56 lines
2.6 KiB
Python
56 lines
2.6 KiB
Python
import math
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from scipy.stats.stats import pearsonr
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class Correlation:
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"""
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A class to calculate the correlation coefficient between two sets of price data
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"""
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@staticmethod
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def calculate_coefficient(symbol1_prices, symbol2_prices, max_set_size_diff_pct=90, overlap_pct=90,
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max_p_value=0.05):
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"""
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Calculates the correlation coefficient between two sets of price data. Uses close price.
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:param symbol1_prices:
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:param symbol2_prices:
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:param max_set_size_diff_pct: Correlations will only be calculated if the sizes of the two price data sets are
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within this pct of each other
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:param overlap_pct:
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:param max_p_value: The maximum p value for the correlation to be meaningful
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:return: correlation coefficient, or None if coefficient could not be calculated.
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"""
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# Calculate size of intersection and determine if prices for symbols have enough overlapping timestamps for
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# correlation coefficient calculation to be meaningful. Is the smallest set at least max_set_size_diff_pct % of
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# the size of the largest set and is the overlap set size at least overlap_pct % the size of the smallest set?
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coefficient = None
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intersect_dates = (set(symbol1_prices['time']) & set(symbol2_prices['time']))
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len_smallest_set = int(min([len(symbol1_prices.index), len(symbol2_prices.index)]))
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len_largest_set = int(max([len(symbol1_prices.index), len(symbol2_prices.index)]))
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similar_size = len_largest_set * (max_set_size_diff_pct / 100) <= len_smallest_set
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enough_overlap = len(intersect_dates) >= len_smallest_set * (overlap_pct / 100)
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suitable = similar_size and enough_overlap
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if suitable:
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# Calculate coefficient on close prices
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# First filter prices to only include those that intersect
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symbol1_prices_filtered = symbol1_prices[symbol1_prices['time'].isin(intersect_dates)]
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symbol2_prices_filtered = symbol2_prices[symbol2_prices['time'].isin(intersect_dates)]
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# Calculate coefficient. Only use if p value is < 0.01 (highly likely that coefficient is valid and null
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# hypothesis is false).
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coefficient_with_p_value = pearsonr(symbol1_prices_filtered['close'], symbol2_prices_filtered['close'])
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coefficient = None if coefficient_with_p_value[1] >= max_p_value else coefficient_with_p_value[0]
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# If NaN, change to None
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if coefficient is not None and math.isnan(coefficient):
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coefficient = None
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return coefficient
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