Co-evolving online high-frequency trading strategies using grammatical evolution

Gabrielsson, Patrick; Johansson, Ulf; König, Rikard

doi:10.1109/cifer.2014.6924111

Cited by 5 publications

(3 citation statements)

References 4 publications

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“…Gabrielsson et al [26] explored the feasibility of evolving transparent entry and exit trading strategies, for the E-mini S&P 500 index futures market, in a high-frequency trading environment using GE. They compared the performance of models incorporating risk into their calculations with models that did not.…”

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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“…In the sphere of HFT, authors in [78] utilize seven trading rule families as a measure of the impact of trading speed, while in [44] a fuzzy momentum analysis based on technical indicators for high speed trading is presented. Grammatical evolution is used in the E-mini S&P 500 index futures market along with technical indicators for entry and exit trading exploration in [32]. In [53] authors provide an extensive investigation of charting analysis of nonparametric kernel regression for Nasdaq stocks via an automated strategy.…”

Section: Related Literaturementioning

confidence: 99%

Mid-Price Prediction Based on Machine Learning Methods with Technical and Quantitative Indicators

et al. 2018

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Stock price prediction is a challenging task, but machine learning methods have recently been used successfully for this purpose. In this paper, we extract over 270 hand-crafted features (factors) inspired by technical and quantitative analysis and tested their validity on short-term mid-price movement prediction. We focus on a wrapper feature selection method using entropy, least-mean squares, and linear discriminant analysis. We also build a new quantitative feature based on adaptive logistic regression for online learning, which is constantly selected first among the majority of the proposed feature selection methods. This study examines the best combination of features using high frequency limit order book data from Nasdaq Nordic. Our results suggest that sorting methods and classifiers can be used in such a way that one can reach the best performance with a combination of only very few advanced hand-crafted features.

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“…In the first phase, a global optimization method directs the production of artificial features from the existing ones with the help of grammatical evolution [70]. Grammatical evolution is a variation of genetic programming where the chromosomes are production rules of the target BNF grammar, and it has been used successfully in a variety of applications, such as music composition [71], economics [72], symbolic regression [73], robotics [74], and caching algorithms [75]. The global optimization method used in this work is the particle swarm optimization (PSO) method [76][77][78].…”

mentioning

confidence: 99%

A Feature Construction Method That Combines Particle Swarm Optimization and Grammatical Evolution

Tsoulos

Tzallas

2023

Applied Sciences

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The problem of data classification or data fitting is widely applicable in a multitude of scientific areas, and for this reason, a number of machine learning models have been developed. However, in many cases, these models present problems of overfitting and cannot generalize satisfactorily to unknown data. Furthermore, in many cases, many of the features of the input data do not contribute to learning, or there may even be hidden correlations between the features of the dataset. The purpose of the proposed method is to significantly reduce data classification or regression errors through the usage of a technique that utilizes the particle swarm optimization method and grammatical evolution. This method is divided into two phases. In the first phase, artificial features are constructed using grammatical evolution, and the progress of the creation of these features is controlled by the particle swarm optimization method. In addition, this new technique utilizes penalty factors to limit the generated features to a range of values to make training machine learning models more efficient. In the second phase of the proposed technique, these features are exploited to transform the original dataset, and then any machine learning method can be applied to this dataset. The performance of the proposed method was measured on some benchmark datasets from the relevant literature. Also, the method was tested against a series of widely used machine learning models. The experiments performed showed a significant improvement of 30% on average in the classification datasets and an even greater improvement of 60% in the data fitting datasets.

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Co-evolving online high-frequency trading strategies using grammatical evolution

Cited by 5 publications

References 4 publications

Evolution of trading strategies with flexible structures: A configuration comparison

Evolution of trading strategies with flexible structures: A configuration comparison

Mid-Price Prediction Based on Machine Learning Methods with Technical and Quantitative Indicators

A Feature Construction Method That Combines Particle Swarm Optimization and Grammatical Evolution

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