Fix flake8 warnings

Author: MDUYN

pyindicators/indicators/macd.py

@@ -39,7 +39,7 @@ def macd(
 
     Returns:
         Union[pd.DataFrame, pl.DataFrame]: DataFrame with MACD, Signal
-            Line, and Histogram.
+        Line, and Histogram.
     """
     if source_column not in data.columns:
         raise PyIndicatorException(
@@ -60,9 +60,47 @@ def macd(
 
         # Calculate the MACD Histogram
         data[histogram_column] = data[macd_column] - data[signal_column]
+
+        # Delete the temporary EMA columns
+        data = data.drop(columns=[f"EMA_{short_period}", f"EMA_{long_period}"])
         return data
     elif isinstance(data, pl.DataFrame):
-        return None
+        # Polars implementation
+        data = data.with_columns([
+            ema(
+                data,
+                source_column,
+                short_period,
+                f"EMA_{short_period}"
+            )[f"EMA_{short_period}"],
+            ema(
+                data,
+                source_column,
+                long_period,
+                f"EMA_{long_period}"
+            )[f"EMA_{long_period}"]
+        ])
+
+        data = data.with_columns(
+            (
+                pl.col(f"EMA_{short_period}") - pl.col(f"EMA_{long_period}")
+            ).alias(macd_column)
+        )
+
+        data = data.with_columns(
+            ema(data, macd_column, signal_period, signal_column)[signal_column]
+        )
+
+        data = data.with_columns(
+            (
+                pl.col(macd_column) - pl.col(signal_column)
+            ).alias(histogram_column)
+        )
+
+        # Delete the temporary EMA columns
+        data = data.drop([f"EMA_{short_period}", f"EMA_{long_period}"])
+
+        return data
     else:
         raise PyIndicatorException(
             "Unsupported DataFrame type. Use Pandas or Polars."

tests/indicators/test_macd.py

@@ -0,0 +1,70 @@
+import pandas as pd
+import polars as pl
+import pandas.testing as pdt
+from polars.testing import assert_frame_equal
+
+from tests.resources import TestBaseline
+from pyindicators import macd
+
+
+class Test(TestBaseline):
+    correct_output_csv_filename = \
+        "MACD_BTC-EUR_BINANCE_15m_2023-12-01-00-00_2023-12-25-00-00.csv"
+
+    def generate_pandas_df(self, polars_source_df):
+        polars_source_df = macd(
+            data=polars_source_df, source_column="Close"
+        )
+        return polars_source_df
+
+    def generate_polars_df(self, pandas_source_df):
+        pandas_source_df = macd(
+            data=pandas_source_df, source_column="Close"
+        )
+        return pandas_source_df
+
+    def test_comparison_pandas(self):
+
+        # Load the correct output in a pandas dataframe
+        correct_output_pd = pd.read_csv(self.get_correct_output_csv_path())
+
+        # Load the source in a pandas dataframe
+        source = pd.read_csv(self.get_source_csv_path())
+
+        # Generate the pandas dataframe
+        output = self.generate_pandas_df(source)
+        output = output[correct_output_pd.columns]
+        output["Datetime"] = \
+            pd.to_datetime(output["Datetime"]).dt.tz_localize(None)
+        correct_output_pd["Datetime"] = \
+            pd.to_datetime(correct_output_pd["Datetime"]).dt.tz_localize(None)
+
+        pdt.assert_frame_equal(correct_output_pd, output)
+
+    def test_comparison_polars(self):
+
+        # Load the correct output in a polars dataframe
+        correct_output_pl = pl.read_csv(self.get_correct_output_csv_path())
+
+        # Load the source in a polars dataframe
+        source = pl.read_csv(self.get_source_csv_path())
+
+        # Generate the polars dataframe
+        output = self.generate_polars_df(source)
+
+        # Convert the datetime columns to datetime
+        # Convert the 'Datetime' column in both DataFrames to datetime
+        output = output.with_columns(
+            pl.col("Datetime").str.strptime(pl.Datetime).alias("Datetime")
+        )
+
+        correct_output_pl = correct_output_pl.with_columns(
+            pl.col("Datetime").str.strptime(pl.Datetime).alias("Datetime")
+        )
+        output = output[correct_output_pl.columns]
+        output = self.make_polars_column_datetime_naive(output, "Datetime")
+        correct_output_pl = self.make_polars_column_datetime_naive(
+            correct_output_pl, "Datetime"
+        )
+
+        assert_frame_equal(correct_output_pl, output)