Codependence Matrix
The functions in this part of the module are used to generate dependence and distance matrices using the codependency and distance metrics described previously.
Dependence Matrix function is used to compute codependences between elements in a given dataframe of elements using various codependence metrics like Mutual Information, Variation of Information, Distance Correlation, Spearman’s Rho, GPR distance, and GNPR distance.
Distance Matrix function can be used to compute a distance matrix from a given codependency matrix using distance metrics like angular, squared angular and absolute angular.
Note
Underlying Literature
The following sources elaborate extensively on the topic:
Codependence (Presentation Slides) by Marcos Lopez de Prado.
Implementation
Example
import pandas as pd
from arbitragelab.codependence import (get_dependence_matrix, get_distance_matrix)
# Import dataframe of returns for assets in a portfolio
asset_returns = pd.read_csv(DATA_PATH, index_col='Date', parse_dates=True)
# Calculate distance correlation matrix
distance_corr = get_dependence_matrix(asset_returns, dependence_method='distance_correlation')
# Calculate Pearson correlation matrix
pearson_corr = asset_returns.corr()
# Calculate absolute angular distance from a Pearson correlation matrix
abs_angular_dist = absolute_angular_distance(pearson_corr)
Presentation Slides
