scholarly journals Reinforcement Learning for Systematic FX Trading

2021 ◽  
Author(s):  
Gabriel Borrageiro ◽  
Nick Firoozye ◽  
Paolo Barucca
Keyword(s):  
Online Learning ◽  
Reinforcement Learning ◽  
Gaussian Mixture Models ◽  
Feature Space ◽  
Gaussian Mixture ◽  
Learning Context ◽  
Information Ratio ◽  
Intrinsic Nature ◽  
The 2008 Financial Crisis ◽  
Interest Differential

We conduct a detailed experiment on major cash fx pairs, accurately accounting for transaction and funding costs. These sources of profit and loss, including the price trends that occur in the currency markets, are made available to our recurrent reinforcement learner via a quadratic utility, which learns to target a position directly. We improve upon earlier work, by casting the problem of learning to target a risk position, in an online learning context. This online learning occurs sequentially in time, but also in the form of transfer learning. We transfer the output of radial basis function hidden processing units, whose means, covariances and overall size are determined by Gaussian mixture models, to the recurrent reinforcement learner and baseline momentum trader. Thus the intrinsic nature of the feature space is learnt and made available to the upstream models. The recurrent reinforcement learning trader achieves an annualised portfolio information ratio of 0.52 with compound return of 9.3\%, net of execution and funding cost, over a 7 year test set. This is despite forcing the model to trade at the close of the trading day 5pm EST, when trading costs are statistically the most expensive. These results are comparable with the momentum baseline trader, reflecting the low interest differential environment since the the 2008 financial crisis, and very obvious currency trends since then. The recurrent reinforcement learner does nevertheless maintain an important advantage, in that the model's weights can be adapted to reflect the different sources of profit and loss variation. This is demonstrated visually by a USDRUB trading agent, who learns to target different positions, that reflect trading in the absence or presence of cost.<br>

scholarly journals Reinforcement Learning for Systematic FX Trading

2021 ◽  
Author(s):  
Gabriel Borrageiro ◽  
Nick Firoozye ◽  
Paolo Barucca
Keyword(s):  
Online Learning ◽  
Reinforcement Learning ◽  
Gaussian Mixture Models ◽  
Feature Space ◽  
Gaussian Mixture ◽  
Learning Context ◽  
Information Ratio ◽  
Intrinsic Nature ◽  
The 2008 Financial Crisis ◽  
Interest Differential

We conduct a detailed experiment on major cash fx pairs, accurately accounting for transaction and funding costs. These sources of profit and loss, including the price trends that occur in the currency markets, are made available to our recurrent reinforcement learner via a quadratic utility, which learns to target a position directly. We improve upon earlier work, by casting the problem of learning to target a risk position, in an online learning context. This online learning occurs sequentially in time, but also in the form of transfer learning. We transfer the output of radial basis function hidden processing units, whose means, covariances and overall size are determined by Gaussian mixture models, to the recurrent reinforcement learner and baseline momentum trader. Thus the intrinsic nature of the feature space is learnt and made available to the upstream models. The recurrent reinforcement learning trader achieves an annualised portfolio information ratio of 0.52 with compound return of 9.3\%, net of execution and funding cost, over a 7 year test set. This is despite forcing the model to trade at the close of the trading day 5pm EST, when trading costs are statistically the most expensive. These results are comparable with the momentum baseline trader, reflecting the low interest differential environment since the the 2008 financial crisis, and very obvious currency trends since then. The recurrent reinforcement learner does nevertheless maintain an important advantage, in that the model's weights can be adapted to reflect the different sources of profit and loss variation. This is demonstrated visually by a USDRUB trading agent, who learns to target different positions, that reflect trading in the absence or presence of cost.<br>

scholarly journals Reinforcement Learning for Systematic FX Trading

2021 ◽  
Author(s):  
Gabriel Borrageiro ◽  
Nick Firoozye ◽  
Paolo Barucca
Keyword(s):  
Online Learning ◽  
Reinforcement Learning ◽  
Gaussian Mixture Models ◽  
Feature Space ◽  
Gaussian Mixture ◽  
Learning Context ◽  
Information Ratio ◽  
Intrinsic Nature ◽  
The 2008 Financial Crisis ◽  
Interest Differential

We conduct a detailed experiment on major cash fx pairs, accurately accounting for transaction and funding costs. These sources of profit and loss, including the price trends that occur in the currency markets, are made available to our recurrent reinforcement learner via a quadratic utility, which learns to target a position directly. We improve upon earlier work, by casting the problem of learning to target a risk position, in an online learning context. This online learning occurs sequentially in time, but also in the form of transfer learning. We transfer the output of radial basis function hidden processing units, whose means, covariances and overall size are determined by Gaussian mixture models, to the recurrent reinforcement learner and baseline momentum trader. Thus the intrinsic nature of the feature space is learnt and made available to the upstream models. The recurrent reinforcement learning trader achieves an annualised portfolio information ratio of 0.52 with compound return of 9.3\%, net of execution and funding cost, over a 7 year test set. This is despite forcing the model to trade at the close of the trading day 5pm EST, when trading costs are statistically the most expensive. These results are comparable with the momentum baseline trader, reflecting the low interest differential environment since the the 2008 financial crisis, and very obvious currency trends since then. The recurrent reinforcement learner does nevertheless maintain an important advantage, in that the model's weights can be adapted to reflect the different sources of profit and loss variation. This is demonstrated visually by a USDRUB trading agent, who learns to target different positions, that reflect trading in the absence or presence of cost.<br>

scholarly journals Reinforcement Learning for Systematic FX Trading

2021 ◽  
Author(s):  
Gabriel Borrageiro ◽  
Nick Firoozye ◽  
Paolo Barucca
Keyword(s):  
Online Learning ◽  
Reinforcement Learning ◽  
Gaussian Mixture Models ◽  
Feature Space ◽  
Gaussian Mixture ◽  
Learning Context ◽  
Information Ratio ◽  
Intrinsic Nature ◽  
The 2008 Financial Crisis ◽  
Interest Differential

We conduct a detailed experiment on major cash fx pairs, accurately accounting for transaction and funding costs. These sources of profit and loss, including the price trends that occur in the currency markets, are made available to our recurrent reinforcement learner via a quadratic utility, which learns to target a position directly. We improve upon earlier work, by casting the problem of learning to target a risk position, in an online learning context. This online learning occurs sequentially in time, but also in the form of transfer learning. We transfer the output of radial basis function hidden processing units, whose means, covariances and overall size are determined by Gaussian mixture models, to the recurrent reinforcement learner and baseline momentum trader. Thus the intrinsic nature of the feature space is learnt and made available to the upstream models. The recurrent reinforcement learning trader achieves an annualised portfolio information ratio of 0.52 with compound return of 9.3\%, net of execution and funding cost, over a 7 year test set. This is despite forcing the model to trade at the close of the trading day 5pm EST, when trading costs are statistically the most expensive. These results are comparable with the momentum baseline trader, reflecting the low interest differential environment since the the 2008 financial crisis, and very obvious currency trends since then. The recurrent reinforcement learner does nevertheless maintain an important advantage, in that the model's weights can be adapted to reflect the different sources of profit and loss variation. This is demonstrated visually by a USDRUB trading agent, who learns to target different positions, that reflect trading in the absence or presence of cost.<br>

scholarly journals Reinforcement Learning for Systematic FX Trading

2021 ◽  
Author(s):  
Gabriel Borrageiro ◽  
Nick Firoozye ◽  
Paolo Barucca
Keyword(s):  
Reinforcement Learning ◽  
Transfer Learning ◽  
Radial Basis Function Network ◽  
Gaussian Mixture ◽  
Feature Representation ◽  
Currency Markets ◽  
Learning Context ◽  
Information Ratio ◽  
Learning Agent ◽  
Inductive Transfer Learning

We explore online inductive transfer learning, with a feature representation transfer from a radial basis function network formed of Gaussian mixture model hidden processing units to a direct, recurrent reinforcement learning agent. This agent is put to work in an experiment, trading the major spot market currency pairs, where we accurately account for transaction and funding costs. These sources of profit and loss, including the price trends that occur in the currency markets, are made available to the agent via a quadratic utility, who learns to target a position directly. We improve upon earlier work by learning to target a risk position in an online transfer learning context. Our agent achieves an annualised portfolio information ratio of 0.52 with a compound return of 9.3%, net of execution and funding cost, over a 7-year test set; this is despite forcing the model to trade at the close of the trading day at 5 pm EST when trading costs are statistically the most expensive.<br>

ATOMIC HUMAN ACTION SEGMENTATION AND RECOGNITION USING A SPATIO-TEMPORAL PROBABILISTIC FRAMEWORK

2007 ◽  
Vol 01 (02) ◽  
pp. 205-220
Author(s):  
DUAN-YU CHEN ◽  
HONG-YUAN MARK LIAO ◽  
SHENG-WEN SHIH
Keyword(s):  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Human Action ◽  
Temporal Variations ◽  
Human Actions ◽  
Action Sequence ◽  
Continuous Action ◽  
Intrinsic Nature ◽  
Action Segmentation ◽  
Spatio Temporal

In this paper, a framework of automatic human action segmentation and recognition in continuous action sequences is proposed. A star figure enclosed by a bounding convex polygon is used to effectively represent the extremities of the silhouette of a human body. The human action, thus, is recorded as a sequence of the star-figure's parameters, which is used for action modeling. To model human actions in a compact manner while characterizing their spatio-temporal distributions, the star-figure's parameters are represented by Gaussian mixture models (GMM). In addition, to address the intrinsic nature of temporal variations in a continuous action sequence, we transform the time sequence of star-like figure parameters into frequency domain by discrete cosine transform (DCT) and use only the first few coefficients to represent different temporal patterns with significant discriminating power. The performance shows that the proposed framework can recognize continuous human actions in an efficient way.

scholarly journals Estimation of Gaussian mixture models via tensor moments with application to online learning

2020 ◽  
Vol 131 ◽  
pp. 285-292
Author(s):  
Donya Rahmani ◽  
Mahesan Niranjan ◽  
Damien Fay ◽  
Akiko Takeda ◽  
Jacek Brodzki
Keyword(s):  
Online Learning ◽  
Mixture Models ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture

scholarly journals Gaussian mixture models for semantic ranking in domain specific databases with application in radiology

2011 ◽  
Vol 1 (3) ◽  
Author(s):  
Adrian Barb
Keyword(s):  
Mixture Models ◽  
Dirichlet Process ◽  
Imaging Techniques ◽  
Gaussian Mixture Models ◽  
Feature Space ◽  
Gaussian Mixture ◽  
Average Precision ◽  
Domain Experts ◽  
Domain Specific ◽  
Computer Based

AbstractWith recent advances in imaging techniques, huge quantities of domain-specific images, such as medical or geospatial images, are produced and stored daily in computer-based image repositories. Size of databases and limited time at hand makes manual evaluation and annotation by domain experts difficult. In such cases computer based methods can be used to enrich the process of decision making while eliciting previously unknown information. For example, in the medical domain, query by image methods can be used by medical experts for differential diagnosis by displaying previously evaluated cases that contain similar visual patterns. Also, less experienced practitioners can benefit from query-by-semantic methods in training processes, especially for difficult-to-interpret cases with multiple pathologies. In this article we develop a methodology for ranking medical images using customized mixture models. The regions of interest are determined using Dirichlet process to determine natural groupings of images in a content-based feature space. These natural groupings of images are then evaluated for relevance to mixtures of associative semantic mappings. We evaluate and compare the performance of our method on two medical datasets using mean average precision and precision-recall charts.

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