Parallel genetic algorithms for stock market trading rules

Straßburg, Janko; González-Martel, Christian; Alexandrov, Vassil

doi:10.1016/j.procs.2012.04.143

Cited by 24 publications

(16 citation statements)

References 11 publications

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“…They are known as Pareto optimal solutions [1] [2]. MOPs naturally arise in many area of knowledge such as economics [3]- [5], machine learning [6]- [8] and electrical power system [9]- [12].…”

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confidence: 99%

Local Search-Inspired Rough Sets for Improving Multiobjective Evolutionary Algorithm

El‐Sawy¹,

Hussein²,

Zaki³

et al. 2014

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In this paper we present a new optimization algorithm, and the proposed algorithm operates in two phases. In the first one, multiobjective version of genetic algorithm is used as search engine in order to generate approximate true Pareto front. This algorithm is based on concept of co-evolution and repair algorithm for handling nonlinear constraints. Also it maintains a finite-sized archive of non-dominated solutions which gets iteratively updated in the presence of new solutions based on the concept of ε-dominance. Then, in the second stage, rough set theory is adopted as local search engine in order to improve the spread of the solutions found so far. The results, provided by the proposed algorithm for benchmark problems, are promising when compared with exiting well-known algorithms. Also, our results suggest that our algorithm is better applicable for solving real-world application problems.

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confidence: 99%

Local Search-Inspired Rough Sets for Improving Multiobjective Evolutionary Algorithm

El‐Sawy¹,

Hussein²,

Zaki³

et al. 2014

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“…Interbank rate, MA [116] Interbank rate, MA [117] GA [118] Note: the Optimization object "MF", " RComb. ", "RCons.…”

Section: Page 19 Of 52mentioning

confidence: 99%

Application of evolutionary computation for rule discovery in stock algorithmic trading: A literature review

Liu

Zhang

et al. 2015

Applied Soft Computing

159

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“…By taking the transaction cost into consideration, Mallick et al [21] and Esfahanipour et al [22] reported positive excess return over buy-and-hold strategies in the markets, the genetic programming generated trading rules were able to beat the popularly used MACD technical indicator. Straßburg et al [23] proposed the parallelization to improve the generation of technical trading rules by speeding up the computing and enabling more results. Hongguang and Ping [24] tested the performance of genetic programming systems with the China index future market.…”

Section: Genetic Programmingmentioning

confidence: 99%

Intra-Day Trading System Design Based on the Integrated Model of Wavelet De-Noise and Genetic Programming

Liu

Jin

2016

Entropy

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Technical analysis has been proved to be capable of exploiting short-term fluctuations in financial markets. Recent results indicate that the market timing approach beats many traditional buy-and-hold approaches in most of the short-term trading periods. Genetic programming (GP) was used to generate short-term trade rules on the stock markets during the last few decades. However, few of the related studies on the analysis of financial time series with genetic programming considered the non-stationary and noisy characteristics of the time series. In this paper, to de-noise the original financial time series and to search profitable trading rules, an integrated method is proposed based on the Wavelet Threshold (WT) method and GP. Since relevant information that affects the movement of the time series is assumed to be fully digested during the market closed periods, to avoid the jumping points of the daily or monthly data, in this paper, intra-day high-frequency time series are used to fully exploit the short-term forecasting advantage of technical analysis. To validate the proposed integrated approach, an empirical study is conducted based on the China Securities Index (CSI) 300 futures in the emerging China Financial Futures Exchange (CFFEX) market. The analysis outcomes show that the wavelet de-noise approach outperforms many comparative models.

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Parallel genetic algorithms for stock market trading rules

Cited by 24 publications

References 11 publications

Local Search-Inspired Rough Sets for Improving Multiobjective Evolutionary Algorithm

Local Search-Inspired Rough Sets for Improving Multiobjective Evolutionary Algorithm

Application of evolutionary computation for rule discovery in stock algorithmic trading: A literature review

Intra-Day Trading System Design Based on the Integrated Model of Wavelet De-Noise and Genetic Programming

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