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Optimal online two-way trading with bounded number of transactions

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conference contribution
posted on 2017-11-09, 14:26 authored by Stanley P. Y. Fung
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.

History

Citation

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2017, 10392 LNCS, pp. 212-223

Author affiliation

/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/Department of Informatics

Source

COCOON 2017: Computing and Combinatorics

Version

  • AM (Accepted Manuscript)

Published in

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Publisher

Springer Verlag (Germany)

issn

0302-9743

eissn

1611-3349

isbn

9783319623887

Copyright date

2017

Available date

2017-11-09

Publisher version

https://link.springer.com/chapter/10.1007/978-3-319-62389-4_18

Language

en

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