Add stochastic oscillator tests and data

Author: MDUYN

tests/indicators/test_stochastic_oscillator.py

@@ -0,0 +1,82 @@
+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 stochastic_oscillator
+
+
+class Test(TestBaseline):
+    correct_output_csv_filename = \
+        "STOCHASTIC_OSCILLATOR_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 = stochastic_oscillator(
+            data=polars_source_df,
+            high_column="High",
+            low_column="Low",
+            close_column="Close",
+            k_period=4,
+            d_period=3,
+            k_slowing=10
+        )
+        return polars_source_df
+
+    def generate_polars_df(self, pandas_source_df):
+        pandas_source_df = stochastic_oscillator(
+            data=pandas_source_df,
+            high_column="High",
+            low_column="Low",
+            close_column="Close",
+            k_period=4,
+            d_period=3,
+            k_slowing=10
+        )
+        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)