Otimização Evolutiva Lexicográfica de um Portfólio de Estratégias Automatizadas no Mercado Futuro Brasileiro

• Anderson C. Guedes UEFS
• Carlos Alberto Rodrigues UEFS
• Angelo C. Loula UEFS

DOI: https://doi.org/10.5753/eniac.2021.18275

Abstract

A set of automated trading strategies in capital markets can be organized into a portfolio seeking to maximize returns and minimize losses. The best arrangement for the portfolio requires assigning optimal weights to each strategy, considering different financial indicators. This article proposes the application of an evolutionary algorithm with a lexicographical approach to optimize a portfolio of automated strategies applied to the Brazilian future market. The experiments consider different objective functions of financial indicators, with different orderings, in addition to optimization conditions and temporal variations, applying historical data from future indexes mini-contracts of the Ibovespa and US dollar.

References

Aranha, C. and Iba, H. (2007). Modelling cost into a genetic algorithm-based portfolio optimization system by seeding and objective sharing. In 2007 IEEE Congress on Evolutionary Computation, pages 196–203. IEEE.

Cacique, F. V. and Pereira, A. C. M. (2020). Pattern searcher for decision making of trading agents using genetic algorithm. In 2020 IEEE Congress on Evolutionary Computation (CEC), pages 1–8.

Cavalcante, R. C., Brasileiro, R. C., Souza, V. L., Nobrega, J. P., and Oliveira, A. L. (2016). Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications, 55:194 – 211.

Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. John Wiley & Sons, Inc., New York, NY, USA.

Fishburn, P. C. (1974). Exceptional paper - lexicographic orders, utilities and decision rules: A survey. Management science, 20(11):1442–1471.

Glantz, M. and Kissell, R. (2014). Multi-Asset Risk Modeling: Techniques for a Global Economy in an Electronic and Algorithmic Trading Era. Academic Press.

Haynes, R. and Roberts, J. S. (2017). Automated trading in futures markets-update. White paper, Office of the Chief Economist, Commodity Futures Trading Commission.

Liao, B.-Y., Chen, H.-W., Kuo, S.-Y., and Chou, Y.-H. (2015). Portfolio optimization based on novel risk assessment strategy with genetic algorithm. In 2015 IEEE International Conference on Systems, Man, and Cybernetics, pages 2861–2866.

Macedo, L. L., Godinho, P., and Alves, M. J. (2017). Mean-semivariance portfolio optimization with multiobjective evolutionary algorithms and technical analysis rules. Expert Systems with Applications, 79:33–43.

Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1):77–91. Nuti, G., Mirghaemi, M., Treleaven, P., and Yingsaeree, C. (2011). Algorithmic trading. Computer, (11):61–69.

Paraná, E. (2017). A digitalização do mercado de capitais no brasil: tendências recentes. Boletim de Economia e Política Internacional, BEPI(25):43–71.

Pardo, R. (1992). Design, testing, and optimization of trading systems, volume 2. John Wiley & Sons.

Pimenta, A., Nametala, C. A. L., Guimarães, F. G., and Carrano, E. G. (2018). An automated investing method for stock market based on multiobjective genetic programming. Computational Economics, 52(1):125–144.

Sawik, B. (2013). Survey of multi-objective portfolio optimization by linear and mixed integer programming. In Applications of management science. Emerald Group Publishing Limited.

Schwager, J. D. (2017). A Complete Guide to the Futures Market: Technical Analysis, Trading Systems, Fundamental Analysis, Options, Spreads, and Trading Principles. John Wiley & Sons.