Custo computacional das otimizações de carteira usando CVXPY O problema de otimização de carteira tem sido um dos problemas mais estudados no gerenciamento de risco durante o século passado. Da pesquisa publicada por Markowitz na década de 1950 houve melhorias significativas na maneira como fazemos otimizações de variância média e os algoritmos usados para resolver o problema de otimização quadrática. Hoje eu quero compartilhar com você algum código para a descoberta do portfólio de variância mínima em python, bem como algumas comparações entre os diferentes algoritmos que temos disponíveis para o cálculo de tais carteiras. Para isso, vou estar usando a biblioteca CVXPY que é, sem dúvida, a melhor biblioteca para resolver este tipo de problema em python. Vamos passar um pouco pelo código e pelos resultados dos diferentes algoritmos disponíveis através da implementação do CVXPY. Função de otimização de otimização mínima Otimizar um portfólio é bastante simples: basta encontrar os pesos que você precisa dar a cada um dos sistemas, de modo que alguma propriedade do portfólio subjacente seja minimizada ou maximizada. Alguns desses procedimentos não podem ser facilmente resolvidos analiticamente e, portanto, requerem alguma forma de iteração 8211, como quando você está procurando o melhor histórico Sharpe 8211, mas outros tipos de otimizações, como a carteira de variância mínima pode realmente ser resolvido analiticamente usando a matriz de covariância. O código acima mostra como você pode obter o portfólio de variância mínimo dado um DataFrame pandas contendo todas as devoluções do sistema e um selectedsolver vindo da lista que você pode obter a partir do comando installedsolvers () CVXOPT). Como você pode ver a grande vantagem da biblioteca CVXPY é que o problema é expresso de uma forma que está mais perto do que você iria escrever em um pedaço de papel, se você estivesse explicando a matemática, em vez de na forma de matriz que é normalmente necessária Pelo uso direto das bibliotecas de otimização. O projeto CVXPY não implementa diretamente bibliotecas de otimização, mas serve apenas como um ótimo wrapper para permitir o uso de todas essas bibliotecas subjacentes com o mesmo formato de entrada e com um conjunto de instruções mais intuitivo. Por exemplo, neste caso, eu simplesmente dizer-lhe que eu quero minimizar o risco e eu quero a soma de pesos para ser 1 e todos eles a ser maior do que 0. Dado que podemos muito facilmente alternar entre otimizadores torna-se muito fácil de executar Benchmarks da otimização usando os diferentes solucionadores disponíveis. A imagem acima mostra a quantidade de tempo que leva em segundos para resolver o problema de variância mínima usando os retornos mensais de 30 anos de 500 estratégias de negociação com os 4 solucionadores diferentes que estão disponíveis após a instalação do CVXPY. Como você pode ver o solver SCS é de longe o melhor 8211 não surpreendentemente desde it8217s codificado em C 8211 com um desempenho que é quase 10x do algoritmo CVXOPT mais comumente usado. A precisão no SCS também não é pior com os Sharpes e os pesos obtidos pelo algoritmo SCS sendo basicamente iguais 8211, na maioria das vezes ainda melhores 8211 do que aqueles alcançados pelos outros tipos de algoritmo. As diferenças entre CVXOPT, ECOS e outras bibliotecas nativas de python é basicamente pequena, uma vez que não é possível remover a maior parte da sobrecarga, a menos que a maior parte do código seja executada usando uma linguagem não interpretada como C. Com o conhecimento de que o algoritmo SCS é o melhor Podemos agora avaliar o custo computacional de realizar otimizações de carteira usando diferentes números de sistema. Como você pode ver na imagem abaixo, o custo computacional aumenta como uma função de lei de potência à medida que o número de sistemas aumenta. No entanto, como você pode ver o ajuste é muito bom no início, mas a estimativa começa a se tornar pior como o número de sistemas vai além da marca de 2500 sistema. Isto é possivelmente porque as matrizes se tornam grandes o suficiente para transferências de dados eo cálculo da matriz de covariância para começar a adicionar significativamente ao custo de execução do programa global. À medida que o número de sistemas começa a vir muito maior do que o número de retornos começamos a obter alguns problemas importantes com o cálculo da matriz de covariância. No caso de 4000 sistemas usando 30 anos de retornos mensais temos 360 valores de retorno mas 4000 valores de sistema, começamos a ver essas instabilidades na matriz de covariância. Outro problema que discutimos antes é que a matriz de covariância de amostra 8211, a que derivamos dos retornos históricos 8211, está sujeita a ter muito erro de estimativa e, portanto, um desempenho mais baixo na amostra fora da amostra quando comparado com portfólios nativamente otimizados ( Por exemplo, carteiras de peso igual), especialmente quando há um elevado número de estratégias não correlacionadas. Há algumas maneiras que podemos tentar contornar isso, que também nos ajudam a reduzir os problemas que temos quando o número de estratégias é muito superior ao número de retornos de amostra. Alguns dos quais estaremos explorando no futuro posts. 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