TestDome-DataScience

StockPrices.py

import pandas as pd
import numpy as np

def most_corr(prices):
    """
    :param prices: (pandas.DataFrame) A dataframe containing each ticker's 
                   daily closing prices.
    :returns: (container of strings) A container, containing the two tickers that 
              are the most highly (linearly) correlated by daily percentage change.
    """
    s = prices.pct_change().corr().unstack()
    s = s[s!=1]
    return s.idxmax()

#For example, the code below should print: ('FB', 'MSFT')
print(most_corr(pd.DataFrame.from_dict({
    'GOOG' : [
        742.66, 738.40, 738.22, 741.16,
        739.98, 747.28, 746.22, 741.80,
        745.33, 741.29, 742.83, 750.50
    ],
    'FB' : [
        108.40, 107.92, 109.64, 112.22,
        109.57, 113.82, 114.03, 112.24,
        114.68, 112.92, 113.28, 115.40
    ],
    'MSFT' : [
        55.40, 54.63, 54.98, 55.88,
        54.12, 59.16, 58.14, 55.97,
        61.20, 57.14, 56.62, 59.25
    ],
    'AAPL' : [
        106.00, 104.66, 104.87, 105.69,
        104.22, 110.16, 109.84, 108.86,
        110.14, 107.66, 108.08, 109.90
    ]
})))