Please use this identifier to cite or link to this item: http://hdl.handle.net/2381/40517
Title: Optimal online two-way trading with bounded number of transactions
Authors: Fung, Stanley P. Y.
First Published: 1-Jul-2017
Presented at: COCOON 2017: Computing and Combinatorics
Publisher: Springer Verlag (Germany)
Citation: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2017, 10392 LNCS, pp. 212-223
Abstract: We consider a two-way trading problem, where investors buy and sell a stock whose price moves within a certain range. Naturally they want to maximize their profit. Investors can perform up to k trades, where each trade must involve the full amount. We give optimal algorithms for three different models which differ in the knowledge of how the price fluctuates. In the first model, there are global minimum and maximum bounds m and M. We first show an optimal lower bound of φφ (where φ=M/mφ=M/m) on the competitive ratio for one trade, which is the bound achieved by trivial algorithms. Perhaps surprisingly, when we consider more than one trade, we can give a better algorithm that loses only a factor of φ2/3φ2/3 (rather than φφ) per additional trade. Specifically, for k trades the algorithm has competitive ratio φ(2k+1)/3φ(2k+1)/3. Furthermore we show that this ratio is the best possible by giving a matching lower bound. In the second model, m and M are not known in advance, and just φφ is known. We show that this only costs us an extra factor of φ1/3φ1/3, i.e., both upper and lower bounds become φ(2k+2)/3φ(2k+2)/3. Finally, we consider the bounded daily return model where instead of a global limit, the fluctuation from one day to the next is bounded, and again we give optimal algorithms, and interestingly one of them resembles common trading algorithms that involve stop loss limits.
DOI Link: 10.1007/978-3-319-62389-4_18
ISSN: 0302-9743
ISBN: 9783319623887
eISSN: 1611-3349
Links: https://link.springer.com/chapter/10.1007%2F978-3-319-62389-4_18
http://hdl.handle.net/2381/40517
Version: Post-print
Status: Peer-reviewed
Type: Conference Paper
Rights: Copyright © 2017, Springer Verlag (Germany). Deposited with reference to the publisher’s open access archiving policy.
Appears in Collections:Published Articles, Dept. of Computer Science

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