black formatting

NitroFE/time_based_features/indicator_features/_AbsolutePriceOscillator.py

@@ -9,9 +9,10 @@
 
 
 class AbsolutePriceOscillator:
-    """ 
+    """
     Provided dataframe must be in ascending order.
     """
+
     def __init__(
         self,
         fast_period: int = 5,

NitroFE/time_based_features/indicator_features/_AverageDirectionalMovementIndex.py

@@ -19,8 +19,9 @@
 
 
 class AverageDirectionalMovementIndex:
-    def __init__(self,
-            directional_movement_lookback_period: int = 4,
+    def __init__(
+        self,
+        directional_movement_lookback_period: int = 4,
         directional_movement_min_periods: int = None,
         directional_movement_smoothing_period: int = 14,
         directional_movement_smoothing_min_periods: int = 0,
@@ -29,7 +30,8 @@ def __init__(self,
         true_range_lookback: int = 4,
         average_true_range_span: int = 6,
         true_range_min_periods: int = None,
-        average_true_range_periods: int = 1):
+        average_true_range_periods: int = 1,
+    ):
         """
         Parameters
         ----------
@@ -54,9 +56,7 @@ def __init__(self,
         average_true_range_periods : int, optional
             Minimum number of observations in window required to have a value for average true range calculation , by default 1
         """
-        self.directional_movement_lookback_period = (
-            directional_movement_lookback_period
-        )
+        self.directional_movement_lookback_period = directional_movement_lookback_period
         self.directional_movement_min_periods = directional_movement_min_periods
         self.directional_movement_smoothing_period = (
             directional_movement_smoothing_period
@@ -78,13 +78,13 @@ def __init__(self,
         self.average_true_range_periods = average_true_range_periods
 
     def _plus_dm(self, x, look_back_period):
-        look_back_period=int(look_back_period/2)
+        look_back_period = int(look_back_period / 2)
         return np.max(x.iloc[look_back_period:]) - np.max(
             x.iloc[0 : look_back_period - 1]
         )
 
     def _minus_dm(self, x, look_back_period):
-        look_back_period=int(look_back_period/2)
+        look_back_period = int(look_back_period / 2)
         return np.min(x.iloc[0 : look_back_period - 1]) - np.min(
             x.iloc[look_back_period:]
         )
@@ -136,8 +136,6 @@ def fit(
                 operation="mean",
             )
 
-
-
         plus_dma = self._plus_dma_object._template_feature_calculation(
             function_name="plus_dma",
             win_function=_identity_window,
@@ -191,7 +189,11 @@ def fit(
             dataframe=dataframe,
             first_fit=first_fit,
         )
-        average_true_range=average_true_range.to_frame() if isinstance(average_true_range,pd.Series) else average_true_range
+        average_true_range = (
+            average_true_range.to_frame()
+            if isinstance(average_true_range, pd.Series)
+            else average_true_range
+        )
 
         plus_directional_index = 100 * (smoothed_plus_dma.div(average_true_range))
         minus_directional_index = 100 * (smoothed_minus_dma.div(average_true_range))
@@ -206,5 +208,3 @@ def fit(
             first_fit=first_fit,
         )
         return adx
-
-

NitroFE/time_based_features/indicator_features/_AverageTrueRange.py

@@ -2,18 +2,24 @@
 import pandas as pd
 from typing import Union, Callable
 
-from NitroFE.time_based_features.weighted_window_features.weighted_windows import _equal_window, _identity_window
+from NitroFE.time_based_features.weighted_window_features.weighted_windows import (
+    _equal_window,
+    _identity_window,
+)
 from NitroFE.time_based_features.weighted_window_features.weighted_window_features import (
     weighted_window_features,
 )
 
+
 class AverageTrueRange:
-    def __init__(self,
+    def __init__(
+        self,
         true_range_lookback: int = 4,
         average_true_range_span: int = 6,
         true_range_min_periods: int = None,
         average_true_range_periods: int = 1,
-        return_true_range:bool=False):
+        return_true_range: bool = False,
+    ):
         """
         Parameters
         ----------
@@ -28,13 +34,13 @@ def __init__(self,
         return_true_range : bool, optional
             If true, True range is returned instead of Average True range
         """
-        
+
         self.true_range_lookback = true_range_lookback
         self.average_true_range_span = average_true_range_span
 
         self.true_range_min_periods = true_range_min_periods
         self.average_true_range_periods = average_true_range_periods
-        self.return_true_range=return_true_range
+        self.return_true_range = return_true_range
 
     def true_range(self, x):
         return np.max(
@@ -45,11 +51,7 @@ def true_range(self, x):
             ]
         )
 
-    def fit(
-        self,
-        dataframe: Union[pd.DataFrame, pd.Series],
-        first_fit: bool = True
-    ):
+    def fit(self, dataframe: Union[pd.DataFrame, pd.Series], first_fit: bool = True):
         """
         For your training/initial fit phase (very first fit) use fit_first=True, and for any production/test implementation pass fit_first=False
 
@@ -96,4 +98,4 @@ def fit(
             operation_args=(),
         )
 
-        return average_true_range
+        return average_true_range

NitroFE/time_based_features/indicator_features/_ElasticSeriesWeightedAverage.py

@@ -11,8 +11,7 @@
 
 
 class ElasticSeriesWeightedAverage:
-    def __init__(self,
-        weight_sum_lookback: int = 4):
+    def __init__(self, weight_sum_lookback: int = 4):
         """
         Parameters
         ----------
@@ -25,7 +24,8 @@ def fit(
         self,
         dataframe: Union[pd.DataFrame, pd.Series],
         dataframe_for_weight: Union[pd.DataFrame, pd.Series],
-        first_fit: bool = True):
+        first_fit: bool = True,
+    ):
         """
         elastic_series_weighted_average
         This feature is an implementation of elastic volume weighted moving average
@@ -55,7 +55,6 @@ def fit(
         if first_fit:
             self._object = weighted_window_features()
 
-
         rolling_sum = self._object._template_feature_calculation(
             function_name="_lag_object",
             win_function=_identity_window,
@@ -70,7 +69,7 @@ def fit(
 
         if isinstance(dataframe, pd.Series):
             dataframe = dataframe.to_frame()
-        
+
         eswa = pd.DataFrame(np.zeros(dataframe.shape), columns=dataframe.columns)
         print(dataframe.shape)
         print(eswa)
@@ -95,8 +94,7 @@ def fit(
                 + ((r1[1] - previous_eswa) * (r3[1] / r4[1])).fillna(0)
             ).values[0]
 
-
-        res=eswa.iloc[1:][ll]
-
+        res = eswa.iloc[1:][ll]
+
         self.values_from_last_run = eswa.iloc[-1:]
-        return res
+        return res

NitroFE/time_based_features/indicator_features/_KeltnerChannel.py

@@ -15,6 +15,7 @@
     ExponentialMovingFeature,
 )
 
+
 class KeltnerChannel:
     def __init__(
         self,

NitroFE/time_based_features/indicator_features/_MovingAverageConvergenceDivergence.py

@@ -12,9 +12,10 @@
 
 
 class MovingAverageConvergenceDivergence:
-    """ 
+    """
     Provided dataframe must be in ascending order.
     """
+
     def __init__(
         self,
         fast_period: int = 26,
@@ -32,7 +33,7 @@ def __init__(
         return_histogram=False,
     ):
         """
-        
+
         Parameters
         ----------
         fast_period : int, optional
@@ -86,7 +87,7 @@ def fit(
         For your training/initial fit phase (very first fit) use fit_first=True, and for any production/test implementation pass fit_first=False
 
         Returns --> Smoothed signal line , macd histogram
-        
+
         Parameters
         ----------
         dataframe : Union[pd.DataFrame, pd.Series]

NitroFE/time_based_features/indicator_features/_PercentageValueOscillator.py

@@ -9,14 +9,15 @@
 
 
 class PercentageValueOscillator:
-    """ 
+    """
     Provided dataframe must be in ascending order.
     """
+
     def __init__(
         self,
         fast_period: int = 4,
         slow_period: int = 8,
-        smoothing_period:int = 9,
+        smoothing_period: int = 9,
         fast_operation: str = "mean",
         slow_operation: str = "mean",
         initialize_using_operation: bool = False,
@@ -52,7 +53,7 @@ def __init__(
         """
         self.span_fast = fast_period
         self.span_slow = slow_period
-        self.span_smoothing=smoothing_period
+        self.span_smoothing = smoothing_period
 
         self.min_periods = min_periods
 

NitroFE/time_based_features/indicator_features/_RelativeStrengthIndex.py

@@ -1,5 +1,3 @@
-
-
 import numpy as np
 import pandas as pd
 from typing import Union
@@ -10,13 +8,18 @@
     _equal_window,
     _identity_window,
 )
-from NitroFE.time_based_features.moving_average_features.moving_average_features import ExponentialMovingFeature,HullMovingFeature,\
-    KaufmanAdaptiveMovingAverage,FractalAdaptiveMovingAverage,TripleExponentialMovingFeature,SmoothedMovingAverage
+from NitroFE.time_based_features.moving_average_features.moving_average_features import (
+    ExponentialMovingFeature,
+    HullMovingFeature,
+    KaufmanAdaptiveMovingAverage,
+    FractalAdaptiveMovingAverage,
+    TripleExponentialMovingFeature,
+    SmoothedMovingAverage,
+)
 
 
 class RelativeStrengthIndex:
-    def __init__(self,
-        lookback_period:int=8):
+    def __init__(self, lookback_period: int = 8):
         """
         Parameters
         ----------
@@ -25,19 +28,16 @@ def __init__(self,
         """
         self.lookback_period = lookback_period
 
-    def _diff_pos(self,x): 
-        diff_val=x.iloc[-1]-x.iloc[-2]
-        return diff_val if diff_val>0 else 0 
+    def _diff_pos(self, x):
+        diff_val = x.iloc[-1] - x.iloc[-2]
+        return diff_val if diff_val > 0 else 0
 
-    def _diff_neg(self,x): 
-        diff_val=x.iloc[-1]-x.iloc[-2]
-        res=diff_val if diff_val<0 else 0
+    def _diff_neg(self, x):
+        diff_val = x.iloc[-1] - x.iloc[-2]
+        res = diff_val if diff_val < 0 else 0
         return -res
 
-    def fit(
-        self,
-        dataframe: Union[pd.DataFrame, pd.Series],
-        first_fit: bool = True):
+    def fit(self, dataframe: Union[pd.DataFrame, pd.Series], first_fit: bool = True):
         """
 
         Provided dataframe must be in ascending order.
@@ -59,10 +59,13 @@ def fit(
             self._up_object = weighted_window_features()
             self._down_object = weighted_window_features()
 
-            self._up_smoothed=SmoothedMovingAverage(lookback_period=self.lookback_period)
-            self._down_smoothed=SmoothedMovingAverage(lookback_period=self.lookback_period)
+            self._up_smoothed = SmoothedMovingAverage(
+                lookback_period=self.lookback_period
+            )
+            self._down_smoothed = SmoothedMovingAverage(
+                lookback_period=self.lookback_period
+            )
 
-
         if isinstance(dataframe, pd.Series):
             dataframe = dataframe.to_frame()
 
@@ -71,7 +74,7 @@ def fit(
             win_function=_identity_window,
             first_fit=first_fit,
             dataframe=dataframe,
-            window=2 ,
+            window=2,
             min_periods=None,
             symmetric=None,
             operation=self._diff_pos,
@@ -83,15 +86,19 @@ def fit(
             win_function=_identity_window,
             first_fit=first_fit,
             dataframe=dataframe,
-            window=2 ,
+            window=2,
             min_periods=None,
             symmetric=None,
             operation=self._diff_neg,
             operation_args=(),
         )
 
-        smoothed_up_value=self._up_smoothed.fit(dataframe=up_value,first_fit=first_fit)
-        smoothed_down_value=self._down_smoothed.fit(dataframe=down_value,first_fit=first_fit)
+        smoothed_up_value = self._up_smoothed.fit(
+            dataframe=up_value, first_fit=first_fit
+        )
+        smoothed_down_value = self._down_smoothed.fit(
+            dataframe=down_value, first_fit=first_fit
+        )
 
-        rsi =  100 -100/(1+(smoothed_up_value/smoothed_down_value))
-        return rsi     
+        rsi = 100 - 100 / (1 + (smoothed_up_value / smoothed_down_value))
+        return rsi