High Frequency Trading with Complex Event Processing

Ajay, Acharya; Sidnal, Nandini

doi:10.1109/hipcw.2016.014

Cited by 4 publications

(3 citation statements)

References 4 publications

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“…Few studies have provided specific details for low-latency event triggered system design. For a theoretical framework, we may refer to Acharya and Sidnal (2016), where an HFT architecture with complex event identification and processing is proposed.…”

Section: Systems Based On Textual Analysismentioning

confidence: 99%

Automated trading systems statistical and machine learning methods and hardware implementation: a survey

Huang

Huan

et al. 2018

Enterprise Information Systems

110

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Automated trading, which is also known as algorithmic trading, is a method of using a predesigned computer program to submit a large number of trading orders to an exchange. It is substantially a real-time decision-making system which is under the scope of Enterprise Information System (EIS). With the rapid development of telecommunication and computer technology, the mechanisms underlying automated trading systems have become increasingly diversified. Considerable effort has been exerted by both academia and trading firms towards mining potential factors that may generate significantly higher profits. In this paper, we review studies on trading systems built using various methods and empirically evaluate the methods by grouping them into three types: technical analyses, textual analyses and highfrequency trading. Then, we evaluate the advantages and disadvantages of each method and assess their future prospects.

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Section: Systems Based On Textual Analysismentioning

confidence: 99%

Automated trading systems statistical and machine learning methods and hardware implementation: a survey

Huang

Huan

et al. 2018

Enterprise Information Systems

110

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“…Stream processing applications are widely used in the industry to perform both real-time and offline analysis on unbounded, timeordered, and high-frequency event streams. For example, social media companies (e.g., Twitter, Meta) use click stream analytics for serving advertisements [26,49], banking institutions analyze purchasing trends for identifying fraudulent transactions [58], and investment companies conduct high-frequency trading based on real-time stock prices [1]. In recent years, stream processing is finding even wider application in non-traditional areas like agriculture [51], climate science [5], energy/manufacturing industry [42], and healthcare [19].…”

Section: Introductionmentioning

confidence: 99%

TiLT: A Time-Centric Approach for Stream Query Optimization and Parallelization

Jayarajan

Zhao

Sun

et al. 2023

Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems,

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Stream processing engines (SPEs) are widely used for large scale streaming analytics over unbounded time-ordered data streams. Modern day streaming analytics applications exhibit diverse compute characteristics and demand strict latency and throughput requirements. Over the years, there has been significant attention in building hardware-efficient stream processing engines (SPEs) that support several query optimization, parallelization, and execution strategies to meet the performance requirements of large scale streaming analytics applications. However, in this work, we observe that these strategies often fail to generalize well on many real-world streaming analytics applications due to several inherent design limitations of current SPEs. We further argue that these limitations stem from the shortcomings of the fundamental design choices and the query representation model followed in modern SPEs. To address these challenges, we first propose TiLT, a novel intermediate representation (IR) that offers a highly expressive temporal query language amenable to effective query optimization and parallelization strategies. We subsequently build a compiler backend for TiLT that applies such optimizations on streaming queries and generates hardware-efficient code to achieve high performance on multi-core stream query executions. We demonstrate that TiLT achieves up to 326× (20.49× on average) higher throughput compared to state-of-the-art SPEs (e.g., Trill) across eight real-world streaming analytics applications. TiLT source code is available at https://github.com/ampersand-projects/tilt.git.

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“…Gradually many well-tested responses and reliable recommendations can be made automatic. Practical agile applications in specific areas include banking fraud detection (Nguyen, Schiefer, & Tjoa, 2005), algorithmic stock trading (Acharya & Sidnal, 2016), B2B eService recommendation system (Shambour & Lu, 2012), traffic management (Dunkel, Fernández, Ortiz, & Ossowski, 2011), etc.…”

Section: Ops Agilitymentioning

confidence: 99%

In A Quest to Solve Information System Agility Problems : A SaaS Experience

Yang¹

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<p>Change is endemic in modern business competition. In an age of globalisation, with the rapid development of information technologies (IT), changes occur at a much faster pace, and are also more unpredictable. Being agile in a turbulent environment has been ranked highly by executives in surveys of business issues conducted in past five years. Today nearly all organisations rely on information systems (IS) to operate. IS Agility is critical in achieving overall agility and performance in business. However, despite the strong interest from the practitioner community, IS Agility in academia has often been perceived as an overly abstract concept that is difficult to study. Resultantly, despite its importance, little has been published on how to systematically solve IS Agility problems with theoretical rigour and practical relevance. This “how to” question is a challenging one to researchers and is the major motivation of the present study. A key difficulty to study IS Agility is the lack of a solid conceptualisation. In this thesis, based on a multidisciplinary literature review looking for a unified theory of IS Agility, we proposed the IS Agility Nomological Network (ISANN) as a holistic conceptualisation to be used for problem solving. Such a conceptualisation includes an IS Agility Cycle illustrating four stages (i.e. Sense, Diagnose, Select, and Execute) of the dynamic interactions between IS and its human agents (e.g. IS users and IS developers), a decision tree presenting four main IS Agility drivers (i.e. Change Frequency, Uncertainty, Information Intensity, and Time Criticality), and a pyramid incorporating four IS Agility Providers (i.e. Agile System-Development, Agile-System Architecture, Agile System-Operation, and Agile-System Configuration ). We classify IS Agility as having at least two sides, Dev Agility and Ops Agility. The former represents the agility of IS development function while the later refers to the IS operation function. We believe they are not the same, as agility in system development process doesn’t necessarily translate to agility in the resulting system operation. To be able to answer the “how to” question and design a systematic problem-solving approach, we then operationalised ISANN by developing data and task models in real-world settings. These models were used to investigate and analyse IS Agility problems faced by Software as a Service (SaaS) adopters. Such a SaaS environment, due to its multi-tenancy nature, provides a great opportunity to observe the interactions and trade-offs between Dev Agility (e.g. stories from engineers and developers) and Ops Agility (e.g. stories from operators and users), as well as an abundant source of IS Agility related business problems. Eventually, more elements and factors emerged from this SaaS practice and were merged into the final artefact created in this study: ISACAM (Information System Agility Causation Analysis Method). ISACAM incorporates all the dimensions and facts derived from the theoretical conceptualisation and the ongoing real-world problem-solving practice. The effectiveness of ISACAM in solving IS Agility problems has been observed through improved performance in real-life businesses. Furthermore, five technological rules have been synthesised to offer a prescription for designing solutions to improve IS Agility.</p>

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High Frequency Trading with Complex Event Processing

Cited by 4 publications

References 4 publications

Automated trading systems statistical and machine learning methods and hardware implementation: a survey

Automated trading systems statistical and machine learning methods and hardware implementation: a survey

TiLT: A Time-Centric Approach for Stream Query Optimization and Parallelization

In A Quest to Solve Information System Agility Problems : A SaaS Experience

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