In this post, we'll a guide to knowing about portfolio optimization and implementing it through the Python language.
Portfolio optimization is the process of choosing the best portfolio among the set of all portfolios.
The naive way is to select a group of random allocations and figure out which one has the best Sharpe Ratio. This is known as the Monte Carlo Simulation where randomly a weight is assigned to each security in the portfolio and then the mean daily return and standard deviation of daily return is calculated. This helps in calculating the Sharpe Ratio for randomly selected allocations.
To know more about Sharpe Ratio, check out my previous article:
But the naive way is time taking so an optimization algorithm is used which works on the concept of the minimizer. The higher the Sharpe Ratio, the higher is the risk-adjusted return and the better the portfolio selection. So the intuition is to maximize the Sharpe Ratio meaning that the optimizer should minimize the negative Sharpe Ratio. The optimization algorithm will allocate optimal weights to the portfolio on the basis of the Sharpe Ratio.
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