Correlation



In [1]:

    
import pandas as pd
import matplotlib.pyplot as plt
import datetime
import numpy as np

import pinkfish as pf

# format price data
pd.options.display.float_format = '{:0.2f}'.format

%matplotlib inline



In [2]:

    
# set size of inline plots
'''note: rcParams can't be in same cell as import matplotlib
   or %matplotlib inline
   
   %matplotlib notebook: will lead to interactive plots embedded within
   the notebook, you can zoom and resize the figure
   
   %matplotlib inline: only draw static images in the notebook
'''
plt.rcParams["figure.figsize"] = (10, 7)



In [3]:

    
#symbols = ['SPY', 'GLD', 'BOND', 'EEM', 'IYR', 
#           'JNK', 'LQD', 'NLY',  'PFF', 'PGX',
#           'SLY', 'TIP', 'TLT',  'BSV', 'VNQ']
symbols = ['GLD', 'GDX']

#symbols = ['SPY', '^GSPC']
start = datetime.datetime(1900, 1, 1)
end = datetime.datetime.now()



In [4]:

    
# fetch timeseries
portfolio = pf.Portfolio()
ts = portfolio.fetch_timeseries(symbols, start, end, fields=['close'])



In [5]:

    
ts, start = portfolio.finalize_timeseries(ts, start)



In [6]:

    
corr_df = portfolio.correlation_map(ts)
corr_df