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Improved readme and added some customisation options in correlation calculation

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This commit is contained in:
Jamie Cash
2021-02-05 12:08:14 +00:00
parent a9b65f95c3
commit 16d8a98609
5 changed files with 152 additions and 37 deletions
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mt5_correlation/correlation.py
+11 -6
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@@ -8,24 +8,29 @@ class Correlation:
"""
@staticmethod
def calculate_coefficient(symbol1_prices, symbol2_prices):
def calculate_coefficient(symbol1_prices, symbol2_prices, max_set_size_diff_pct=90, overlap_pct=90,
max_p_value=0.05):
"""
Calculates the correlation coefficient between two sets of price data. Uses close price.
:param symbol1_prices:
:param symbol2_prices:
:param max_set_size_diff_pct: Correlations will only be calculated if the sizes of the two price data sets are
within this pct of each other
:param overlap_pct:
:param max_p_value: The maximum p value for the correlation to be meaningful
: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?
# correlation coefficient calculation to be meaningful. Is the smallest set at least max_set_size_diff_pct % of
# the size of the largest set and is the overlap set size at least overlap_pct % 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
similar_size = len_largest_set * (max_set_size_diff_pct / 100) <= len_smallest_set
enough_overlap = len(intersect_dates) >= len_smallest_set * (overlap_pct / 100)
suitable = similar_size and enough_overlap
if suitable:
@@ -38,7 +43,7 @@ class Correlation:
# 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]
coefficient = None if coefficient_with_p_value[1] >= max_p_value else coefficient_with_p_value[0]
# If NaN, change to None
if coefficient is not None and math.isnan(coefficient):