Investigating technical trading strategy via an multi-objective evolutionary platform

We present a robust multi-market optimization methodology for technical trading strategies, whereby robustness is incorporated via the environmental variables of the problem. The search for the optimum parameters is conducted over several markets, in the hope of exposing the GA to differing conditions, increasing the robustness of the solutions produced. Our results show an improvement in terms of performance for the solutions generated under this robust method when compared to those offered by single-market optimization.

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“…Evaluate(P (t)) 8: 9: if P (t + 1) not full then 10: R ← CrowdingDistanceSelection(P (t) ∪ P (t)) 11:…”

Section: Multi-objective Ga Usedmentioning

confidence: 99%

“…Otherwise, if there is a big performance difference, the solutions could be useless. Previous studies by [11] mention the importance of having a positive correlation between training and evaluation performance. We measured the correlation between SRs obtained during training and evaluation.…”

Section: Experiments 1: Single-market Optimizationmentioning

confidence: 99%

On the quest for robust technical trading strategies using multi-objective optimization

2014

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“…A Multi-Objective system to maximize the total returns and to minimize the risk as the exposure to it is proposed by (Chiam et al, 2009). The framework is tested using data gotten from one stock market, the Singapore Exchange stock market (Straits Times Index (STI)).…”

Section: Related Workmentioning

confidence: 99%

Multi-objective Optimization of Investment Strategies - Based on Evolutionary Computation Techniques, in Volatile Environments

Pinto¹,

Neves²,

Horta³

2014

Proceedings of the 16th International Conference on Enterprise Information Systems

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In this document, the use of a multi-objective evolutionary system to optimize an investment strategy based on the use of Moving Averages is proposed to be used on stock markets, able to yield high returns at minimal risk. Fair and established metrics are used to both evaluate the return and the risk of the optimized strategies. The Pareto Fronts obtained with the training data during the experiments conducted outperform both B&H strategy and the classical approaches that consider solely the absolute return. Additionally, the PF obtained show the inherent trade-off between risk and returns. The experimental results are evaluated using data coming from the principal world markets, namely, the main stock indexes of the most developed economies, such as: NASDAQ, S&P500, FTSE100, DAX30 and NIKKEI225. Although, the experimental results suggest that the positive connection between the gains with training and testing data, usually assumed in the single-objective proposals, is not necessarily true for all cases.

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“…The last decades have been fertile in research regarding optimization techniques applied to Finance, e.g., (Meade and Salkin 1989), (Lee and Eom 1989), (Suganya and Pai 2012), (Hsu 2014). Important results are based on TA inspired models with respect to Forex markets (Olson 2004), (Schulmeister 2008), (Schulmeister 2009), and use of GA (Lo et al 2000), (Hryshko and Downs 2004), (Chiam et al 2009), (Pavlova and Parhizgari 2011), (Mendes et al 2012), (Godinho 2012). Some new articles -(Pavlova and Parhizgari 2011), (Bajgrowicz and Scaillet 2012), (Lin et al 2011), (S.-W. Chen 2010), (Fang et al 2014), (Kuang et al 2014) -question, in a more empirically sustained way, the value of TA in achieving abnormal excess returns, especially when taking into account trading costs.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Technical Trading Strategies Using a Genetic Algorithm

2016

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Traditional approaches to the study of technical analysis (TA) often focus on the performance of a single indicator, which seems to fall short in scope and depth. We use a genetic algorithm (GA) to optimize trading strategies in the three major Forex markets, in order to verify the adequacy of TA strategies and rules to attain consistent superior returns, by comparing momentum, trend and breakout indicators. The indicators with the parameters generated through our GA consistently outperform the equivalent indicators applying parameters commonly used by the trading industry. EUR/USD and GBP/USD markets present interesting return figures before trading costs. The inclusion of spreads and commissions deteriorates returns substantially, suggesting these markets, under a more realistic set of assumptions, may be efficient. Trend indicators generate better outcomes and GBP/USD qualifies as the most profitable market. Different aggregate returns in different markets may stand as evidence of distinct maturation stages under an evolving efficiency market perspective. Our GA is able to search a wider solution space than traditional configurations and presents the possibility of recovering latent data, avoiding premature convergence.

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Investigating technical trading strategy via an multi-objective evolutionary platform

Cited by 19 publications

References 41 publications

On the quest for robust technical trading strategies using multi-objective optimization

On the quest for robust technical trading strategies using multi-objective optimization

Multi-objective Optimization of Investment Strategies - Based on Evolutionary Computation Techniques, in Volatile Environments

A Comparative Study of Technical Trading Strategies Using a Genetic Algorithm

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