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

Berutich, José Manuel; Luna, Francisco; López, Francisco

doi:10.3233/aic-140609

Cited by 3 publications

(2 citation statements)

References 33 publications

(30 reference statements)

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“…We execute 30 independent runs in all of our experiments, and measure the robustness of solutions using shrinkage (Mehta and Bhattacharyya, 2004;Berutich et al, 2014). Shrinkage is calculated as the percentage change in performance between training and testing data.…”

Section: Methodsmentioning

confidence: 99%

Robust technical trading strategies using GP for algorithmic portfolio selection

Berutich

López

Luna

et al. 2016

Expert Systems with Applications

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This paper presents a Robust Genetic Programming approach for discovering profitable trading rules which are used to manage a portfolio of stocks from the Spanish market. The investigated method is used to determine potential buy, sell conditions for stocks, aiming to yield robust solutions able to withstand extreme market conditions, while producing high returns at a minimal risk. One of the biggest challenges GP evolved solutions face is over-fitting. GP trading rules need to have similar performance when tested with new data in order to be deployed in a real situation. We explore a random sampling method (RSFGP) which instead of calculating the fitness over the whole dataset, calculates it on randomly selected segments of it. This method shows improved robustness and out-of-sample results compared to standard genetic programming and a volatility adjusted fitness. Trading strategies (TS) are evolved using financial metrics like the volatility, CAPM alpha and beta, and the Sharpe ratio alongside other Technical Indicators (TI) to find the best investment strategy. These strategies are evaluated in using 21 of the most liquid stocks of the Spanish market. The achieved results clearly outperform both the Buy&Hold. Additionally, the solutions obtained with the training data during the experiments clearly show during testing robustness to step market declines as seen in the European sovereign debt experienced recently in Spain. In this paper the solutions learned where able to operate for prolonged periods, which demonstrated the validity and robustness of the rules learned, which are able to operate continuously and with minimal human intervention. To sum up, the developed method is able to evolve TSs suitable for all market conditions with promising results, which suggests great potential in the method generalization capabilities. The use of the financial metrics alongside popular TI enables the system to increase the stock return while proving resilient through time. The GP system is able to cope with different types of markets achieving a portfolio return of slightly higher than 30% for the period of 20092013 in the Spanish market, in a period that includes the sovereign debt crisis.

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Section: Methodsmentioning

confidence: 99%

Robust technical trading strategies using GP for algorithmic portfolio selection

Berutich

López

Luna

et al. 2016

Expert Systems with Applications

Self Cite

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“…We can also mention [21], which plays with modifications and extensions of common robust optimization techniques by using an hybrid heuristic as solver, or [22], that introduces a single/multiobjective inverse robust evolutionary approach based on non-probabilistic methods that tries to deal with uncertainty in parameters. Lastly, in [23], the authors explored the possibility of increasing the robustness of the solutions obtained by a multiobjective genetic algorithm, exposing the system to several markets.…”

Section: Introductionmentioning

confidence: 99%

Portfolio Implementation Risk Management Using Evolutionary Multiobjective Optimization

et al. 2017

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Portfolio management based on mean-variance portfolio optimization is subject to different sources of uncertainty. In addition to those related to the quality of parameter estimates used in the optimization process, investors face a portfolio implementation risk. The potential temporary discrepancy between target and present portfolios, caused by trading strategies, may expose investors to undesired risks. This study proposes an evolutionary multiobjective optimization algorithm aiming at regions with solutions more tolerant to these deviations and, therefore, more reliable. The proposed approach incorporates a user's preference and seeks a fine-grained approximation of the most relevant efficient region. The computational experiments performed in this study are based on a cardinality-constrained problem with investment limits for eight broad-category indexes and 15 years of data. The obtained results show the ability of the proposed approach to address the robustness issue and to support decision making by providing a preferred part of the efficient set. The results reveal that the obtained solutions also exhibit a higher tolerance to prediction errors in asset returns and variance-covariance matrix.

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Adaptive GP agent-based trading system under intraday seasonality model

Aloud¹

2017

IDT

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

Cited by 3 publications

References 33 publications

Robust technical trading strategies using GP for algorithmic portfolio selection

Robust technical trading strategies using GP for algorithmic portfolio selection

Portfolio Implementation Risk Management Using Evolutionary Multiobjective Optimization

Adaptive GP agent-based trading system under intraday seasonality model

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