Smart portfolio using quantitative investment models

In the past few decades, the identification of state variables and parameters of a model from measured data has increased dramatically. This widespread growth has created a growing need for integrated models. Achieving sustained and long-term economic growth requires optimal resource allocation, and this is not possible without the use of financial markets, especially efficient capital markets, so portfolio optimization and wealth allocation between different assets are among the most important issues in investing. In this research, in order to implement smart financial portfolio, it is tried to improve the existing optimization methods based on Sharp Ratio performance and to present an intelligent method for trading based on different algorithms. For this purpose, first, create a quantitative investment model using momentum algorithm and long-term investment model over a 6-year time horizon using monthly stock exchange data and then a set of smart models (general functions, general average and The general algorithm (developed by Kalman filter), which calculates the amount of capital using smart patterns to maximize return and avert negative return on equity investments and optimize capital investing to make the proposed structure perform better than other algorithms. Conventional and can fit and alternative approaches to achieve better results finally, the results indicate that the proposed model is effective and efficient.