IDEAS home Printed from
   My bibliography  Save this paper

Algorithmic and High-Frequency Trading Strategies: A Literature Review


  • Alexandru Mandes

    () (University of Giessen)


The advances in computer and communication technologies have created new opportunities for improving, extending the application of or even developing new trading strategies. Transformations have been observed both at the level of investment decisions, as well as at the order execution layer. This review paper describes how traditional market participants, such as market-makers and order anticipators, have been reshaped and how new trading techniques relying on ultra-low-latency competitive advantage, such as electronic “front running”, function. Also, the natural conflict between liquidity-consumers and liquidity-suppliers has been taken to another level, due to the proliferation of algorithmic trading and electronic liquidity provision strategies.

Suggested Citation

  • Alexandru Mandes, 2016. "Algorithmic and High-Frequency Trading Strategies: A Literature Review," MAGKS Papers on Economics 201625, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
  • Handle: RePEc:mar:magkse:201625

    Download full text from publisher

    File URL:
    File Function: First 201625
    Download Restriction: no

    References listed on IDEAS

    1. Sanmay Das, 2005. "A learning market-maker in the Glosten-Milgrom model," Quantitative Finance, Taylor & Francis Journals, vol. 5(2), pages 169-180.
    2. Glosten, Lawrence R. & Harris, Lawrence E., 1988. "Estimating the components of the bid/ask spread," Journal of Financial Economics, Elsevier, vol. 21(1), pages 123-142, May.
    3. Harris, Larry, 2002. "Trading and Exchanges: Market Microstructure for Practitioners," OUP Catalogue, Oxford University Press, number 9780195144703.
    4. Bialkowski, Jedrzej & Darolles, Serge & Le Fol, Gaëlle, 2008. "Improving VWAP strategies: A dynamic volume approach," Journal of Banking & Finance, Elsevier, vol. 32(9), pages 1709-1722, September.
    5. Marco Avellaneda & Sasha Stoikov, 2008. "High-frequency trading in a limit order book," Quantitative Finance, Taylor & Francis Journals, vol. 8(3), pages 217-224.
    6. Nicholas T. Chan and Christian Shelton, 2001. "An Adaptive Electronic Market-Maker," Computing in Economics and Finance 2001 146, Society for Computational Economics.
    7. Glosten, Lawrence R. & Milgrom, Paul R., 1985. "Bid, ask and transaction prices in a specialist market with heterogeneously informed traders," Journal of Financial Economics, Elsevier, vol. 14(1), pages 71-100, March.
    8. Charles Cao & Oliver Hansch & Xiaoxin Wang, 2009. "The information content of an open limit‐order book," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 29(1), pages 16-41, January.
    9. David Easley & Marcos M. López de Prado & Maureen O'Hara, 2012. "Flow Toxicity and Liquidity in a High-frequency World," Review of Financial Studies, Society for Financial Studies, vol. 25(5), pages 1457-1493.
    10. Fabrizio Lillo & J. Doyne Farmer & Rosario N. Mantegna, 2002. "Single Curve Collapse of the Price Impact Function for the New York Stock Exchange," Papers cond-mat/0207428,
    Full references (including those not matched with items on IDEAS)

    More about this item


    algorithmic trading; high-frequency trading; electronic market making;

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • G19 - Financial Economics - - General Financial Markets - - - Other

    NEP fields

    This paper has been announced in the following NEP Reports:


    Access and download statistics


    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:mar:magkse:201625. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Bernd Hayo). General contact details of provider: .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.