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Investment Portfolio Optimization Using Technical Indicators and White-Box Models
(IEEE, 2024-12-04)
Caro Reyna Luis Fernando
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Arcos Bravo David Gamaliel
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Quantitative trading has revolutionized in recent years with the integration of machine learning. However, most proposals are complex models that often need help with model understanding and feature importance identification. This study presents a methodology for optimizing investment portfolios using the XGBoost algorithm and a comprehensive set of technical indicators. The primary objective is to maximize returns by accurately predicting stock prices and selecting the most profitable stocks. Our proposal is based on decision trees, eliminating the need for recurrent neural networks or time series representations of data and enabling white-box machine learning models that are easier to interpret. We tried our proposal with real data corresponding to a collection of stocks of the 500 most influential companies in the United States of America, utilizing historical data such as open prices, highest and lowest prices, and trading volume. Experimental results demonstrated that our approach successfully identified the most profitable stocks, outperforming random portfolios and showing significant profit accumulation over time. This approach recognizes the most feasible indicators and facilitates the automatic design of investment portfolios and the analysis of the importance of technical indicators in complex data.
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Numerical Analysis of Machining Part Distortion in Aircraft Aluminum Structures
(ASME, 2020)
Ledesma-Orozco, Elías
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The inherent residual stresses in the raw materials of large monolithic structural components whereby machining procedures are needed to produce aircraft components, cause deviations, and distortions that are undesired and rise challenges for engineering design and engineering production teams of the aerospace companies. A numerical approach to address part distortion is proposed in this paper. An algorithm was developed and implemented as a finite element subroutine in the software ANSYS APDL, which uses the raw inherent residual stress parameters of the aluminum alloy and the machining locations of a structural specimen to simulate the machining distortion phenomenon in aircraft aluminum structures. This algorithm uses as inputs the finite element mesh of a component, the coefficients of residual stresses functions, and the machining location parameters from where a part is made of a raw material blank. The numerical results predicted the part distortion phenomenon with an Absolute Error of 2.79% with respect to initial experimental measurements of part distortion. Additionally, the proposed approach was used to develop part distortion curves by considering the machining location of the specimen. From these, numerical optimization techniques led to determine the machining location of the representative specimen that attained lower distortions. Such location corresponded to a vertical value around of 3.15 mm for the two simulated residual stresses conditions in the material. An additional measurement was carried out to validate the optimal numerical results and errors below 3% were obtained. Consequently, the proposed approach can be of use to determine, to reduce and to optimize part distortion without further experimental testing in structural aluminum 7050-T7451 alloy aircraft components.
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An Alternative Method for the Optimum Dynamic Balancing of the Four-Bar Mechanism
(2014)
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Haro-Sandoval, Eduardo
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This article presents the optimum dynamic balancing of the four-bar mechanism, in particular the crank-rocker, by the addition of counterweights. This is done by imposing as little restrictive as possible constraints on the counterweights parameters. First the general analytical equations of motion of the crank-rocker four-bar mechanism are obtained, using natural coordinates. This model allows expressing the dynamic equations of the mechanism just in terms of the mass, as opposed to the need of using also the moment of inertia, and the coordinates of the center of gravity of the counterweights, that are used as optimization variables. This implies that no particular counterweight shape is assumed in advance. The only constraints imposed on these optimization variables are that masses must be non-negative. As a novelty, the most influencing variables in the optimization are identified using a global sensitivity analysis based on polynomial chaos. This allows to impose different constraints an also to reduce the total number of optimization variables without affecting the global results. The results obtained are validated by simulations, and compared to those expressed in representative papers obtained by other authors. © Springer Nature
Scopus© Citations 1 12 1
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Multi-threaded Spotted Hyena Optimizer with thread-crossing techniques
(2021)
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Murillo-Suarez, Alfonso
This paper presents a Multi-threaded version of the Spotted Hyena Optimizer algorithm with thread-crossing techniques (MT-SHO) to improve the ability of the algorithm to explore the search space. The original algorithm is inspired by the hunting behavior of the spotted hyena. Along the different sections of the work, we explain in detail how the original algorithm simulates the spotted hyena's behavior to optimize highly complex mathematical functions and how we handle the procedures and results of the multi-threaded version, with thread-crossing techniques that improve the ability to explore and exploit the search space by letting threads learn between them. We present the experiments used to determine the best value of the parameters used in the parallel version of the algorithm and to prove that our proposal obtains significantly good results we compare the results obtained by evaluating 24 benchmark functions with the results published for the original algorithm as well as other metaheuristic algorithms. © 2021 Elsevier B.V.. All rights reserved.
Scopus© Citations 3 33 1
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MTGWA: A Multithreaded Gray Wolf Algorithm with Strategies Based on Simulated Annealing and Genetic Algorithms
(2021)
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Murillo-Suarez, Alfonso
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García-Jacas, Cesar Raúl
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Guerrero-Valadez, Juan Manuel
In this paper, we present an improvement of the Gray Wolf algorithm (GWO) based on a multi-threaded implementation of the original algorithm. The paper demonstrates how to combine the solutions obtained in each of the threads to achieve a final solution closer to the absolute minimum or even equal to it. To properly combine the solutions of each of the threads of execution, we use strategies based on simulated annealing and genetic algorithms. Also, we show the results obtained for twenty-nine functions: unimodal, multimodal, fixed dimension and composite functions. Experiments show that our proposed improves the results of the original algorithm. © Springer Nature
Scopus© Citations 1 30 2
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Review on recent implementations of multiobjective and multilevel optimization in sustainable energy economics
(2022)
Ganesan, Timothy
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Litvinchev, Igor
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Marmolejo Saucedo, José Antonio
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Thomas, J. Joshua
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Vasant, Pandian
Rapid progress is currently being made globally in the sustainable energy industry. This trend has been seen to concentrate on specific focus areas in the global energy ecosystem. The integration of sustainability ideas into the existing energy ecosystem has given rise to various complexities, e.g., multilevel and multiobjective (MO) scenarios. This in return has generated various avenues for the implementation of mathematical optimization as well as state-of-the-art operations research methodologies on such real-world systems. This chapter aims to provide a concise review on recent implementations of MO and multilevel optimization on sustainable energy economic systems. Three key industrial areas are given emphasis—economic load/emission dispatch, bioenergy supply chains, and sustainable capacity planning. © 2022 Elsevier Inc. All rights reserved.
Scopus© Citations 1 18 1
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A new heuristic algorithm to solve Circle Packing problem inspired by nanoscale electromagnetic fields and gravitational effects
(2018)
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Marmolejo Saucedo, José Antonio
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Murillo-Suarez, Alfonso
In this paper, we present a new algorithm for the fast and efficient solution of the Packing problem in two dimensions. The packing problem consists in finding the best arrangement of objects (many geometrical forms) in a specific space called container.This new algorithm is inspired by the observations of nanometric scale electromagnetic fields. We use the electromagnetic theory of the electric field to calculate the best position to place a circular object in a configuration of other circular objects previously packing. Also, in this new algorithm we simulate two processes called »gravity» and »shaken» that compact the distribution of the objects placed in the container and allow to minimize the unoccupied space. © 2018 IEEE.
Scopus© Citations 3 11 1
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Intelligent Control Navigation Emerging on Multiple Mobile Robots Applying Social Wound Treatment
(2019)
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C. Souza, Paulo Vitor
In robotics, learning new tasks is a complex solving problem. This learning depends on the environment, the robot configuration, the difficulty of the problem task, even the prior knowledge. Reinforcement learning has been widely employed for learning from scratch and policy search; however, it is very time-consuming. Multi-robots, as collaborative learners, have been proposed to improve the speed of learning in robotics. In this paper, we propose a collaborative intelligent control navigation strategy in robots, including a social wound treatment approach, such that robots can jointly learn how to avoid obstacles and move freely around the environment. This collective learning about social treatment aims to detect unexpected or inefficient behaviors of the robots, allowing them to redirect the right tasks with more agility, as observed in some animals. Experimental results over a multiple homogeneous robot system simulation validated our proposal. © 2019 IEEE.
Scopus© Citations 2 11 2
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Rain-Fall Optimization Algorithm with new parallel implementations
(2018)
Guerrero-Valadez, Juan Manuel
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Rainfall Optimization Algorithm (RFO) is a nature-inspired metaheuristic optimization algorithm. RFO mimics the movement of water drops generated during rainfall to optimize a function. The paper study new implementations for RFO to offer more reliable results. Moreover, it studies three restarting techniques that can be applied to the algorithm with multithreading. The different implementations for the RFO are benchmarked to test and verify the performance and accuracy of the solutions. The paper presents and compares the results using several multidimensional testing functions, as well as the visual behavior of the raindrops inside the benchmark functions. The results confirm that the movement of the artificial drops corresponds to the natural behavior of raindrops. The results also show the effectiveness of this behavior to minimize an optimization function and the advantages of parallel computing restarting techniques to improve the quality of the solutions.
Scopus© Citations 1 4 2
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A new heuristic with a multi-threaded implementation of a modified Firefly Algorithm
(2018)
Murillo-Suárez, Alfonso
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In this article, we present a modified version of the Firefly Algorithm implemented in a multi-threaded model to improve the results obtained by the original algorithm significantly. This multi-threaded algorithm allows the threads to obtain different results by the independent execution of the heuristic method in each of them, although for keeping all the threads with significant executions, the algorithm performs some crossover techniques, explained in detail in this article, for the threads to learn between them while maintaining its independence. For testing the new algorithm, we use the six benchmark functions used in the literature for testing the original Firefly Algorithm, and to prove that the improved results are significant, we perform the Wilcoxon test to the results obtained. The results obtained with this new heuristic proved to be significantly better while taking advantage of today's commercial processors. © 2020 Alfonso Murillo-Suarez et al., licensed to EAI.
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