Portfolio modeling for an algorithmic trading based on control theory

In the present paper, a mathematical model for a portfolio is proposed. This model is valid for operations of buying and selling shares of an asset in constant periods of time, additionally, it has a states space form which can be used to design a control law using control theory. The control law designed can be interpreted as a trading signal to reach a portfolio value desired. The mathematical model and control law proposed are validated by means simulations using real daily prices of Mexican stock exchange

Genetic optimization of a trading algorithm based on pattern recognition

In the present paper, a trading strategy based onpattern recognition is optimized by means of a genetic algorithm.The genetic algorithm is used to determine decisions of buy/sellbased on the patterns found through time for a portfolio in thestock market. The predominant algorithms used in this workwere theK-means clustering algorithm to find the patterns indifferent time lapses, and the genetic algorithm for optimization.The results are supported by simulations using a selected sharesof the Mexican stock market.ITESO, A.C

Investment portfolio trading based on Markov chain and fuzzy logic

In the present paper, a trading strategy is proposed for a portfolio composed of shares in the stock exchange. The proposed strategy is based mainly on three blocks: 1) a K-means clustering algorithm is used to determine and learn the internal hidden patterns in the time series of stock market prices, 2) a pattern predictor is performed based on a simple Markov chain, and 3) a fuzzy inference system take the decision to trade based on the estimation. The fuzzy inference system is composed of the rules provided by an expert trader. The performance of the trading algorithm is validated through simulations using real prices of the Mexican stock exchange

Optimization of Seebeck nanoantenna-based infrared harvesters

In this letter, the authors develop an optimized Seebeck nanoantenna design suitablefor IR harvesting applications. The design is optimized via the so-called particle-swarm-optimization algorithm (PSO), an evolutionary algorithm able to drive the morphology of anano-object towards an optimum. Along with the so-called nanoloading technique, efforts aresubsequently addressed to understand the physical mechanisms behind the wave energy to voltageconversion, from both numerical and theoretical perspectives. In particular, the thermal andintrinsic impedance are considered to be the key issues beneath the device’s response.ITESO, A.C

Clustering subsecuencial de series de tiempo: Evidencia de patrones temporales en el tipo de cambio UsdMxn

Este trabajo es sobre Clustering SubSecuencial de Series de Tiempo, una técnica que busca agrupar subsecuencias contenidas dentro de una misma serie de tiempo, por medio del cálculo de un término de distancia euclidiana a manera de medida de similitud entre los datos. Hacemos uso del algoritmo MASS (Mueen's Algorithm for Similarity Search), para la identificación de patrones temporales en la subsecuencia de precios intradía del tipo de cambio Dólar americano Vs Peso Mexicano (UsdMxn). Una búsqueda quasi-exhaustiva de evidencia es conducida utilizando 10 años de información, 14.5 Millones de precios (OHLC de cada minuto), 36,000 mediciones de indicadores macroeconómicos. Los resultados que mostramos son consistentes y documentamos las condiciones bajo las cuales no se cumple la Hipótesis del Mercado Eficiente.ITESO, A.C

A simple recurrent neural network for solution of linear programming: Application to a Microgrid

The aim of this paper is to present a simple new class of recurrent neural networks, which solves linear programming. It is considered as a sliding mode control problem, where the network structure is based on the Karush-Kuhn-Tucker (KKT) optimality conditions, and the KKT multipliers are the control inputs to be implemented with finite time stabilizing terms based on the unit control, instead of common used activation functions. Thus, the main feature of the proposed network is the fixed number of parameters despite of the optimization problem dimension, which means, the network can be easily scaled from a small to a higher dimension problem. The applicability of the proposed scheme is tested on real-time optimization of an electrical Microgrid prototype.Consejo Nacional de Ciencia y Tecnologí

A Fixed Time Convergent Dynamical System to Solve Linear Programming

The aim of this paper is to present a new dynamical system which solves linear programming. Its design is considered as a sliding mode control problem, where its structure is based on the Karush-Kuhn-Tucker optimality conditions, and its multipliers are the control inputs to be implemented by using fixed time stabilizing terms with vectorial structure, based on the unit control, instead of common terms used in other approaches. Thus, the main features of the proposed system are the fixed convergence time to the programming solution and the fixed parameters number despite of the optimization problem dimension. That is, there is a time independent to the initial conditions in which the system converges to the solution and, the proposed structure can be easily scaled from a small to a higher dimension problem. The applicability of the proposed scheme is tested on real-time optimization of an electrical Microgrid prototype.Consejo Nacional de Ciencia y Tecnologí

A recurrent neural network for real time electrical microgrid prototype optimization

The aim of this paper is to present a new class of recurrent neural networks, which solve linear programming. It is considered as a sliding mode control problem, where the network structure is based on the Karush-Kuhn-Tucker (KKT) optimality conditions, and the KKT multipliers are the control inputs to be implemented with fixed time stabilizing terms, instead of common used activation functions. Thus, the main feature of the proposed network is its fixed convergence time to the solution, which means, there it is a time independent to the initial conditions in which the network converges to the optimization solution. The applicability of the proposed scheme is tested on real-time optimization of an electrical microgrid prototype.Consejo Nacional de Ciencia y Tecnologí

Optimal Power Dispatch in a Microgrid

This paper is concerned with the power dispatch in a microgrid. The dispatch problem is formulated as linear program. Thus, the proposed solution is the application of neural network that solves linear programming on-line. This proposal in motivated by the increasing electric energy demand and the rising need to incorporate sustainable energy sources to the power grid in a reliable scheme. A microgrid is an interconnection of distributed energy sources (DES), with the tendency to include renewable energies that offer many advantages to customers and utilities. The different DES that compose the microgrid are controlled independently to track the optimal reference provided by the proposed method in order to supply a demanded power output minimizing the consumed power from the main grid.ITESO, A.C.CINVESTAV-IP

Particle swarm optimization of nanoantennabased infrared detectors

The multi-resonant response of three-steps tapered dipole nano-antennas, coupled to a resistive and fast micro-bolometer, is investigated for the efficient sensing in the infrared band. The proposed devices are designed to operate at 10.6 µm, regime where the complex refractive index of metals becomes important, in contrast to the visible counterpart, and where a full parametric analysis is performed. By using a particle swarm algorithm (PSO) the geometry was adjusted to match the impedance between the nanoantenna and the microbolometer, reducing the return losses by a factor of 650 percent. This technique is compared to standards matching techniques based on transmission lines, showing better accuracy. Tapered dipoles, therefore, open the route towards an efficient energy transfer between load elements and resonant nanoantennas