Abstract
Abstract
Quantitative trading is an integral part of financial markets with high calculation speed requirements, while no quantum algorithms have been introduced into this field yet. We propose quantum algorithms for high-frequency statistical arbitrage trading by utilizing variable time condition number estimation and quantum linear regression. The algorithm complexity has been reduced from the classical benchmark O(N
2
d) to
O
(
d
N
κ
0
2
log
(
1
/
ϵ
)
2
)
, where N is the length of trading data, and d is the number of stocks, κ
0 is the condition number and ϵ is the desired precision. Moreover, two tool algorithms for condition number estimation and cointegration test are developed.
Funder
Innovation Program for Quantum Science and Technology
the National Natural Science Foundation of China
Subject
General Physics and Astronomy
Cited by
6 articles.
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