Algorithmic trading review | Semantic Scholar (2024)

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@article{Treleaven2013AlgorithmicTR, title={Algorithmic trading review}, author={Philip C. Treleaven and Michal Galas and Vidhi Lalchand}, journal={Commun. ACM}, year={2013}, volume={56}, pages={76-85}, url={https://api.semanticscholar.org/CorpusID:14938340}}
  • P. Treleaven, M. Galas, V. Lalchand
  • Published in CACM 1 November 2013
  • Computer Science
  • Commun. ACM

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.

103 Citations

Highly Influential Citations

2

Background Citations

47

Methods Citations

10

Figures from this paper

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103 Citations

IS THERE STILL ROOM FOR INCREASING SPEED IN ALGORITHMIC AND HIGH-FREQUENCY TRADING? THE CASE OF EUROPEAN OPTIONS PRICED IN THE HESTON MODEL
    A. Orzechowski

    Computer Science

    Metody Ilościowe w Badaniach Ekonomicznych

  • 2019

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Offline Algorithms in Low-Frequency Trading

How well-designed software can effectively create wealth by optimizing gains from trade in combinatorial auctions is shown and a deep connection between auctions and a classic textbook problem is unveiled.

  • 1
  • PDF
Trading the Cloud: A Game-Theoretic Approach
    Xianrong Zheng

    Computer Science, Business

    AMCIS

  • 2020

A game-theoretic approach for trading cloud stocks is proposed and results show that the approach can achieve a good rate of return.

  • 1
Imitation in the Imitation Game
    R. Kashyap

    Computer Science, Economics

    ArXiv

  • 2019

A test of intelligence for trading programs, on the lines of the Turing Test, is proposed, long the benchmark for intelligent machines, to solve the dilemma in finance, which is whether, when and how much to Buy, Sell or Hold.

How Does Algorithmic Trading Influence Investor Participation in Peer-to-Peer Online Lending Markets?
    Hongchang WangEric Overby

    Economics, Business

    HICSS

  • 2020

Algorithmic trading has reshaped equity markets and had significant effects on market performance. We examine the effect of algorithmic trading in online peer-to-peer lending markets. These markets

  • 1
  • PDF
Learning Gated Bayesian Networks for Algorithmic Trading
    M. BendtsenJ. Peña

    Computer Science, Business

    Probabilistic Graphical Models

  • 2014

It is shown how using the learnt GBNs can substantially lower risks towards invested capital, while at the same time generating similar or better rewards, compared to the benchmark investment strategy buy-and-hold.

  • 8
  • PDF
GIMS—Software for asset market experiments
    Stefan Palan

    Business, Computer Science

    Journal of behavioral and experimental finance

  • 2015
  • 42
  • PDF
Algorithmic Trading in Experimental Markets with Human Traders: A Literature Survey
    T. BaoE. NekrasovaTibor NeugebauerYohanes E. Riyanto

    Computer Science

    SSRN Electronic Journal

  • 2021

The paper introduces the types and performances of algorithmic traders that interact with human subjects in the laboratory, including zero-intelligent traders, arbitragers, fundamentalists, adaptive algorithms, and manipulators.

Bayesian Optimisation of Gated Bayesian Networks for Algorithmic Trading
    M. Bendtsen

    Computer Science

    BMA@UAI

  • 2015

It is shown how algorithmic trading using GBNs, supported by Bayesian optimisation, can lower risk towards invested capital, while at the same time generating similar or better rewards, compared to the benchmark investment strategy buy-and-hold.

  • 4
  • PDF
Cognitive Trading System Model
    Ramón Martín Parrondo

    Computer Science

    SSRN Electronic Journal

  • 2019

It is hypothesize that cognitive architectures provide an adequate computational abstraction to define a model applicable to the design of trading systems in their entirety, although the model is not in itself such an architecture.

  • PDF

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22 References

Algorithmic Trading
    Giuseppe NutiMahnoosh MirghaemiP. TreleavenChaiyakorn Yingsaeree

    Computer Science, Business

    Computer

  • 2011

In electronic financial markets, algorithmic trading refers to the use of computer programs to automate one or more stages of the trading process: pretrade analysis (data analysis), trading signal

  • 28
Quantitative Trading: How to Build Your Own Algorithmic Trading Business
    Ernest P. Chan

    Computer Science, Business

  • 2008

This book discusses the business case for Quantitative Trading, how to identify a Strategy that suits you, and the importance of minimizing Transaction Costs.

  • 93
The Evaluation and Optimization of Trading Strategies
    R. Pardo

    Business, Economics

  • 2008

This book discusses the development of a Trading Strategy Design Process, the Three Principle Components of a Strategy, and the importance of accuracy in the management of risk.

  • 78
  • PDF
A Computational Social Science Environment for Financial/Economic Experiments
    M. GalasDaniel BrownP. Treleaven

    Computer Science, Economics

  • 2012

The algorithms used for automated trading and a library of algorithms developed which the author believes is unique in academia are described, which are used to study the behavior and risk of trading algorithms.

  • 6
  • PDF
Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency
    Narasimhan JegadeeshS. Titman

    Economics, Business

  • 1993

This paper documents that strategies that buy stocks that have performed well in the past and sell stocks that hav e performed poorly in the past generate significant positive returns o ver three- to

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Inside the Black Box: The Simple Truth About Quantitative Trading
    Rishi K Narang

    Economics, Business

  • 2009

This book presents a practical guide to Quantitative Trading for Investors in Quantitative Strategies and discusses the importance of data mining, analysis, and decision-making in this fast-paced environment.

  • 45
  • Highly Influential
Applied quantitative methods for trading and investment
    C. DunisJason LawsPatrick. Naïm

    Economics, Mathematics

  • 2003

About the Contributors.Preface.1. Applications of Advanced Regression Analysis for Trading and Investment (Christian L. Dunis and Mark Williams).2. Using Cointegration to Hedge and Trade

  • 49
  • PDF
Review of Statistical Arbitrage, Cointegration, and Multivariate Ornstein-Uhlenbeck
    Attilio Meucci

    Mathematics, Business

  • 2009

We introduce the multivariate Ornstein-Uhlenbeck and discuss how it generalizes a vast class of continuous-time and discrete-time multivariate processes. Relying on the simple geometrical

  • 53
Quantitative Equity Portfolio Management: An Active Approach to Portfolio Construction and Management
    Ludwig B. ChincariniDaehwan Kim

    Business, Economics

  • 2006

I An overview of QEPM 1. the Power of QEPM 2. The Fundamentals of QEPM 3. Basic QEPM Models II Portfolio Construction and Maintenance 4. Factors and Factor Choice 5. Stock Screening and Ranking 6.

  • 91
Using a Z-score Approach to Combine Value and Momentum in Tactical Asset Allocation
    Pengguo WangLawrence E. Kochard

    Economics, Business

  • 2012

This article presents several active strategies for combining value and momentum strategies in a tactical asset allocation (TAA) framework. It refines the basic yield approach to valuation by

  • 19

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    Algorithmic trading review | Semantic Scholar (2024)

    FAQs

    Does anyone actually make money with algorithmic trading? ›

    Many new traders find algorithmic trading hard to grasp and often wonder whether algorithmic traders actually make money. Yes, algorithmic traders do make money, but most of them fail to do so.

    What is the success rate of algorithmic trading? ›

    Percentage of Algorithmic Trading

    But in other markets, the percentage of algo-trading is around 80–85% of trade.

    Does algo trading really work? ›

    Since algo-trading does not require human intervention to make buying or selling decisions, algo-trades have a much higher accuracy. They are free of all human-made errors. For example, the algorithm will not misenter the quantity of units meant to be traded.

    Is there such a thing as profitable algorithmic trading? ›

    Yes, it is possible to make money with algorithmic trading. Algorithmic trading can provide a more systematic and disciplined approach to trading, which can help traders to identify and execute trades more efficiently than a human trader could.

    Who is the most successful algo trader? ›

    He built mathematical models to beat the market. He is none other than Jim Simons. Even back in the 1980's when computers were not much popular, he was able to develop his own algorithms that can make tremendous returns. From 1988 to till date, not even a single year Renaissance Tech generated negative returns.

    How much does an Algo trader make? ›

    Algorithmic Trader salary in India ranges between ₹ 2.5 Lakhs to ₹ 70.0 Lakhs with an average annual salary of ₹ 15.0 Lakhs. Salary estimates are based on 32 latest salaries received from Algorithmic Traders.

    What is the most popular algo trading strategy? ›

    Here are some effective strategies to consider for different goals.
    • Momentum Trading. ...
    • Mean Reversion. ...
    • Index Fund Rebalancing. ...
    • Arbitrage. ...
    • Black Swan Catchers. ...
    • Risk-On/Risk-Off Trading. ...
    • Inverse Volatility Trading. ...
    • 9. News-Based Trading.
    Apr 8, 2024

    Can you beat algo trading? ›

    Algo Trading Strategy You Can Beat Introduction

    Yes, they are definitely faster, but you need to understand that they simply follow a set of instructions on which they need to execute. They have a clearly defined risk profile (covering the position), size management, and steps they need to follow.

    Why is algo trading hard? ›

    Disadvantages of Algorithmic Trading

    Automated trading systems are not able to adapt to the ever-changing market conditions that only a human trader can identify and act. One of the biggest difficulties for the robots is knowing when to turn them off, or when they have outlived their lifespan.

    Can we trust algo trading? ›

    Algo trading can be trusted if it's properly designed and implemented. Since it relies on mathematical models and statistical analysis, it can reduce human errors and emotions, which often lead to poor trading decisions. However, like any technology, it's crucial to use reliable and tested algorithms.

    How much does it cost to start algorithmic trading? ›

    An algorithmic trading app usually costs about $125,000 to build. However, the total cost can be as low as $100,000 or as high as $150,000.

    Is it hard to learn algorithmic trading? ›

    Implementing algorithmic trading is difficult at first, but once you have it down, you can easily customise multiple strategies in your stock trading.

    How do you succeed in algorithmic trading? ›

    To succeed in algorithmic trading, you must thoroughly understand market dynamics, develop robust trading strategies, and continuously refine your algorithms based on data analysis and feedback.

    Can you make money with AI trading? ›

    AI technologies can assist investors in acquiring insights, assessing risk, and gauging sentiment to make informed investments. These technologies can also be utilized to make trades based on pre-set conditions and manage portfolios using guidelines and rules set by an investor.

    Is the Algorithmic Trader legit? ›

    Yes, algo trading is legit, legal and reliable if you apply a well-thought-out trading strategy and stick to good software, which allows your trading robots to work 24/7 to bring it to life.

    Is it possible to make a trading algorithm? ›

    Creating an algorithmic trading system can be challenging, as it involves multiple complex components, including financial market data analysis, statistical modeling, and risk management. It also requires a deep understanding of the financial markets and expertise in programming and mathematical modeling.

    Can you lose money with algo trading? ›

    Thus, if a trader doesn't have any coding knowledge, they will not get everything that algo trading offers. Or, they could find they lose money on a faulty algorithm.

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