In this project, I use what I learned about optimizers to optimize a portfolio. I will find how much of a portfolio's funds should be allocated to each stock so as to optimize its performance. While we can optimize for many different metrics, in this project we will maximize Sharpe 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 represents the allocations to each of the equities. The following inputs are used for optimizing a portfolio:
A date range to select the historical data to use (specified by a start and end date) Symbols for equities (e.g., GOOG, AAPL, GLD, XOM).
The goal is to find allocations to the symbols that optimize the criteria given above. Assumptions: 252 trading days in a year and a risk-free return of 0.0 per day.
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