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PyPortOptimization: una tubería de optimización de cartera que aprovecha múltiples métodos de rendimiento esperado,

Rushikesh Nakhate1, Harikrishnan Ramachandran1, Amay Mahajan2

  • 1Symbiosis Institute of Technology (SIT), Pune Campus, Symbiosis International Deemed University (SIDU), Pune, 412115, India.

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PyPortOptimization es una nueva biblioteca para la optimización automatizada de carteras, que ofrece métodos flexibles para construir carteras de inversión robustas y de alto rendimiento. Permite desarrollar canalizaciones personalizadas e incluye simulaciones de Monte Carlo para la evaluación de riesgos.

Palabras clave:
Simulación de MontecarloOptimización de la carteraPyPortfolioOpt (en inglés)En el caso de los productos derivadosEjecutar el proceso de optimización

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Área de la Ciencia:

  • Finanzas computacionales
  • Las finanzas cuantitativas
  • Ingeniería financiera

Sus antecedentes:

  • La optimización tradicional de la cartera se enfrenta a desafíos en cuanto a flexibilidad y escalabilidad.
  • La integración de diversas metodologías para los rendimientos esperados, el modelado de riesgos y la optimización es compleja.

Objetivo del estudio:

  • Introducir PyPortOptimization, una biblioteca automatizada para la construcción de carteras flexibles y escalables.
  • Permitir a los usuarios personalizar cada etapa del flujo de optimización de la cartera.
  • Comparar varios métodos para los rendimientos esperados, el modelado de riesgos y las técnicas de optimización.

Principales métodos:

  • Desarrollo de una biblioteca de optimización de cartera automatizada (PyPortOptimization).
  • Soporte para varias matrices de riesgo-rendimiento, matrices de covarianza/correlación y algoritmos de optimización.
  • Integración de las simulaciones de Monte Carlo para la evaluación de la solidez de la cartera.
  • Implementación de un sistema de caché para optimizar el tiempo de ejecución.

Principales resultados:

  • El método de asignación diseñado a medida demostró un rendimiento superior, superando el índice de Sharpe del asignador proporcional.
  • PyPortOptimization comparó con éxito diversas configuraciones para los pasos de optimización de la cartera.
  • La biblioteca proporciona una solución flexible y escalable para la construcción de carteras.

Conclusiones:

  • PyPortOptimization ofrece una herramienta versátil y eficiente para los profesionales de las finanzas cuantitativas.
  • La biblioteca facilita flujos de trabajo de optimización de cartera personalizados con una evaluación de rendimiento robusta.
  • Las bibliotecas automatizadas como PyPortOptimization mejoran la eficiencia y la eficacia del desarrollo de estrategias de inversión.