Improved project layout

mt5_correlation/correlation.py

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+import math
+from scipy.stats.stats import pearsonr
+
+
+class Correlation:
+    """
+    A class to calculate the correlation coefficient between two sets of price data
+    """
+
+    @staticmethod
+    def calculate_coefficient(symbol1_prices, symbol2_prices):
+        """
+        Calculates the correlation coefficient between two sets of price data. Uses close price.
+
+        :param symbol1_prices:
+        :param symbol2_prices:
+        :return: correlation coefficient, or None if coefficient could not be calculated.
+        """
+        # Calculate size of intersection and determine if prices for symbols have enough overlapping timestamps for
+        # correlation coefficient calculation to be meaningful. Is the smallest set at least 90% of the size of the
+        # largest set and is the overlap set size at least 90% the size of the smallest set?
+        coefficient = None
+
+        intersect_dates = (set(symbol1_prices['time']) & set(symbol2_prices['time']))
+        len_smallest_set = int(min([len(symbol1_prices.index), len(symbol2_prices.index)]))
+        len_largest_set = int(max([len(symbol1_prices.index), len(symbol2_prices.index)]))
+        similar_size = len_largest_set * .9 <= len_smallest_set
+        enough_overlap = len(intersect_dates) >= len_smallest_set * .9
+        suitable = similar_size and enough_overlap
+
+        if suitable:
+            # Calculate coefficient on close prices
+
+            # First filter prices to only include those that intersect
+            symbol1_prices_filtered = symbol1_prices[symbol1_prices['time'].isin(intersect_dates)]
+            symbol2_prices_filtered = symbol2_prices[symbol2_prices['time'].isin(intersect_dates)]
+
+            # Calculate coefficient. Only use if p value is < 0.01 (highly likely that coefficient is valid and null
+            # hypothesis is false).
+            coefficient_with_p_value = pearsonr(symbol1_prices_filtered['close'], symbol2_prices_filtered['close'])
+            coefficient = None if coefficient_with_p_value[1] > 0.01 else coefficient_with_p_value[0]
+
+            # If NaN, change to None
+            if coefficient is not None and math.isnan(coefficient):
+                coefficient = None
+
+        return coefficient
+
+
+