research-article

Free access

Algorithmic trading review

Authors:

Philip Treleaven,

Michal Galas,

Vidhi LalchandAuthors Info & Claims

Communications of the ACM, Volume 56, Issue 11

Pages 76 - 85

https://doi.org/10.1145/2500117

Published: 01 November 2013 Publication History

All formats PDF

Abstract

The competitive nature of AT, the scarcity of expertise, and the vast profits potential, makes for a secretive community where implementation details are difficult to find.

References

[1]

Chan, E.P. Quantitative Trading: How to Build Your Own Algorithmic Trading Business. Wiley Trading, 2009.

Google Scholar

[2]

Che, S., Li, J., Sheaffer, J., Skadron, K. and Lach, J. Accelerating compute-intensive applications with GPUs and FPGAs. In Proceedings of the IEEE Symposium on Application Specific Processors (June 2008).

Digital Library

Google Scholar

[3]

Chincarini, L. and Kim, D. Quantitative Equity Portfolio Management: An Active Approach to Portfolio Construction and Management. McGraw-Hill Library of Investment & Finance, 2006.

Google Scholar

[4]

Dunis, C. L., Laws J. and Naïm P. Applied Quantitative Methods for Trading and Investment. Wiley Finance Series, 2003.

Google Scholar

[5]

Ehrman, D.S. The Handbook of Pairs Trading: Strategies Using Equities, Options, and Futures. Wiley Trading, 2006.

Google Scholar

[6]

Financial Information eXchange (FIX) Protocol; http://en.wikipedia.org/wiki/Financial_Information_eXchange or http://fixprotocol.org/

Google Scholar

[7]

Galas, M., Brown, D. and Treleaven, P. A computational social science environment for financial/economic experiments. In Proceedings of the Computational Social Science Society of the Americas (2012).

Google Scholar

[8]

Jegadeesh, N. and Titman, S. Returns to buying winners and selling losers: Implications for stock market efficiency. J. Finance 48, 1 (Mar. 1993), 65--91.

Crossref

Google Scholar

[9]

Johnson, B. Algorithmic trading & DMA: An introduction to direct access trading strategies, 2010.

Google Scholar

[10]

Kaufman, P.J. The New Trading Systems and Methods. Wiley Trading, 2005.

Google Scholar

[11]

Lo, A. Finance is in need of a technological revolution. Financial Times, Aug. 27, 2012.

Google Scholar

[12]

Meucci, A. Review of Statistical Arbitrage, Cointegration, and Multivariate Ornstein-Uhlenbeck. SSRN preprint 1404905, 2010.

Google Scholar

[13]

Narang, R.K. Inside the Black Box: The Simple Truth About Quantitative Trading. Wiley Finance, 2009.

Crossref

Google Scholar

[14]

Nuti, G., Mirghaemi, M., Treleaven, P. and Yingsaeree, C. Algorithmic trading. IEEE Computer 44, 11 (Nov. 2011), 61--69.

Digital Library

Google Scholar

[15]

Pardo, R. The Evaluation and Optimization of Trading Strategies, Wiley Trading 2008.

Google Scholar

[16]

Stokes, E. Market Neutral Investing. Dearborn Trade Publishing, 2004.

Google Scholar

[17]

Wang, P. and Kochard, L. Using a Z-score Approach to Combine Value and Momentum in Tactical Asset Allocation. SSRN preprint 1726443, 2011.

Google Scholar

[18]

Zigrand, J.P., Cliff, D. and Hendershott, T. Financial stability and computer based trading. The Future of Computer Trading in Financial Markets, U.K. Gov., 2011.

Google Scholar

Cited By

View all

Singh P(2025)Exploring Leading Practices in Algorithmic TradingMachine Learning and Modeling Techniques in Financial Data Science10.4018/979-8-3693-8186-1.ch006(151-184)Online publication date: 31-Jan-2025
https://doi.org/10.4018/979-8-3693-8186-1.ch006
Jain VMitra A(2025)Ensuring Compliance and Ethical Standards with Generative AI in FintechGenerative Artificial Intelligence in Finance10.1002/9781394271078.ch13(253-264)Online publication date: 21-Jan-2025
https://doi.org/10.1002/9781394271078.ch13
Cohen G(2024)Polynomial Moving Regression Band Stocks Trading SystemRisks10.3390/risks1210016612:10(166)Online publication date: 18-Oct-2024
https://doi.org/10.3390/risks12100166
Show More Cited By

Index Terms

Algorithmic trading review

Recommendations

Secondary spectrum trading: auction-based framework for spectrum allocation and profit sharing

Recently, dynamic spectrum sharing has been gaining interest as a potential solution to scarcity of available spectrum. We investigate the problem of designing a secondary spectrum-trading market when there are multiple sellers and multiple buyers and ...
Power trading in cognitive radio networks

Cognitive radio technology has been proposed to improve spectrum utilization by sharing the frequency spectrum bands between licensed and unlicensed users, called primary users (PUs) and secondary users (SUs), respectively. The main objective of the SUs ...
Spectrum Trading in Cognitive Radio Networks: A Contract-Theoretic Modeling Approach

Cognitive radio is a promising paradigm to achieve efficient utilization of spectrum resource by allowing the unlicensed users (i.e., secondary users, SUs) to access the licensed spectrum. Market-driven spectrum trading is an efficient way to achieve ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

Communications of the ACM Volume 56, Issue 11

November 2013

97 pages

ISSN:0001-0782

EISSN:1557-7317

DOI:10.1145/2524713

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 November 2013

Published in CACM Volume 56, Issue 11

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Qualifiers

Research-article
Popular
Refereed

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

85
Total Citations
View Citations
132,430
Total Downloads

Downloads (Last 12 months)1,284
Downloads (Last 6 weeks)190

Reflects downloads up to 18 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Singh P(2025)Exploring Leading Practices in Algorithmic TradingMachine Learning and Modeling Techniques in Financial Data Science10.4018/979-8-3693-8186-1.ch006(151-184)Online publication date: 31-Jan-2025
https://doi.org/10.4018/979-8-3693-8186-1.ch006
Jain VMitra A(2025)Ensuring Compliance and Ethical Standards with Generative AI in FintechGenerative Artificial Intelligence in Finance10.1002/9781394271078.ch13(253-264)Online publication date: 21-Jan-2025
https://doi.org/10.1002/9781394271078.ch13
Cohen G(2024)Polynomial Moving Regression Band Stocks Trading SystemRisks10.3390/risks1210016612:10(166)Online publication date: 18-Oct-2024
https://doi.org/10.3390/risks12100166
Huang YZhou CZhang LLu X(2024)A Self-Rewarding Mechanism in Deep Reinforcement Learning for Trading Strategy OptimizationMathematics10.3390/math1224402012:24(4020)Online publication date: 22-Dec-2024
https://doi.org/10.3390/math12244020
Zhou CHuang YCui KLu X(2024)R-DDQN: Optimizing Algorithmic Trading Strategies Using a Reward Network in a Double DQNMathematics10.3390/math1211162112:11(1621)Online publication date: 22-May-2024
https://doi.org/10.3390/math12111621
Liu TWanvarie D(2024)Enhancing Financial Market Predictions in Taiwan: A Hybrid Approach of Traditional Analysis and Machine Learning Techniques2024 5th Technology Innovation Management and Engineering Science International Conference (TIMES-iCON)10.1109/TIMES-iCON61890.2024.10630738(1-5)Online publication date: 19-Jun-2024
https://doi.org/10.1109/TIMES-iCON61890.2024.10630738
Tudor CSova R(2024)Enhancing Trading Decision in Financial Markets: An Algorithmic Trading Framework With Continual Mean-Variance Optimization, Window Presetting, and Controlled Early-StoppingIEEE Access10.1109/ACCESS.2024.341781512(87633-87644)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3417815
Park JKim JHuh J(2024)Deep Reinforcement Learning Robots for Algorithmic Trading: Considering Stock Market Conditions and U.S. Interest RatesIEEE Access10.1109/ACCESS.2024.336103512(20705-20725)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3361035
Koo J(2024)AI is not careful: approach to the stock market and preference for AI advisorInternational Journal of Bank Marketing10.1108/IJBM-10-2023-0568Online publication date: 15-Jul-2024
https://doi.org/10.1108/IJBM-10-2023-0568
Dakalbab FTalib MNasir QSaroufil T(2024)Artificial intelligence techniques in financial trading: A systematic literature reviewJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2024.10201536:3(102015)Online publication date: Mar-2024
https://doi.org/10.1016/j.jksuci.2024.102015
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Digital Edition

View this article in digital edition.

Digital Edition

Magazine Site

View this article on the magazine site (external)

Magazine Site

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Abstract

References

Cited By

Index Terms

Recommendations

Secondary spectrum trading: auction-based framework for spectrum allocation and profit sharing

Power trading in cognitive radio networks

Spectrum Trading in Cognitive Radio Networks: A Contract-Theoretic Modeling Approach

Comments

Information

Published In

Publisher

Publication History

Permissions

Check for updates

Qualifiers

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

View options

PDF

eReader

Digital Edition

Magazine Site

Login options

Full Access

Share

Share this Publication link

Share on social media

Affiliations