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hIGh-frEqUEnc Y TraDInG (HFT) has emerged as a
powerful force in modern financial markets. Only
20 years ago, most of the trading volume occurred
in exchanges such as the New York Stock Exchange,
where humans dressed in brightly colored outfits
would gesticulate and scream their trading intentions.
Today, trading occurs mostly in electronic servers in
data centers, where computers communicate their
trading intentions through network messages.
This transition from physical exchanges to electronic
platforms has been particularly profitable for HFT
firms, which invested heavily in the infrastructure
of this new environment.
Although the look of the venue
and its participants has dramatically
changed, the motivation of all traders,
whether electronic or human, remains
the same: to buy an asset from a loca-tion/trader and to sell it to another lo-cation/trader for a higher price. The
defining difference between a human
trader and an HFT is that the latter can
react faster, more frequently, and has
very short portfolio holding periods. A
typical HFT algorithm operates at the
sub-millisecond time scale, where human traders cannot compete, as the
blink of a human eye takes approximately 300 milliseconds. As HFT algorithms compete with each other, they
face two challenges:
online
Algorithms
in high-
frequency
trading
Doi: 10.1145/2507771.2507780
Article development led by
queue.acm.org
The challenges faced by
competing HFT algorithms.
BY JACoB LoVeLess, sAshA stoiKoV, AnD RoLf WAeBeR
