| import pandas as pd | |
| import numpy as np | |
| import datetime as dt | |
| import pandas_datareader as pdr | |
| # Read in Stock csv data and convert to have each Ticker as a column. | |
| #df = pd.read_csv('us-shareprices-daily.csv', sep=';') | |
| #stocks = df.pivot(index="Date", columns="Ticker", values="Adj. Close") | |
| #logRet = np.log(stocks/stocks.shift()) | |
| # Calculate the Correlation Coefficient for all Stocks | |
| #stocksCorr = logRet.corr() | |
| # Output to csv | |
| #stocksCorr.to_csv (r'correlation_matrix.csv', index = None, header=True) | |
| # Enter path of SimFin Data to convert to format for Calculations | |
| def convert_simFin(path): | |
| df = pd.read_csv(path, sep=';') | |
| stocks = df.pivot(index="Date", columns="Ticker", values="Adj. Close") | |
| return stocks | |
| # Calculate Log returns of the Formatted Stocks | |
| def log_of_returns(stocks): | |
| log_returns = np.log(stocks/stocks.shift()) | |
| return log_returns | |
| # Enter Log returns of Stocks to Calculate the Correlation Matrix. | |
| def correlation_matrix(lr): | |
| return lr.corr() | |