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<\/span><\/p>\n <\/p>\n This function should find the optimal allocations for a given set of stocks. You should optimize for maximum Sharpe tuple<\/p>\n<\/dd>\n<\/dl>\n<\/dd>\n<\/dl>\n This function WILL NOT be called by the auto grader.<\/p>\n<\/dd>\n<\/dl>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n <\/p>\n <\/p>\noptimization.py<\/span><\/h2>\n
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optimize_portfolio<\/code>(<\/span>sd=datetime.datetime(2008, 1, 1, 0, 0), ed=datetime.datetime(2009, 1, 1, 0, 0), syms=[‘GOOG’, ‘AAPL’, ‘GLD’, ‘XOM’], gen_plot=False<\/em>)<\/span><\/dt>\n
Ratio. The function should accept as input a list of symbols as well as start and end dates and return a list of
floats (as a one-dimensional NumPy array) that represent the allocations to each of the equities. You can take
advantage of routines developed in the optional assess portfolio project to compute daily portfolio value and
statistics.<\/p>\n\n
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symbol in the data directory)<\/li>\n
code with gen_plot = False.<\/li>\n<\/ul>\n<\/dd>\n
standard deviation of daily returns, and Sharpe ratio<\/dd>\n\n
test_code<\/code>(<\/span>)<\/span><\/dt>\n