Hierarchical Temporal Memory-based algorithmic trading of financial markets

Gabrielsson, Patrick; König, Rikard; Johansson, Ulf

doi:10.1109/cifer.2012.6327784

Cited by 5 publications

(3 citation statements)

References 18 publications

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“…Results showed an increase in accuracy for both datasets (73.5 percent versus 71.2 percent and 86.7 percent versus 84.2, respectively), compared with the original LLC model. Gabrielsson, Konig, and Johansson (2012) aimed at leveraging HTM to create a profitable software agent for trading financial markets. A supervised training scheme was used for HTM, with intraday tick data for the E-mini Standard and Poor's 500 futures markets.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning

Awad¹,

Khanna²

2015

Efficient Learning Machines

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Any fool can know. The point is to understand. -Albert EinsteinArtificial neural networks (ANNs) have had a history riddled with highs and lows since their inception. At a nodal level, ANNs started with highly simplified neural models, such as McCulloch-Pitts neurons (McCulloch and Pitts 1943), and then evolved into Rosenblatt's perceptrons (Rosenblatt 1957) and a variety of more complex and sophisticated computational units. From single-and multilayer networks, to self-recurrent Hopfield networks (Tank and Hopfield 1986), to self-organizing maps (also called Kohonen networks) (Kohonen 1982), adaptive resonance theory and time delay neural networks among other recommendations, ANNs have witnessed many structural iterations. These generations carried incremental enhancements that promised to address predecessors' limitations and achieve higher levels of intelligence. Nonetheless, the compounded effect of these "intelligent" networks has not been able to capture the true human intelligence (Guerriere and Detsky 1991; Becker and Hinton 1992). Thus, Deep learning is on the rise in the machine learning community, because the traditional shallow learning architectures have proved unfit for the more challenging tasks of machine learning and strong artificial intelligence (AI). The surge in and wide availability of increased computing power (Misra and Saha 2010), coupled with the creation of efficient training algorithms and advances in neuroscience, have enabled the implementation, hitherto impossible, of deep learning principles. These developments have led to the formation of deep architecture algorithms that look in to cognitive neuroscience to suggest biologically inspired learning solutions. This chapter presents the concepts of spiking neural networks (SNNs) and hierarchical temporal memory (HTM), whose associated techniques are the least mature of the techniques covered in this book. Overview of Hierarchical Temporal MemoryHTM aims at replicating the functional and structural properties of the neocortex. HTM incorporates a number of insights from Hawkins's book On Intelligence (2007), which postulates that the key to intelligence is the ability to predict. Its framework was designed as a biomimetic model of the neocortex that seeks to replicate the brain's structural and algorithmic properties, albeit in a simplified, functionally oriented manner. HTM is therefore organized hierarchically, as depicted generically in Figure 9-1. All levels of hierarchy and their subcomponents perform a common computational algorithm.

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Section: Related Workmentioning

confidence: 99%

Deep Learning

Awad¹,

Khanna²

2015

Efficient Learning Machines

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“…In [3], the predictive performance of the HTM technology was investigated within algorithmic trading of financial markets. The study showed promising results, in which the HTM-based algorithm was profitable across bullish-, bearish and horizontal market trends, yielding comparable results to its neural network benchmark.…”

Section: Related Workmentioning

confidence: 99%

“…It borrows concepts from neural networks, Bayesian networks and makes use of spatiotemporal clustering algorithms to handle noisy inputs and to create invariant representations of patterns discovered in its input stream. In a previous paper [3], an initial study was carried-out where the predictive performance of the HTM technology was investigated within algorithmic trading of financial markets. The study showed promising results, in which the HTMbased algorithm was profitable across bullish-, bearish and horizontal market trends, yielding comparable results to its neural network benchmark.…”

Section: Introductionmentioning

confidence: 99%

Evolving Hierarchical Temporal Memory-Based Trading Models

Gabrielsson

König

Johansson

2013

Applications of Evolutionary Computation

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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. IntroductionOne recent machine learning technology that has shown good potential in algorithmic trading is Hierarchical Temporal Memory (HTM). It borrows concepts from neural networks, Bayesian networks and makes use of spatiotemporal clustering algorithms to handle noisy inputs and to create invariant representations of patterns discovered in its input stream. In a previous paper [3], an initial study was carried-out where the predictive performance of the HTM technology was investigated within algorithmic trading of financial markets. The study showed promising results, in which the HTMbased algorithm was profitable across bullish-, bearish and horizontal market trends, yielding comparable results to its neural network benchmark. Although, the previous work lacked any attempt to produce near optimal trading models. One popular group of optimization methods are evolutionary optimization methods. The simplest evolutionary optimization technique is the genetic algorithm. This paper extends the HTM-based trading algorithm, developed in the previous work, by employing the genetic algorithm as an optimization method. Once again, neural networks are used as the benchmark technology.

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