Python for Finance

2. Code for defining two functions:
Chapter 9
def ret_monthly(ticker): # function 1
x = getData(ticker,(begYear,1,1),(endYear,12,31),asobject=True
,adjusted=True)
logret=np.log(x.aclose[1:]/x.aclose[:-1])
date=[]
d0=x.date
for i in range(0,np.size(logret)):
date.append(''.join([d0[i].strftime("%Y"),d0[i].
strftime("%m")]))
y=pd.DataFrame(logret,date,columns=[ticker])
return y.groupby(y.index).sum()
# function 2: objective function
def objFunction(W, R, target_ret):
stock_mean=np.mean(R,axis=0)
port_mean=np.dot(W,stock_mean)
# portfolio mean
cov=np.cov(R.T)
# var-cov matrix
port_var=np.dot(np.dot(W,cov),W.T) # portfolio variance
penalty = 2000*abs(port_mean-target_ret)# penalty 4 deviation
return np.sqrt(port_var) + penalty # objective function
3. Code for generating a return matrix R:
R0=ret_monthly(stocks[0])
# starting from 1st
stock
n_stock=len(stocks)
# number of stocks
for i in xrange(1,n_stock):
# merge with other
stocks
x=ret_monthly(stocks[i])
R0=pd.merge(R0,x,left_index=True,right_index=True)
R=np.array(R0)
4. Code for estimating optimal portfolios for a given return:
out_mean,out_std,out_weight=[],[],[]
stockMean=np.mean(R,axis=0)
for r in np.linspace(np.min(stockMean),np.max(stockMean),num=100):
W = np.ones([n_stock])/n_stock # starting from equal
weights
b_ = [(0,1)
for i in range(n_stock)]
# bounds, here no short
c_ = ({'type':'eq', 'fun': lambda W: sum(W)-1. })#constraint
result=sp.optimize.minimize(objFunction,W,(R,r),method='SLSQP'
,constraints=c_, bounds=b_)
if not result.success:
# handle error raise
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