Dr G A Vijayalakshmi Pai's repositories
PythonFinance-PortfolioOptimization
Python for Portfolio Optimization: The Ascent! First working lessons to ascend the hilly terrain of Portfolio Optimization in seven strides (Lessons), beginning with the fundamentals (Lesson 1) and climbing slope after slope (Lessons 2-6), to reach the first peak of constrained portfolio optimization models (Lesson 7), amongst a range of peaks waiting beyond!
PythonFinance--Portfolio-Rebalancing-using-Metaheuristics
Rebalancing a portfolio with optimal buy/sell decisions using Metaheuristics
PythonFinance--RiskBudgeted-Portfolio-Construction
Construction of optimal Risk-budgeted and leveraged long-short portfolios using a metaheuristic strategy.y
PythonFinance--130-30-Portfolio-Construction
Construction of optimal 130-30 portfolios using Metaheuristics
PythonFinance--Global-optimization-of-portfolios-a-rendezvous-with-VitaOptimum-Plus
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 adopting artificial intelligence methods, have quite often risen to the occasion by yielding near-optimal or acceptable solutions to those complex problem models, which traditional methods found difficult to handle, or even yielding efficient or practically prudent solutions when compared to those obtained by traditional methods for less difficult models. 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
PythonFinance-Constrained-Bi-objective-Portfolio-Optimization-using-VitaOptimumPlus
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 a Bi-objective, constrained portfolio optimization problem.