Article

Competitive algorithms for VWAP and limit order trading

Authors:

Sham M. Kakade,

Michael Kearns,

Yishay Mansour,

Luis E. OrtizAuthors Info & Claims

EC '04: Proceedings of the 5th ACM conference on Electronic commerce

Pages 189 - 198

https://doi.org/10.1145/988772.988801

Published: 17 May 2004 Publication History

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Abstract

We introduce new online models for two important aspectsof modern financial markets: Volume Weighted Average Pricetrading and limit order books. We provide an extensivestudy of competitive algorithms in these models and relatethem to earlier online algorithms for stock trading.

References

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[2]

A. Borodin and R. El-Yaniv. Online Computation and Competitive Analysis. Cambridge University Press, 1998.

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[3]

R. El-Yaniv, A. Fiat, R. M. Karp, and G. Turpin. Optimal search and one-way trading online algorithms. Algorithmica, 30:101--139, 2001.

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M. Kearns and L. Ortiz. The Penn-Lehman automated trading project. IEEE Intelligent Systems, 2003. To appear.

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Cited By

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Li XWu PZou CLi Q(2024)Hierarchical Deep Reinforcement Learning for VWAP Strategy OptimizationIEEE Transactions on Big Data10.1109/TBDATA.2023.333801110:3(288-300)Online publication date: Jun-2024
https://doi.org/10.1109/TBDATA.2023.3338011
Byun WChoi BKim SJo J(2023)Practical Application of Deep Reinforcement Learning to Optimal Trade ExecutionFinTech10.3390/fintech20300232:3(414-429)Online publication date: 29-Jun-2023
https://doi.org/10.3390/fintech2030023
Sun SWang RAn B(2023)Reinforcement Learning for Quantitative TradingACM Transactions on Intelligent Systems and Technology10.1145/358256014:3(1-29)Online publication date: 24-Mar-2023
https://dl.acm.org/doi/10.1145/3582560
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Index Terms

Competitive algorithms for VWAP and limit order trading
1. Applied computing
  1. Law, social and behavioral sciences
    1. Economics
2. Theory of computation
  1. Design and analysis of algorithms

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Published In

EC '04: Proceedings of the 5th ACM conference on Electronic commerce

May 2004

278 pages

ISBN:1581137710

DOI:10.1145/988772

General Chair:
Jack Breese
Microsoft
,
Program Chairs:
Joan Feigenbaum
Yale University
,
Margo Seltzer
Harvard University

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 ACM 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]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 May 2004

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Conference

EC04

Sponsor:

EC04: Fifth ACM Conference on Electronic Commerce 2004 ( held in conjunction with WWW'04: The 2004 World Wide Web Conference )

May 17 - 20, 2004

NY, New York, USA

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EC '04 Paper Acceptance Rate 24 of 146 submissions, 16%;

Overall Acceptance Rate 664 of 2,389 submissions, 28%

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Li XWu PZou CLi Q(2024)Hierarchical Deep Reinforcement Learning for VWAP Strategy OptimizationIEEE Transactions on Big Data10.1109/TBDATA.2023.333801110:3(288-300)Online publication date: Jun-2024
https://doi.org/10.1109/TBDATA.2023.3338011
Byun WChoi BKim SJo J(2023)Practical Application of Deep Reinforcement Learning to Optimal Trade ExecutionFinTech10.3390/fintech20300232:3(414-429)Online publication date: 29-Jun-2023
https://doi.org/10.3390/fintech2030023
Sun SWang RAn B(2023)Reinforcement Learning for Quantitative TradingACM Transactions on Intelligent Systems and Technology10.1145/358256014:3(1-29)Online publication date: 24-Mar-2023
https://dl.acm.org/doi/10.1145/3582560
Tatsumura KHidaka RNakayama JKashimata TYamasaki M(2023)Real-Time Trading System Based on Selections of Potentially Profitable, Uncorrelated, and Balanced Stocks by NP-Hard Combinatorial OptimizationIEEE Access10.1109/ACCESS.2023.332681611(120023-120033)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3326816
Ye JLi XWang Y(2022)A Multi-Agent Deep Reinforcement Learning Framework for VWAP Strategy Optimization2022 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN55064.2022.9892166(1-8)Online publication date: 18-Jul-2022
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Zheng DZhang JWang XGuo XXiao J(2022)Dual Feature Fusion Trade Execution Framework with DDQN2022 4th International Conference on Data Intelligence and Security (ICDIS)10.1109/ICDIS55630.2022.00073(442-446)Online publication date: Aug-2022
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Jeong SLee HNam HOh K(2021)Using a Genetic Algorithm to Build a Volume Weighted Average Price Model in a Stock MarketSustainability10.3390/su1303101113:3(1011)Online publication date: 20-Jan-2021
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Zhao MLinetsky VCalinescu ASzpruch L(2021)High frequency automated market making algorithms with adverse selection risk control via reinforcement learningProceedings of the Second ACM International Conference on AI in Finance10.1145/3490354.3494398(1-9)Online publication date: 3-Nov-2021
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Lin SBeling P(2021)Investigating the Robustness and Generalizability of Deep Reinforcement Learning Based Optimal Trade Execution SystemsIntelligent Computing10.1007/978-3-030-80126-7_64(912-926)Online publication date: 7-Jul-2021
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Zhang MZhang JCheng THua G(2020)How do Normal Traders and Sharp Traders Make Profits in the Chinese Security Market?Journal of Systems Science and Systems Engineering10.1007/s11518-019-5442-829:2(203-234)Online publication date: 11-Apr-2020
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