This book presents a genetic algorithm that optimizes a grid template pattern detector to find the best point to trade in the SP 500. The pattern detector is based on a template using a grid of weights with a fixed size. The template takes in consideration not only the closing price but also the open, high, and low values of the price during the period under testing in contrast to the traditional methods of analysing only the closing price. Each cell of the grid encompasses a score, and these are optimized by an evolutionary genetic algorithm that takes genetic diversity into consideration through a speciation routine, giving time for each individual of the population to be optimized within its own niche. With this method, the system is able to present better results and improves the results compared with other template approaches. The tests considered real data from the stock market and against state-of-the-art solutions, namely the ones using a grid of weights which does not havea fixed size and non-speciated approaches. During the testing period, the presented solution had a return of 21.3% compared to 10.9% of the existing approaches. The use of speciation was able to increase the returns of some results as genetic diversity was taken into consideration.

Presents a new way to attribute the score to the signal Shows how to retrieve more information and improves the accuracy of the algorithm's decision Reports the optimization of grid scores

Autorentext
Tiago Mousinho Martins is Analytics Solutions Professional at Nokia since 2019. He received the Master's Degree in Telecommunications and Computer Science Engineering from Instituto Superior Técnico, Technical University of Lisbon, Portugal, in 2018. His professional career started at EY (formerly Ernst & Young) where he enrolled in data analytics and software engineering tasks, such as ETL and automation assignments, .NET development for client and internal projects, Azure Cloud Server administration, and process mining research. Afterwards, he moved from Nokia to his current job as Data Scientist/Analytics Solutions Professional in a Global Customer Care Analytics Team that leverages big data technologies (Hadoop Ecosystem) to deliver insights to the customers regarding fixed and network insights for end users.

Rui Ferreira Neves is a professor at Instituto Superior Técnico since 2005. He received the Diploma in Engineering and the Ph.D. degrees in Electrical and Computer Engineering from the Instituto Superior Técnico, Technical University of Lisbon, Portugal, in 1993 and 2001, respectively. In 2006, he joined Instituto de Telecomunicações (IT) as a research associate. His research activity deals with evolutionary computation and pattern matching applied to the financial markets, sensor networks, embedded systems, and mixed signal integrated circuits. He uses both fundamental, technical, and pattern matching indicators to find the evolution of the financial markets. 

Inhalt

1. Introduction.- 2. Related Work.- 3. Architecture.- 4. Test Scenarios and Evaluation.- 5. Conclusions and Future Work.