Merge pull request #53 from shivam096/yasovar-kpi-tests

Yasovar kpi tests

dinero/backend/kpi_manager.py

@@ -4,12 +4,10 @@
 Relative Strength Index (RSI), Rate of Change (ROC), and Bollinger Bands Percent (BBP)
 by utilizing the technical_indicators module.
 """
-import os
-import pandas as pd
 import backend.technical_indicators as ti
-
 from backend.stock_data_manager import get_stock_data
 
+
 # Fetches Technical Indicator
 def get_technical_indicator(ticker_symbol, length, indicator):
     """Fetches technical indicators from the technical_indicators module"""

dinero/backend/technical_indicators.py

@@ -44,6 +44,10 @@ def calculate_bollinger_bands_percent(data, length=20, num_std_dev=2):
 
 def _formatted_dataframe(data, indicator, name):
     """Formats the resulting DataFrame by appending the date and the calculated indicator."""
+    if not isinstance(data, pd.DataFrame):
+        raise TypeError("data must be a pandas DataFrame")
+    if not isinstance(name, str):
+        raise TypeError("name must be a string")
     result_df = pd.DataFrame()
     result_df['Date'] = data['Date']
     result_df[name] = indicator

dinero/tests/test_kpi.py

@@ -1,13 +1,14 @@
 """Unit tests for the KPI (Key Performance Indicators) Manager module.
 
-This module contains unittests for testing the functionality of the KPI Manager, particularly focusing on reading stock data and calculating various technical indicators.
+This module contains unittests for testing the functionality of the KPI Manager, particularly
+focusing on reading stock data and calculating various technical indicators.
 """
 import unittest
 from unittest.mock import patch
 import pandas as pd
+from backend.technical_indicators import _formatted_dataframe
 from backend.kpi_manager import get_technical_indicator
 
-
 class TestKPIManager(unittest.TestCase):
     """Tests for KPI manager and Technical indicators"""
 
@@ -68,6 +69,35 @@ def test_get_technical_indicator_unsupported(self):
         with self.assertRaises(ValueError):
             get_technical_indicator("AAPL", 10, "UNKNOWN")
 
+    def test_formatted_dataframe(self):
+        """Test the _formatted_dataframe function to ensure it formats the DataFrame correctly."""
+        mock_data = pd.DataFrame({
+            'Date': ['2023-01-01', '2023-01-02', '2023-01-03'],
+            'Dummy': [1, 2, 3]  # This column won't be used, just to simulate structure
+        })
+        mock_indicator = pd.Series([0.1, 0.5, 0.9], name='BBP')
+        result_df = _formatted_dataframe(mock_data, mock_indicator, 'BBP')
+
+        self.assertIsInstance(result_df, pd.DataFrame)
+        self.assertListEqual(list(result_df['Date']), list(mock_data['Date']))
+
+    def test_formatted_dataframe_with_invalid_data_type(self):
+        """Test _formatted_dataframe with invalid data type for data parameter."""
+        mock_data = "not a dataframe"
+        mock_indicator = pd.Series([1, 2, 3])
+        with self.assertRaises(TypeError) as context:
+            _formatted_dataframe(mock_data, mock_indicator, 'TestName')
+        self.assertTrue("data must be a pandas DataFrame" in str(context.exception))
+
+    def test_formatted_dataframe_with_invalid_name_type(self):
+        """Test _formatted_dataframe with invalid data type for name parameter."""
+        mock_data = pd.DataFrame({'Date': ['2023-01-01', '2023-01-02'], 'Close': [100, 200]})
+        mock_indicator = pd.Series([1, 2])
+        with self.assertRaises(TypeError) as context:
+            _formatted_dataframe(mock_data, mock_indicator, 123)
+        self.assertTrue("name must be a string" in str(context.exception))
+
+
 # This allows the test suite to be run from the command line
 if __name__ == "__main__":
     unittest.main()