def return_sr(weights, data):
"""
Telle me what the
In this simplified version we assume all assets have the same volatility and mean returns
This means we only need to use the correlation matrix instead of the covariance
And a dull vector of returns
You can change this if required....
"""
sigma=correlation_matrix(data)
default_SR=1.0
mus=np.array([(sigma.diagonal()[idx]**.5)*default_SR/16.0 for idx in range(len(data.columns))], ndmin=2).transpose()
return -neg_SR(weights, sigma,mus)
if __name__=="__main__":
## Get the data
"""
This is a trivial example, 3 possible assets, portfolio of 2
"""
filename="assetprices.csv"
data=pd_readcsv(filename)
asset_names=data.columns
assets_to_add=2
cmat=correlation_matrix(data)
instrument_index=instrument_index_list(data.shape[1])
first_index=find_most_diversifying_asset(cmat)
instrument_index.add(first_index)
print "Started with %s" % asset_names[first_index]
while len(instrument_index.in_portfolio)<assets_to_add:
current_portfolio=instrument_index.in_portfolio
candidates=instrument_index.not_in_portfolio
new_addition=find_best_addition(data, candidates, current_portfolio)
print "Adding %s" % asset_names[new_addition]
instrument_index.add(new_addition)
print "Selected portfolio:"
