Python for Finance

output.columns=[ticker]
return output
final=ret_f(s[0])
for i in sp.arange(1,n_stocks):
ret=ret_f(s[i])
final=pd.merge(final,ret,left_index=True, right_index=True)
Chapter 9
To randomly choose m stocks from a set of existing available stocks (n of them), see
the command of scipy.random.uniform(low=1,high=len(x4),size=n_stocks).
Since n_stocks has a value of 10, we choose 10 stocks from len(x4). The output is
shown here:
IO A AA KB DELL IN
\
DATE
20110930 -0.330976 -0.152402 -0.252006 -0.206395 -0.048679 -0.115332
20111031 0.610994 0.185993 0.124464 0.192002 0.117690 0.237730
20111130 -0.237533 0.011535 -0.066794 -0.106274 -0.002616 -0.090458
20111230 0.055077 -0.068422 -0.135992 -0.102006 -0.072131 -0.065395
20120131 0.212072 0.215972 0.173964 0.209317 0.178092 0.230321
INF IBM SKK BC
DATE
20110930 -0.228456 0.017222 0.227586 -0.116382
20111031 0.142429 0.055822 -0.305243 0.257695
20111130 -0.038058 0.022314 -0.022372 0.057484
20111230 0.059345 -0.021882 -0.024262 -0.030140
20120131 0.079202 0.047379 -0.142131 0.182020
In finance, constructing an efficient frontier is always a challenging job. This is
especially true with real-world data. In this section, we discuss the estimation of a
variance-covariance matrix and its optimization, finding an optimal portfolio, and
constructing an efficient frontier with stock data downloaded from Yahoo! Finance.
When a return matrix is given, we could estimate its variance-covariance matrix. For
a given set of weights, we could further estimate the portfolio variance. The formula
to estimate the variance and standard deviation for returns from a single stock are
given as follows:
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