Added unit tests for correlation

mt5_correlation/correlation.py

@@ -255,8 +255,8 @@ class Correlation:
         """
         Calculates the correlation coefficient between two sets of price data. Uses close price.
 
-        :param symbol1_prices: Pandas dataframe containing prices or ticks for symbol 1
+        :param symbol1_prices: Pandas dataframe containing prices for symbol 1
-        :param symbol2_prices: Pandas dataframe containing prices or ticks for symbol 2
+        :param symbol2_prices: Pandas dataframe containing prices for symbol 2
         :param min_prices: The minimum number of prices that should be used to calculate coefficient. If this threshold
             is not met then returned coefficient will be None
         :param max_set_size_diff_pct: Correlations will only be calculated if the sizes of the two price data sets are
@@ -511,6 +511,10 @@ class Correlation:
         coefficient_history_columns = ['Symbol 1', 'Symbol 2', 'Coefficient', 'UTC Date From', 'UTC Date To']
         self.coefficient_history = pd.DataFrame(columns=coefficient_history_columns)
 
+        # Clear price data and tick data
+        self.__price_data = None
+        self.__monitor_tick_data = {}
+
     def __update_coefficient_data(self, symbol1, symbol2, coefficient, date_from, date_to):
         """
         Updates the coefficient data with the latest coefficient and adds to coefficient history.

test/test_correlation.py

@@ -0,0 +1,279 @@
+import unittest
+from unittest.mock import patch
+import time
+import mt5_correlation.correlation as correlation
+import pandas as pd
+from datetime import datetime, timedelta
+from test_mt5 import Symbol
+import random
+import os
+
+
+class TestCorrelation(unittest.TestCase):
+    # Mock symbols. 4 Symbols, 3 visible.
+    mock_symbols = [Symbol(name='SYMBOL1', visible=True),
+                    Symbol(name='SYMBOL2', visible=True),
+                    Symbol(name='SYMBOL3', visible=False),
+                    Symbol(name='SYMBOL4', visible=True)]
+
+    # Start and end date for price data and mock prices: base; correlated; and uncorrelated.
+    start_date = None
+    end_date = None
+    price_columns = None
+    mock_base_prices = None
+    mock_correlated_prices = None
+    mock_uncorrelated_prices = None
+
+    def setUp(self):
+        """
+        Creates some price data fro use in tests
+        :return:
+        """
+        # Start and end date for price data and mock price dataframes. One for: base; correlated; uncorrelated and
+        # different dates.
+        self.start_date = datetime(2021, 1, 1, 1, 5, 0)
+        self.end_date = datetime(2021, 1, 1, 11, 30, 0)
+        self.price_columns = ['time', 'close']
+        self.mock_base_prices = pd.DataFrame(columns=self.price_columns)
+        self.mock_correlated_prices = pd.DataFrame(columns=self.price_columns)
+        self.mock_uncorrelated_prices = pd.DataFrame(columns=self.price_columns)
+        self.mock_correlated_different_dates = pd.DataFrame(columns=self.price_columns)
+
+        # Build the price data for the test. One price every 5 minutes for 500 rows. Base will use min for price,
+        # correlated will use min + 5 and uncorrelated will use random
+        for date in (self.start_date + timedelta(minutes=m) for m in range(0, 500*5, 5)):
+            self.mock_base_prices = self.mock_base_prices.append(pd.DataFrame(columns=self.price_columns,
+                                                                              data=[[date, date.minute]]))
+            self.mock_correlated_prices = \
+                self.mock_correlated_prices.append(pd.DataFrame(columns=self.price_columns,
+                                                                data=[[date, date.minute + 5]]))
+            self.mock_uncorrelated_prices = \
+                self.mock_uncorrelated_prices.append(pd.DataFrame(columns=self.price_columns,
+                                                                  data=[[date, random.randint(0, 1000000)]]))
+
+            self.mock_correlated_different_dates = \
+                self.mock_correlated_different_dates.append(pd.DataFrame(columns=self.price_columns,
+                                                                         data=[[date + timedelta(minutes=100),
+                                                                                date.minute + 5]]))
+
+    @patch('mt5_correlation.mt5.MetaTrader5')
+    def test_calculate(self, mock):
+        """
+        Test the calculate method. Uses mock for MT5 symbols and prices.
+        :param mock:
+        :return:
+        """
+        # Mock symbol return values
+        mock.symbols_get.return_value = self.mock_symbols
+
+        # Correlation class
+        cor = correlation.Correlation()
+
+        # Calculate for price data. We should have 100% matching dates in sets. Get prices should be called 3 times.
+        # We dont have a SYMBOL2 as this is set as not visible. Correlations should be as follows:
+        #   SYMBOL1:SYMBOL2 should be fully correlated (1)
+        #   SYMBOL1:SYMBOL4 should be uncorrelated (0)
+        #   SYMBOL2:SYMBOL4 should be uncorrelated (0)
+        # We will not use p_value as the last set uses random numbers so p value will not be useful.
+        mock.copy_rates_range.side_effect = [self.mock_base_prices, self.mock_correlated_prices,
+                                             self.mock_uncorrelated_prices]
+        cor.calculate(date_from=self.start_date, date_to=self.end_date, timeframe=5, min_prices=100,
+                      max_set_size_diff_pct=100, overlap_pct=100, max_p_value=1)
+
+        # Test the output. We should have 3 rows. S1:S2 c=1, S1:S4 c<1, S2:S4 c<1>
+        self.assertEqual(len(cor.coefficient_data.index), 3, "There should be three correlations rows calculated.")
+        self.assertEqual(cor.coefficient_data.iloc[0, 2], 1, "The correlation for SYMBOL1:SYMBOL2 should be 1.")
+        self.assertTrue(cor.coefficient_data.iloc[1, 2] < 1, "The correlation for SYMBOL1:SYMBOL4 should be <1.")
+        self.assertTrue(cor.coefficient_data.iloc[2, 2] < 1, "The correlation for SYMBOL2:SYMBOL4 should be <1.")
+
+        # Set the monitoring threshold to 1 and get filtered correlations. There should only be 1
+        cor.monitoring_threshold = 1
+        self.assertEqual(len(cor.filtered_coefficient_data.index), 1, "There should only be 1 row in filtered "
+                                                                      "coefficient data.")
+
+        # Now were going to recalculate, but this time:
+        #   SYMBOL1:SYMBOL2 will have non overlapping dates and coefficient should be None. There shouldn't be a row
+        #   SYMBOL1:SYMBOL4 will be correlated
+        #   SYMBOL2:SYMBOL4 will have non overlapping dates and coefficient should be None.  There shouldn't be a row.
+        mock.copy_rates_range.side_effect = [self.mock_base_prices, self.mock_correlated_different_dates,
+                                             self.mock_correlated_prices]
+        cor.calculate(date_from=self.start_date, date_to=self.end_date, timeframe=5, min_prices=100,
+                      max_set_size_diff_pct=100, overlap_pct=100, max_p_value=1)
+        self.assertEqual(len(cor.coefficient_data.index), 1, "There should be one correlations rows calculated.")
+        self.assertEqual(cor.coefficient_data.iloc[0, 2], 1, "The correlation for SYMBOL1:SYMBOL4 should be 1.")
+
+        # Get the price data used to calculate the coefficients fro symbol 1. It should match mock_base_prices.
+        price_data = cor.get_price_data('SYMBOL1')
+        self.assertTrue(price_data.equals(self.mock_base_prices), "Price data returned post calculation should match "
+                                                                  "mock price data.")
+
+    def test_calculate_coefficient(self):
+        """
+        Tests the coefficient calculation.
+        :return:
+        """
+        # Correlation class
+        cor = correlation.Correlation()
+
+        # Test 2 correlated sets
+        coefficient = cor.calculate_coefficient(self.mock_base_prices, self.mock_correlated_prices)
+        self.assertEqual(coefficient, 1, "Coefficient should be 1.")
+
+        # Test 2 uncorrelated sets. Set p value to 1 to force correlation to be returned.
+        coefficient = cor.calculate_coefficient(self.mock_base_prices, self.mock_uncorrelated_prices, max_p_value=1)
+        self.assertTrue(coefficient < 1, "Coefficient should be < 1.")
+
+        # Test 2 sets where prices dont overlap
+        coefficient = cor.calculate_coefficient(self.mock_base_prices, self.mock_correlated_different_dates)
+        self.assertTrue(coefficient < 1, "Coefficient should be None.")
+
+    @patch('mt5_correlation.mt5.MetaTrader5')
+    def test_get_ticks(self, mock):
+        """
+        Test that caching works. For the purpose of this test, we can use price data rather than tick data.
+        Mock 2 different sets of prices. Get three times. Base, One within cache threshold and one outside. Set 1
+        should match set 2 but differ from set 3.
+        :param mock:
+        :return:
+        """
+
+        # Correlation class to test
+        cor = correlation.Correlation()
+
+        # Mock the tick data to contain 2 different sets. Then get twice. They should match as the data was cached.
+        mock.copy_ticks_range.side_effect = [self.mock_base_prices, self.mock_correlated_prices]
+        base_ticks = cor.get_ticks('SYMBOL1', None, None, cache_time=3)
+        cached_ticks = cor.get_ticks('SYMBOL1', None, None, cache_time=3)
+        self.assertTrue(base_ticks.equals(cached_ticks),
+                        "Both sets of tick data should match as set 2 came from cache.")
+
+        # Wait 3 seconds
+        time.sleep(3)
+
+        # Retrieve again. This one should be different as the cache has expired.
+        non_cached_ticks = cor.get_ticks('SYMBOL1', None, None, cache_time=3)
+        self.assertTrue(not base_ticks.equals(non_cached_ticks),
+                        "Both sets of tick data should differ as cached data had expired.")
+
+    @patch('mt5_correlation.mt5.MetaTrader5')
+    def test_start_monitor(self, mock):
+        """
+        Test that starting the monitor and running for 2 seconds produces two sets of coefficint history when using an
+        interval of 1 second.
+        :param mock:
+        :return:
+        """
+        # Mock symbol return values
+        mock.symbols_get.return_value = self.mock_symbols
+
+        # Correlation class
+        cor = correlation.Correlation()
+
+        # Calculate for price data. We should have 100% matching dates in sets. Get prices should be called 3 times.
+        # We dont have a SYMBOL2 as this is set as not visible. All pairs should be correlated for the purpose of this
+        # test.
+        mock.copy_rates_range.side_effect = [self.mock_base_prices, self.mock_correlated_prices,
+                                             self.mock_correlated_prices]
+
+        cor.calculate(date_from=self.start_date, date_to=self.end_date, timeframe=5, min_prices=100,
+                      max_set_size_diff_pct=100, overlap_pct=100, max_p_value=1)
+
+        # We will build some tick data for each symbol and patch it in. Tick data will be from 10 seconds ago to now.
+        # We only need to patch in one set of tick data for each symbol as it will be cached.
+        columns = ['time', 'ask']
+        starttime = datetime.now() - timedelta(seconds=10)
+        tick_data_s1 = pd.DataFrame(columns=columns)
+        tick_data_s3 = pd.DataFrame(columns=columns)
+        tick_data_s4 = pd.DataFrame(columns=columns)
+
+        now = datetime.now()
+        price_base = 1
+        while starttime < now:
+            tick_data_s1 = tick_data_s1.append(pd.DataFrame(columns=columns, data=[[starttime, price_base * 0.5]]))
+            tick_data_s3 = tick_data_s1.append(pd.DataFrame(columns=columns, data=[[starttime, price_base * 0.1]]))
+            tick_data_s4 = tick_data_s1.append(pd.DataFrame(columns=columns, data=[[starttime, price_base * 0.25]]))
+            starttime = starttime + timedelta(milliseconds=10*random.randint(0, 100))
+            price_base += 1
+
+        # Patch it in
+        mock.copy_ticks_range.side_effect = [tick_data_s1, tick_data_s3, tick_data_s4]
+
+        # Start the monitor. Run every second. Use ~10 seconds of data. Were not testing the overlap and price data
+        # quality metrics here as that is set elsewhere so these can be set to not take effect. Set cache level high
+        # and don't use autosave. Timer runs in a separate thread so test can continue after it has started.
+        cor.start_monitor(interval=1, from_mins=0.66, min_prices=0, max_set_size_diff_pct=0, overlap_pct=0,
+                          max_p_value=1, cache_time=100, autosave=False)
+
+        # Wait 2 seconds so timer runs twice
+        time.sleep(2)
+
+        # Stop the monitor
+        cor.stop_monitor()
+
+        # We should have 2 coefficients calculated for each symbol pair
+        self.assertEqual(len(cor.coefficient_history.index), 6)
+
+    @patch('mt5_correlation.mt5.MetaTrader5')
+    def test_load_and_save(self, mock):
+        """Calculate and run monitor for a few seconds. Store the data. Save it, load it then compare against stored
+        data."""
+
+        # Correlation class
+        cor = correlation.Correlation()
+
+        # Patch symbol and price data, then calculate
+        mock.symbols_get.return_value = self.mock_symbols
+        mock.copy_rates_range.side_effect = [self.mock_base_prices, self.mock_correlated_prices,
+                                             self.mock_correlated_prices]
+        cor.calculate(date_from=self.start_date, date_to=self.end_date, timeframe=5, min_prices=100,
+                      max_set_size_diff_pct=100, overlap_pct=100, max_p_value=1)
+
+        # Patch the tick data
+        columns = ['time', 'ask']
+        starttime = datetime.now() - timedelta(seconds=10)
+        tick_data_s1 = pd.DataFrame(columns=columns)
+        tick_data_s3 = pd.DataFrame(columns=columns)
+        tick_data_s4 = pd.DataFrame(columns=columns)
+        now = datetime.now()
+        price_base = 1
+        while starttime < now:
+            tick_data_s1 = tick_data_s1.append(pd.DataFrame(columns=columns, data=[[starttime, price_base * 0.5]]))
+            tick_data_s3 = tick_data_s1.append(pd.DataFrame(columns=columns, data=[[starttime, price_base * 0.1]]))
+            tick_data_s4 = tick_data_s1.append(pd.DataFrame(columns=columns, data=[[starttime, price_base * 0.25]]))
+            starttime = starttime + timedelta(milliseconds=10 * random.randint(0, 100))
+            price_base += 1
+        mock.copy_ticks_range.side_effect = [tick_data_s1, tick_data_s3, tick_data_s4]
+
+        # Start monitor and run for a seconds with a 1 second interval to produce some coefficient history. Then stop
+        # the monitor
+        cor.start_monitor(interval=1, from_mins=0.66, min_prices=0, max_set_size_diff_pct=0, overlap_pct=0,
+                          max_p_value=1, cache_time=100, autosave=False)
+        time.sleep(2)
+        cor.stop_monitor()
+
+        # Get copies of data that will be saved.
+        cd_copy = cor.coefficient_data
+        pd_copy = cor.get_price_data('SYMBOL1')
+        mtd_copy = cor.get_ticks('SYMBOL1', cache_only=True)
+        ch_copy = cor.coefficient_history
+
+        # Save, reset data, then reload
+        cor.save("unittest.cpd")
+        cor.load("unittest.cpd")
+
+        # Test that the reloaded data matches the original
+        self.assertTrue(cd_copy.equals(cor.coefficient_data),
+                        "Saved and reloaded coefficient data should match original.")
+        self.assertTrue(pd_copy.equals(cor.get_price_data('SYMBOL1')),
+                        "Saved and reloaded price data should match original.")
+        self.assertTrue(mtd_copy.equals(cor.get_ticks('SYMBOL1', cache_only=True)),
+                        "Saved and reloaded tick data should match original.")
+        self.assertTrue(ch_copy.equals(cor.coefficient_history),
+                        "Saved and reloaded coefficient history should match original.")
+
+        # Cleanup. delete the file
+        os.remove("unittest.cpd")
+
+
+if __name__ == '__main__':
+    unittest.main()