Python for Finance

Portfolio Theory
output=pd.DataFrame(ret,index=ddate[1:])
output.columns=[ticker]
return output
ret=ret_f('IBM')
print(ret.head())
IBM
DATE
19620228 -0.008475
19620330 -0.008547
19620430 -0.146552
19620531 -0.136364
19620629 -0.134503
Finally, we could construct an n-stock return matrix from yanMonthly.pkl:
import scipy as sp
import pandas as pd
import numpy as np
n_stocks=10
x=pd.read_pickle('c:/temp/yanMonthly.pkl')
x2=sp.unique(np.array(x.index))
x3=x2[x2<'ZZZZ']
# remove all indices
sp.random.seed(1234567)
nonStocks=['GOLDPRICE','HML','SMB','Mkt_Rf','Rf','Russ3000E_D','US_
DEBT','Russ3000E_X','US_GDP2009dollar','US_GDP2013dollar']
x4=list(x3)
for i in range(len(nonStocks)):
x4.remove(nonStocks[i])
k=sp.random.uniform(low=1,high=len(x4),size=n_stocks)
y,s=[],[]
for i in range(n_stocks):
index=int(k[i])
y.append(index)
s.append(x4[index])
final=sp.unique(y)
print(s)
def ret_f(ticker):
a=x[x.index==ticker]
p=sp.array(a['VALUE'])
ddate=a['DATE']
ret=p[1:]/p[:-1]-1
output=pd.DataFrame(ret,index=ddate[1:])
[ 304 ]