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Authors: Haotian Weng and Artem Lenskiy

Affiliation: Research School of Computer Science, The Australian National University, Canberra, Australia

Keyword(s): Homomorphic Encryption, Quantitative Finance, Algorithmic Trading.

Abstract: Algorithmic trading has dominated the area of quantitative finance for already over a decade. The decisions are made without human intervention using the data provided by brokerage firms and exchanges. An emerging intermediate layer of financial players that are placed in between a broker and algorithmic traders has recently been introduced. The role of this layer is to aggregate market decisions from the algorithmic traders and send a final market order to a broker. In return, the quantitative analysts receive incentives proportional to the correctness of their predictions. In such a setup, the intermediate player — an aggregator — does not provide the market data in plaintext but encrypts it. Encrypting market data prevents quantitative analysts from trading on their own, as well as keeps valuable financial data private. This paper proposes an implementation of a popular trend-following indicator with two different homomorphic encryption libraries — SEAL and HEAAN — and compares it to the trading indicator implemented for plaintext. Then, an attempt to implement a trading strategy is presented and analysed. The trading indicator implemented with SEAL and HEAAN is almost identical to that implemented on the plaintext, with the percentage error of 0.14916% and 0.00020% respectively. Despite many limitations that homomorphic encryption imposes on this algorithm’s implementation, quantitative finance has a potential of benefiting from the methods of homomorphic encryption. (More)

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Paper citation in several formats:
Weng, H. and Lenskiy, A. (2020). A Trend-following Trading Indicator on Homomorphically Encrypted Data. In Proceedings of the 17th International Joint Conference on e-Business and Telecommunications - SECRYPT; ISBN 978-989-758-446-6; ISSN 2184-7711, SciTePress, pages 602-607. DOI: 10.5220/0009835706020607

@conference{secrypt20,
author={Haotian Weng. and Artem Lenskiy.},
title={A Trend-following Trading Indicator on Homomorphically Encrypted Data},
booktitle={Proceedings of the 17th International Joint Conference on e-Business and Telecommunications - SECRYPT},
year={2020},
pages={602-607},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009835706020607},
isbn={978-989-758-446-6},
issn={2184-7711},
}

TY - CONF

JO - Proceedings of the 17th International Joint Conference on e-Business and Telecommunications - SECRYPT
TI - A Trend-following Trading Indicator on Homomorphically Encrypted Data
SN - 978-989-758-446-6
IS - 2184-7711
AU - Weng, H.
AU - Lenskiy, A.
PY - 2020
SP - 602
EP - 607
DO - 10.5220/0009835706020607
PB - SciTePress

- Science and Technology Publications, Lda.
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