Portfolio optimization problem models have typically adopted solvers such as SciPy in the Python arena, that employs traditional methods to obtain the optimal portfolios. However, despite their sophistication, traditional methods suffer from pitfalls that can stifle its ability to handle complex problem models or yield efficient solutions.
Heuristic solvers inspired by nature or artificial intelligence based methods, have quite often risen to the occasion by yielding near-optimal or acceptable solutions to such complex problem models, which traditional methods found difficult to handle. Again, for the less difficult models, heuristic solvers have exhibited the potential to yield efficient or practically prudent solutions when compared to those obtained by traditional methods. VitaOptimum Plus is a Python based non-traditional solver, that employs cutting edge technologies of Artificial Intelligence, Evolutionary Computation, Swarm Intelligence and Advanced Statistics, to arrive at the global optimum.
This post investigates the potential of the traditional and heuristic solvers viz., optimize of SciPy and Ccs of VitaOptimum Plus respectively, to solve the Sharpe Ratio based Portfolio Optimization model.