Algorithmic Trading

Nuti, Giuseppe; Mirghaemi, Mahnoosh; Treleaven, Philip; Yingsaeree, Chaiyakorn

doi:10.1109/mc.2011.31

Cited by 92 publications

(64 citation statements)

References 7 publications

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“…An algorithmic trading system consists of several components, some of which may be automated by a computer, and others that may be manually executed [4,5,6]. A schematic overview of the components of a general algorithmic trading system is shown in Figure 3.…”

Section: Algorithmic Tradingmentioning

confidence: 99%

Gated Bayesian networks for algorithmic trading

Bendtsen

Peña

2016

International Journal of Approximate Reasoning

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This paper introduces a new probabilistic graphical model called gated Bayesian network (GBN). This model evolved from the need to represent processes that include several distinct phases. In essence, a GBN is a model that combines several Bayesian networks (BNs) in such a manner that they may be active or inactive during queries to the model. We use objects called gates to combine BNs, and to activate and deactivate them when predefined logical statements are satisfied. In this paper we also present an algorithm for semi-automatic learning of GBNs. We use the algorithm to learn GBNs that output buy and sell decisions for use in algorithmic trading systems. We show how the learnt GBNs can substantially lower risk towards invested capital, while they at the same time generate similar or better rewards, compared to the benchmark investment strategy buy-and-hold. We also explore some differences and similarities between GBNs and other related formalisms.

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Section: Algorithmic Tradingmentioning

confidence: 99%

Gated Bayesian networks for algorithmic trading

Bendtsen

Peña

2016

International Journal of Approximate Reasoning

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“…In order to be concrete as regards the methodology, the EEFE Platform & Infrastructure will take the advantage of the embryonic version developed at the UCL centre platform and infrastructure. The Algorithmic Trading & Risk Analytics Development Environment (ATRADE) developed at UCL is a 'financial wind tunnel', originally developed for algorithmic trading but now used for risk: systematic, institutional and high-frequency (HFT) [89,90]. The ATRADE platform was initially developed to speed up the development of our algorithmic trading systems.…”

Section: Methodsmentioning

confidence: 99%

An economic and financial exploratory

Cincotti

Sornette

Treleaven

et al. 2012

Eur. Phys. J. Spec. Top.

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An economic and financial exploratoryCincotti, S.; Sornette, D.; Treleaven, P.; Battiston, S.; Caldarelli, G.; Hommes, C.H.; Kirman, A. Published in:The European Physical Journal -Special Topics DOI:10.1140/epjst/e2012-01699-6Link to publication Citation for published version (APA):Cincotti, S., Sornette, D., Treleaven, P., Battiston, S., Caldarelli, G., Hommes, C. H., & Kirman, A. (2012). An economic and financial exploratory. The European Physical Journal -Special Topics, 214(1), 361-400. DOI: 10.1140/epjst/e2012-01699-6 General rightsIt is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons). Disclaimer/Complaints regulationsIf you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: http://uba.uva.nl/en/contact, or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible. Abstract. This paper describes the vision of a European Exploratory for economics and finance using an interdisciplinary consortium of economists, natural scientists, computer scientists and engineers, who will combine their expertise to address the enormous challenges of the 21st century. This Academic Public facility is intended for economic modelling, investigating all aspects of risk and stability, improving financial technology, and evaluating proposed regulatory and taxation changes. The European Exploratory for economics and finance will be constituted as a network of infrastructure, observatories, data repositories, services and facilities and will foster the creation of a new cross-disciplinary research community of social scientists, complexity scientists and computing (ICT) scientists to collaborate in investigating major issues in economics and finance. It is also considered a cradle for training and collaboration with the private sector to spur spin-offs and job creations in Europe in the finance and economic sectors. The Exploratory will allow Social Scientists and Regulators as well as Policy Makers and the private sector to conduct realistic investigations with real economic, financial and social data. The Exploratory will (i)

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“…The rate at which an agent's profit margin converges to the observed shout price is determined by a simple version of the Widrow-Hoff delta rule for machine learning. This gives an update rule between the times of t and t + 1 as shown in (2), where Δ i,t is defined in (3).…”

Section: The Zip Algorithmmentioning

confidence: 99%

“…Since the late 1990's computers have been increasingly used as intelligent agents to trade on behalf of human operators at a much higher frequency. High frequency trading has been bolted onto pre-existing system regulation and this may enhance the effects where billions of dollars have been lost in a span of just 15 minutes [1] [2]. The root cause of some of these 'dips' such as the flash crash of May 2006 have not been unilaterally agreed on but have been classed as a systemic failure [1].…”

Section: Introductionmentioning

confidence: 99%

Automated Trading for Smart Grids: Can It Work?

Laffoy

Dave

Sooriyabandara

2014

Self-Organizing Systems

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Abstract. This paper applies basic economic principles which have been developed in financial markets to a future smart grid scenario. Our method allows for autonomous bidding for electricity units to create an emerging market price for electricity. We start with replicating the popular Zero-Intelligence-Plus algorithm and setting it in a electricity supplier-consumer scenario. We identify significant weaknesses of applying this in an electricity market especially when intermittent sources of energy are present or when the supplier to consumer ratio is very small. A new algorithm (ZIP-260) is proposed which includes a measure of fairness based on minimising the deviation across all un-matched demand for a given period. This approach means that no consumer in the system is constantly experiencing an electricity supply deficit. We show and explain how market conditions can lead to collective bargaining of consumers and monopolistic behaviour of suppliers and conclude with observations on automated trading for smart grids.

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Algorithmic Trading

Cited by 92 publications

References 7 publications

Gated Bayesian networks for algorithmic trading

Gated Bayesian networks for algorithmic trading

An economic and financial exploratory

Automated Trading for Smart Grids: Can It Work?

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