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  • 1.
    Gabrielsson, Patrick
    et al.
    University of Borås, School of Business and IT.
    Johansson, Ulf
    University of Borås, School of Business and IT.
    König, Rikard
    University of Borås, School of Business and IT.
    Co-Evolving Online High-Frequency Trading Strategies Using Grammatical Evolution2014Conference paper (Refereed)
    Abstract [en]

    Numerous sophisticated algorithms exist for discovering reoccurring patterns in financial time series. However, the most accurate techniques available produce opaque models, from which it is impossible to discern the rationale behind trading decisions. It is therefore desirable to sacrifice some degree of accuracy for transparency. One fairly recent evolutionary computational technology that creates transparent models, using a user-specified grammar, is grammatical evolution (GE). In this paper, we explore the possibility 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 grammatical evolution. We compare the performance of models incorporating risk into their calculations with models that do not. Our empirical results suggest that profitable, risk-averse, transparent trading strategies for the E-mini S&P 500 can be obtained using grammatical evolution together with technical indicators.

    Download full text (pdf)
    FULLTEXT01
  • 2.
    Gabrielsson, Patrick
    et al.
    University of Borås, School of Business and IT.
    König, Rikard
    University of Borås, School of Business and IT.
    Johansson, Ulf
    University of Borås, School of Business and IT.
    Evolving Hierarchical Temporal Memory-Based Trading Models2013Conference paper (Refereed)
    Abstract [en]

    We explore the possibility of using the genetic algorithm to optimize trading models based on the Hierarchical Temporal Memory (HTM) machine learning technology. Technical indicators, derived from intraday tick data for the E-mini S&P 500 futures market (ES), were used as feature vectors to the HTM models. All models were configured as binary classifiers, using a simple buy-and-hold trading strategy, and followed a supervised training scheme. The data set was partitioned into multiple folds to enable a modified cross validation scheme. Artificial Neural Networks (ANNs) were used to benchmark HTM performance. The results show that the genetic algorithm succeeded in finding predictive models with good performance and generalization ability. The HTM models outperformed the neural network models on the chosen data set and both technologies yielded profitable results with above average accuracy.

    Download full text (pdf)
    FULLTEXT01
  • 3.
    Gabrielsson, Patrick
    et al.
    University of Borås, School of Business and IT.
    König, Rikard
    University of Borås, School of Business and IT.
    Johansson, Ulf
    University of Borås, School of Business and IT.
    Hierarchical Temporal Memory-based algorithmic trading of financial markets2012Conference paper (Refereed)
    Abstract [en]

    This paper explores the possibility of using the Hierarchical Temporal Memory (HTM) machine learning technology to create a profitable software agent for trading financial markets. Technical indicators, derived from intraday tick data for the E-mini S&P 500 futures market (ES), were used as features vectors to the HTM models. All models were configured as binary classifiers, using a simple buy-and-hold trading strategy, and followed a supervised training scheme. The data set was divided into a training set, a validation set and three test sets; bearish, bullish and horizontal. The best performing model on the validation set was tested on the three test sets. Artificial Neural Networks (ANNs) were subjected to the same data sets in order to benchmark HTM performance. The results suggest that the HTM technology can be used together with a feature vector of technical indicators to create a profitable trading algorithm for financial markets. Results also suggest that HTM performance is, at the very least, comparable to commonly applied neural network models.

    Download full text (pdf)
    FULLTEXT01
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  • fi-FI
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