.py

@@ -33,7 +33,7 @@ class ALSTM(Model):
     d_feat : int
         input dimension for each time step
     metric: str
-        the evaluate metric used in early stop
+        the evaluation metric used in early stop
     optimizer : str
         optimizer name
     GPU : int

qlib/contrib/model/pytorch_gats.py

@@ -33,7 +33,7 @@ class GATs(Model):
     d_feat : int
         input dimensions for each time step
     metric : str
-        the evaluate metric used in early stop
+        the evaluation metric used in early stop
     optimizer : str
         optimizer name
     GPU : int

qlib/contrib/model/pytorch_gats_ts.py

@@ -50,7 +50,7 @@ class GATs(Model):
     d_feat : int
         input dimensions for each time step
     metric : str
-        the evaluate metric used in early stop
+        the evaluation metric used in early stop
     optimizer : str
         optimizer name
     GPU : int

qlib/contrib/model/pytorch_gru.py

@@ -30,7 +30,7 @@ class GRU(Model):
     d_feat : int
         input dimension for each time step
     metric: str
-        the evaluate metric used in early stop
+        the evaluation metric used in early stop
     optimizer : str
         optimizer name
     GPU : str

qlib/contrib/model/pytorch_gru_ts.py

@@ -31,7 +31,7 @@ class GRU(Model):
     d_feat : int
         input dimension for each time step
     metric: str
-        the evaluate metric used in early stop
+        the evaluation metric used in early stop
     optimizer : str
         optimizer name
     GPU : str