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Sökning: WAKA:kon > Högskolan i Borås > König Rikard

  • Resultat 1-10 av 21
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1.
  • Gabrielsson, Patrick, et al. (författare)
  • Co-Evolving Online High-Frequency Trading Strategies Using Grammatical Evolution
  • 2014
  • Konferensbidrag (refereegranskat)abstract
    • 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.
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2.
  • Gabrielsson, Patrick, et al. (författare)
  • Evolving Hierarchical Temporal Memory-Based Trading Models
  • 2013
  • Konferensbidrag (refereegranskat)abstract
    • 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.
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3.
  • Gabrielsson, Patrick, et al. (författare)
  • Hierarchical Temporal Memory-based algorithmic trading of financial markets
  • 2012
  • Konferensbidrag (refereegranskat)abstract
    • 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.
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4.
  • Johansson, Ulf, et al. (författare)
  • Accurate and Interpretable Regression Trees using Oracle Coaching
  • 2014
  • Konferensbidrag (refereegranskat)abstract
    • In many real-world scenarios, predictive models need to be interpretable, thus ruling out many machine learning techniques known to produce very accurate models, e.g., neural networks, support vector machines and all ensemble schemes. Most often, tree models or rule sets are used instead, typically resulting in significantly lower predictive performance. The over- all purpose of oracle coaching is to reduce this accuracy vs. comprehensibility trade-off by producing interpretable models optimized for the specific production set at hand. The method requires production set inputs to be present when generating the predictive model, a demand fulfilled in most, but not all, predic- tive modeling scenarios. In oracle coaching, a highly accurate, but opaque, model is first induced from the training data. This model (“the oracle”) is then used to label both the training instances and the production instances. Finally, interpretable models are trained using different combinations of the resulting data sets. In this paper, the oracle coaching produces regression trees, using neural networks and random forests as oracles. The experiments, using 32 publicly available data sets, show that the oracle coaching leads to significantly improved predictive performance, compared to standard induction. In addition, it is also shown that a highly accurate opaque model can be successfully used as a pre- processing step to reduce the noise typically present in data, even in situations where production inputs are not available. In fact, just augmenting or replacing training data with another copy of the training set, but with the predictions from the opaque model as targets, produced significantly more accurate and/or more compact regression trees.
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6.
  • Johansson, Ulf, et al. (författare)
  • Genetic rule extraction optimizing brier score
  • 2010
  • Ingår i: Proceedings of the 12th Annual Genetic and Evolutionary Computation Conference, GECCO '10. - New York : Association for Computing Machinery (ACM). - 9781450300728 ; , s. 1007-1014
  • Konferensbidrag (refereegranskat)abstract
    • Most highly accurate predictive modeling techniques produce opaque models. When comprehensible models are required, rule extraction is sometimes used to generate a transparent model, based on the opaque. Naturally, the extracted model should be as similar as possible to the opaque. This criterion, called fidelity, is therefore a key part of the optimization function in most rule extracting algorithms. To the best of our knowledge, all existing rule extraction algorithms targeting fidelity use 0/1 fidelity, i.e., maximize the number of identical classifications. In this paper, we suggest and evaluate a rule extraction algorithm utilizing a more informed fidelity criterion. More specifically, the novel algorithm, which is based on genetic programming, minimizes the difference in probability estimates between the extracted and the opaque models, by using the generalized Brier score as fitness function. Experimental results from 26 UCI data sets show that the suggested algorithm obtained considerably higher accuracy and significantly better AUC than both the exact same rule extraction algorithm maximizing 0/1 fidelity, and the standard tree inducer J48. Somewhat surprisingly, rule extraction using the more informed fidelity metric normally resulted in less complex models, making sure that the improved predictive performance was not achieved on the expense of comprehensibility. Copyright 2010 ACM.
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7.
  • Johansson, Ulf, et al. (författare)
  • Using Genetic Programming to Obtain Implicit Diversity
  • 2009
  • Konferensbidrag (refereegranskat)abstract
    • When performing predictive data mining, the use of ensembles is known to increase prediction accuracy, compared to single models. To obtain this higher accuracy, ensembles should be built from base classifiers that are both accurate and diverse. The question of how to balance these two properties in order to maximize ensemble accuracy is, however, far from solved and many different techniques for obtaining ensemble diversity exist. One such technique is bagging, where implicit diversity is introduced by training base classifiers on different subsets of available data instances, thus resulting in less accurate, but diverse base classifiers. In this paper, genetic programming is used as an alternative method to obtain implicit diversity in ensembles by evolving accurate, but different base classifiers in the form of decision trees, thus exploiting the inherent inconsistency of genetic programming. The experiments show that the GP approach outperforms standard bagging of decision trees, obtaining significantly higher ensemble accuracy over 25 UCI datasets. This superior performance stems from base classifiers having both higher average accuracy and more diversity. Implicitly introducing diversity using GP thus works very well, since evolved base classifiers tend to be highly accurate and diverse.
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8.
  • König, Rikard, et al. (författare)
  • Finding the Tree in the Forest
  • 2010
  • Ingår i: Proceeding of IADIS International Conference Applied Computing 2010. - : IADIS Press. - 9789728939304 ; , s. 135-142
  • Konferensbidrag (refereegranskat)abstract
    • Decision trees are often used for decision support since they are fast to train, easy to understand and deterministic; i.e., always create identical trees from the same training data. This property is, however, only inherent in the actual decision tree algorithm, nondeterministic techniques such as genetic programming could very well produce different trees with similar accuracy and complexity for each execution. Clearly, if more than one solution exists, it would be misleading to present a single tree to a decision maker. On the other hand, too many alternatives could not be handled manually, and would only lead to confusion. Hence, we argue for a method aimed at generating a suitable number of alternative decision trees with comparable accuracy and complexity. When too many alternative trees exist, they are grouped and representative accurate solutions are selected from each group. Using domain knowledge, a decision maker could then select a single best tree and, if required, be presented with a small set of similar solutions, in order to further improve his decisions. In this paper, a method for generating alternative decision trees is suggested and evaluated. All in all,four different techniques for selecting accurate representative trees from groups of similar solutions are presented. Experiments on 19 UCI data sets show that it often exist dozens of alternative trees, and that one of the evaluated techniques clearly outperforms all others for selecting accurate and representative models.
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10.
  • König, Rikard, et al. (författare)
  • Improving GP Classification Performance by Injection of Decision Trees
  • 2010
  • Konferensbidrag (refereegranskat)abstract
    • This paper presents a novel hybrid method combining genetic programming and decision tree learning. The method starts by estimating a benchmark level of reasonable accuracy, based on decision tree performance on bootstrap samples of the training set. Next, a normal GP evolution is started with the aim of producing an accurate GP. At even intervals, the best GP in the population is evaluated against the accuracy benchmark. If the GP has higher accuracy than the benchmark, the evolution continues normally until the maximum number of generations is reached. If the accuracy is lower than the benchmark, two things happen. First, the fitness function is modified to allow larger GPs, able to represent more complex models. Secondly, a decision tree with increased size and trained on a bootstrap of the training data is injected into the population. The experiments show that the hybrid solution of injecting decision trees into a GP population gives synergetic effects producing results that are better than using either technique separately. The results, from 18 UCI data sets, show that the proposed method clearly outperforms normal GP, and is significantly better than the standard decision tree algorithm.
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