Python for Finance

ticker='IBM'
begdate=(2013,1,1)
enddate=(2013,11,9)
#
x = getData(ticker, begdate, enddate,asobject=True, adjusted=True)
logret = np.log(x.aclose[1:]/x.aclose[:-1])
yyyymm=[]
d0=x.date
#
for i in range(0,np.size(logret)):
yyyymm.append(''.join([d0[i].strftime("%Y"),d0[i].
strftime("%m")]))
y=pd.DataFrame(logret,yyyymm,columns=['retMonthly'])
retMonthly=y.groupby(y.index).sum()
print(retMonthly.head())
The output is shown here:
Chapter 8
Merging datasets by date
The following program merges the daily adjusted closing price of IBM with the
daily Fama-French 3-factor time-series. The ffMonthly.pkl is available at:
http://canisius.edu/~yany/python/ffDaily.pkl:
from matplotlib.finance import quotes_historical_yahoo_ochl as getData
import numpy as np
import pandas as pd
ticker='IBM'
begdate=(2016,1,2)
enddate=(2017,1,9)
x =getData(ticker, begdate, enddate,asobject=True, adjusted=True)
myName=ticker+'_adjClose'
x2=pd.DataFrame(x['aclose'],x.date,columns=[myName])
ff=pd.read_pickle('c:/temp/ffDaily.pkl')
final=pd.merge(x2,ff,left_index=True,right_index=True)
print(final.head())
[ 253 ]