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portfolio.py
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portfolio.py
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'''
program to analyze a stock portfolio over a period of one year
and compare it with the S&P 500
uses the pandas module
this program plots the portfolio value, cumulative returns, and computes
the Sharpe ratio, Jensen's alpha, beta, and R-squared values.
'''
import numpy as np, pandas as pd, pandas.io.data as web, matplotlib.pyplot as plt, datetime as dt
from pandas.stats.api import ols
def scatterplot(d1,d2,res):
fig = plt.figure()
#create axis and scatterplot our data
ax = fig.add_subplot(111)
ax.scatter(d1*100., d2*100.)
#prevent matplotlib from automatically changing axis ranges
ax.autoscale(False)
#add x = 0 and y = 0 lines
ax.vlines(0,-10,10)
ax.hlines(0,-10,10)
#add y = x and linear regression result
ax.plot((-10,10),(-10,10),label='y=x')
ax.plot((-10,10),(-10*res.beta['x']+100.*res.beta['intercept'],10*res.beta['x']+100.*res.beta['intercept']), color='r',label='linear regression')
ax.set_xlabel("S&P 500 Daily Returns (in %)")
ax.set_ylabel("Portfolio Daily Returns (in %)")
ax.legend()
plt.show()
def main():
# dictionary of stocks (stocks tickers and share numbers): this is just an example, not my actual portfolio :-)
tickers= { 'MBT':742,'T':326,'C':205,'BAC':781,'SAN':1409,'SCGLY':1320,'LFRGY':693,'CRRFY':1814,'MTNOY':505,'BACHY':914,'BCS':821,'SRGHY':738,'VCISY':758,'VIV':532}
end = dt.date.today() #today's date
start= end-dt.timedelta(days=365)
# create a pandas dataframe by reading data from the internet
stock={} #create a dictionary
tickers_names=tickers.keys()
for i in np.arange(len(tickers)):
temp=web.DataReader(tickers_names[i],data_source='yahoo',start=start,end=end)
stock[tickers_names[i]]=temp['Close']*tickers[tickers_names[i]]
SPX=web.DataReader('^GSPC',data_source='yahoo',start=start,end=end)
SPX=SPX['Close']
#convert my dictionary into a pandas dataframe:
stocks=pd.DataFrame(stock)
#compute the porfolio daily values
stocks=stocks.sum(axis=1)
stocks_dchange=stocks.pct_change() #daily returns (price_today-price_yesterday)/price_yesterday
stocks_change=(stocks_dchange+1).cumprod()-1 #multiple period returns (cumulative returns)
SPX_dchange=SPX.pct_change()
SPX_change=(SPX_dchange+1).cumprod()-1
#plot cumulative returns
plt.clf()
plt.subplot(211)
plt.title('Portfolio')
plt.ylabel('cumulative daily returns (%)')
plt.xlabel('Date')
plt.plot(stocks.index,stocks_change*100.,label='Portfolio')
plt.plot(SPX.index,SPX_change*100.,label='S&P 500')
plt.legend()
plt.subplot(212)
plt.ylabel('Portfolio daily value (USD)')
plt.xlabel('Date')
plt.plot(stocks.index,stocks,label='Portfolio')
plt.show()
#run a linear regression with ordinary least-squares
Rf=(1.+0.0025)**(1./252.)-1. #daily risk-free rate, from the 1-year treasury rate of 0.25%
print 'daily risk-free rate (in percentage)=%f' % (Rf*100.)
#linear regression from the risk-adjusted returns
res = ols(y=stocks_dchange-Rf, x=SPX_dchange-Rf)
#Sharpe ratio:
sharpe=(stocks_dchange-Rf).mean()/(stocks_dchange-Rf).std()
# returns the Jensen's alpha and beta (from daily returns)
print '---------------------------------------------------------------------'
print 'Jensen annualized alpha (in percentage)= %f' % (((1.+res.beta['intercept'])**252-1.)*100.)
print 'portfolio beta= %f' % res.beta['x']
print 'portfolio R-squared= %f' % res.r2
print 'adjusted R-squared= %f' % res.r2_adj
print 'Sharpe ratio= %f' % (sharpe*np.sqrt(252)) # annualized Sharpe ratio
print 'Maximum portfolio value= %f' % stocks.max()
print 'Minimum portfolio value= %f' % stocks.min()
print '---------------------------------------------------------------------'
#scatter pot of daily returns
scatterplot(SPX_dchange,stocks_dchange,res)
if __name__ == "__main__":
main()