Fix flake8 errors

Author: MDUYN

.github/workflows/test.yml

@@ -34,7 +34,7 @@ jobs:
       #----------------------------------------------
       - run: python -m pip install black flake8 isort
       - run: |
-          flake8 ./investing_algorithm_framework
+          flake8 ./pyindicators
   test:
     needs: linting
     strategy:

pyindicators/indicators/crossover.py

@@ -11,17 +11,23 @@ def is_crossover(
     strict=True,
 ) -> bool:
     """
-    Returns a boolean when the first series crosses above the second series at any point or within the last n data points.
+    Returns a boolean when the first series crosses above the second
+        series at any point or within the last n data points.
 
     Args:
         data (Union[PdDataFrame, PlDataFrame]): The input data.
         first_column (str): The name of the first series.
         second_column (str): The name of the second series.
-        data_points (int, optional): The number of data points to consider. Defaults to None.
-        strict (bool, optional): If True, the first series must be strictly greater than the second series. If False, the first series must be greater than or equal to the second series. Defaults to True.
+        data_points (int, optional): The number of data points
+            to consider. Defaults to None.
+        strict (bool, optional): If True, the first series must
+            be strictly greater than the second series. If False,
+            the first series must be greater than or equal
+            to the second series. Defaults to True.
 
     Returns:
-        bool: Returns True if the first series crosses above the second series at any point or within the last n data points.
+        bool: Returns True if the first series crosses above the
+            second series at any point or within the last n data points.
     """
 
     if len(data) < 2:

pyindicators/indicators/exponential_moving_average.py

@@ -4,6 +4,7 @@
 import polars as pl
 from pyindicators.exceptions import PyIndicatorException
 
+
 def ema(
     data: Union[PdDataFrame, PlDataFrame],
     source_column: str,
@@ -21,7 +22,8 @@ def ema(
             exponential moving average. Defaults to None.
 
     Returns:
-        Union[PdDataFrame, PlDataFrame]: Returns a DataFrame with the EMA of the series.
+        Union[PdDataFrame, PlDataFrame]: Returns a DataFrame with
+            the EMA of the series.
     """
 
     if len(data) < period:
@@ -35,9 +37,11 @@ def ema(
         result_column = f"EMA_{source_column}_{period}"
 
     if isinstance(data, PdDataFrame):
-        data[result_column] = data[source_column].ewm(span=period, adjust=False).mean()
+        data[result_column] = data[source_column]\
+            .ewm(span=period, adjust=False).mean()
     else:
-        # Polars does not have a direct EWM function, so we implement it manually
+        # Polars does not have a direct EWM function,
+        # so we implement it manually
         alpha = 2 / (period + 1)
         ema_values = []
         ema_prev = data[source_column][0]  # Initialize with the first value

pyindicators/indicators/rsi.py

@@ -1,5 +1,6 @@
 from pandas import Series
 
+
 def rsi(series: Series, timeperiod=14):
     """
     Calculate the Relative Strength Index (RSI) for a given Pandas series.
@@ -30,8 +31,9 @@ def rsi(series: Series, timeperiod=14):
             i]) / timeperiod
 
     # Calculate relative strength (RS)
-    rs = avg_gain / avg_loss.replace(0,
-                                     1)  # Replace 0s with 1 to avoid division by zero
+    rs = avg_gain / avg_loss.replace(
+        0, 1
+    )  # Replace 0s with 1 to avoid division by zero
 
     # Calculate RSI
     rsi_values = 100 - (100 / (1 + rs))
@@ -59,4 +61,4 @@ def rsi(series: Series, timeperiod=14):
     #
     # # Drop intermediate columns
     # df_deep_copy.drop(['Delta'], axis=1, inplace=True)
-    # return df_deep_copy[[f'Rsi_{period}']]
+    # return df_deep_copy[[f'Rsi_{period}']]

pyindicators/indicators/simple_moving_average.py

@@ -21,7 +21,8 @@ def sma(
             simple moving average. Defaults to None.
 
     Returns:
-        Union[PdDataFrame, PlDataFrame]: Returns a DataFrame with the simple moving average of the series.
+        Union[PdDataFrame, PlDataFrame]: Returns a DataFrame
+            with the simple moving average of the series.
     """
 
     if len(data) < period:
@@ -38,8 +39,7 @@ def sma(
         data[result_column] = data[source_column].rolling(window=period).mean()
     else:
         data = data.with_column(
-            data[source_column]
-                .rolling(window=period).mean(),
+            data[source_column].rolling(window=period).mean(),
             result_column
         )