Correlation Examples

import pandas as pd
import yfinance as yf
from mlfinlab.codependence import distance_correlation, angular_distance, absolute_angular_distance, squared_angular_distance

def get_prices(stocks, start_date="2020-03-01", end_date="2020-05-30"):
    stocks_string = " ".join(stocks)
    data = yf.download(stocks_string, start=start_date, end=end_date,
                      group_by="ticker")
    data = data.fillna(method='ffill')
    # Drop columns with no entries
    data = data.dropna(axis='columns', how='all')

    prices_df = pd.concat([data[ticker]["Close"] for ticker in stocks], axis=1)
    prices_df.columns = stocks
    return prices_df
  
asset_returns = get_prices(['IP.CN', 'NTAR.CN'])
print(asset_returns)
# Calculate distance correlation between chosen assets
distance_corr = distance_correlation(asset_returns['IP.CN'], asset_returns['NTAR.CN'])
print(distance_corr)
# Calculate angular distance between chosen assets
angular_dist = angular_distance(asset_returns['IP.CN'], asset_returns['NTAR.CN'])
print(angular_dist)
# Calculate absolute angular distance between chosen assets
angular_dist = absolute_angular_distance(asset_returns['IP.CN'], asset_returns['NTAR.CN'])
print(angular_dist)
# Calculate squared angular distance between chosen assets
angular_dist = squared_angular_distance(asset_returns['IP.CN'], asset_returns['NTAR.CN'])
print(angular_dist)

[*********************100%***********************]  2 of 2 completed
            IP.CN  NTAR.CN
Date                      
2020-03-02  0.055     1.62
2020-03-03  0.050     1.59
2020-03-04  0.040     1.57
2020-03-05  0.045     1.48
2020-03-06  0.040     1.32
...           ...      ...
2020-05-25  0.065     2.07
2020-05-26  0.065     2.20
2020-05-27  0.060     2.18
2020-05-28  0.065     2.07
2020-05-29  0.065     2.11

[63 rows x 2 columns]
0.8200543899391678
0.2742084582009295
0.2742084582009295
0.3729254632246639

!pip3 install mlfinlab yfinance

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