Analytics#2.py

import pandas as pd
import numpy as np

class StockPrices:
    # param prices dict of string to list. A dictionary containing the tickers of the stocks, and each tickers daily prices.
    # returns list of strings. A list containing the tickers of the two most correlated stocks.
    @staticmethod
    def most_corr(prices):
        data=pd.DataFrame.from_dict(prices)
        au_corr=data.corr()
        listku=au_corr.values.flatten()
        listku=np.unique(listku)
        #dengan cara ini juga bisa sih
        #arrayidx=[]
        #arrayvalues=[]
        #for idx, values in au_corr.stack().iteritems():
        #    arrayidx.append(idx)
         #   arrayvalues.append(values)
        #for i in range(len(arrayvalues)):
         #   if (arrayvalues[i]==sorted(listku)[-2]):
          #      indices=arrayidx[i]
        indices = np.where(au_corr > sorted(listku)[-3])
        indices = [(au_corr.index[x], au_corr.columns[y]) for x, y in zip(*indices) if x != y and x < y]
        return indices

#For example, with the parameters below the function should return ['FB', 'MSFT'].
prices = {
    '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
    ]
}

print(StockPrices.most_corr(prices))