Optimization of Trading Rules for the Spanish Stock Market by Genetic Programming

Luengo, Sergio; Winkler, Stephan; Barrero, David F.; Castaño, Bonifacio

doi:10.1007/978-3-319-19066-2_60

Cited by 6 publications

(3 citation statements)

References 10 publications

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“…Luengo et al [27], based on the application of a set of simple trading rules optimized by GP, looked for a method for generating input and output signals in the Spanish stock market under three different market scenarios: bull market, bear market and sideways market. In their results, they found that market global behavior had a great influence on the results of each method.…”

Section: Contributions Based On Genetic Programmingmentioning

confidence: 99%

Evolution of trading strategies with flexible structures: A configuration comparison

2019

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Section: Contributions Based On Genetic Programmingmentioning

confidence: 99%

Evolution of trading strategies with flexible structures: A configuration comparison

2019

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“…Our proposed method has some advantages, first our method learns trading rules, and not only optimizes the parameters of some predefined rules, but is able to construct rules and choose the best parameters as we employ a GP system instead of a GA. Secondly our GP system learns the trading rules from a basket of stocks, instead of a single market index, exposing the evolutionary process to more data, and different market conditions, as some stocks can be in a bullish trend while other are bearish or range bound (sideways), thus producing solutions that are more robust, and able to cope with extreme market conditions. Luengo et al (2015) manually divide the stock price time series into 3 segments of 4 years which sometimes overlap, and then this previous segments are again divided into 3 different period, 1 for pre calculating the technical indices, 2 for GP training and 3 for testing. In comparison our approach is has some advantages as we can use the best evolved rule in all market conditions proving to be resilient to regime switches in the data.…”

Section: Related Workmentioning

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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“…Hongguang and Ping [24] tested the performance of genetic programming systems with the China index future market. Luengo et al [25] separated the market into three market scenarios, including bull market, bear market and sideways markets. They found that strategies based on genetic programming perform the best in the sideways 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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Optimization of Trading Rules for the Spanish Stock Market by Genetic Programming

Cited by 6 publications

References 10 publications

Evolution of trading strategies with flexible structures: A configuration comparison

Evolution of trading strategies with flexible structures: A configuration comparison

Robust technical trading strategies using GP for algorithmic portfolio selection

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

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