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algos more on the less liquid
stocks—but these will be difficult
for any algo. It is hard for any algo
to adjust where liquidity follows an
episodic or erratic pattern.”
Algorithm providers have been
working on different techniques
to frustrate predators. One is
algorithmic switching. So-called
intelligent switching engines
have been used to move between
algorithms and choose the best
ones depending on market condi-
tions for a decade now. The next
generation of switching algorithms
are being developed to incorporate
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switching between axes of order
flow, in effect disappearing from
where they might have become
detectable.”
New algo regs
The company has been developing
a deep learning architecture in the
last year to drive the assignation of
new order strategies. The technol-
ogy, which is still in beta testing,
will replace the previous version
of the machine learning platform,
offering more models and with a
focus on interpreting data “through
the lens of prediction,” says Wael-
“Algo switching engines try and frustrate HFTs by
switching between axes of order flow.”
HENRI WAELBROECK, DIRECTOR OF RESEARCH, PORTWARE
artificial intelligence to harness the
vast stores of data from historical
trading logs and use this for better
execution.
Portware’s algo switching engine
(ASE) uses machine learning to
determine which of the dozens
of trading algorithms that traders
have at their fingertips is likely to
have the best performance for a
particular order under particular
market conditions.
“Our ASEs are based on an effort
to understand algo trading efficien-
cy as a function of the order flow,”
says Henri Waelbroeck, director
of research at trading platform
provider Portware. “Traders will
be looking for signs of algo activity
to see if there is a persistent buyer
or seller out there. Algo switching
engines try and frustrate HFTs by
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TheTrade
Summer 2017
broeck.
“Rather than collecting data and
making a prediction when asked,
the Portware brain is designed to
predict everything all the time,” he
says.
Others are doing similar things:
“You can infer other portfolio
managers might be doing similar
size trades and get information to
help others,” says one bank partic-
ipant who asked to remain anon-
ymous. “We are seeing improved
performance equivalent to 20bps
of expected returns in situations
where you need to switch algos
using machine learning.”
The focus on algorithmic execu-
tion comes in the glare of MiFID II
which is imposing significant new
regulatory edicts on the market.
Rule 6 of the MiFID II Regulatory
Technical Standards (RTS 6) will
require governance and testing
obligations for all investment firms
running algorithms. While this is
likely to increase the time and cost
burden on managers to test each
new broker algorithm suite the
general feeling is it should help to
manage risk associated with algo-
rithmic execution.
“RTS 6 is trying to prevent rogue
algos which have led to events
like the flash crashes,” says Horan.
“Regulators are trying to bring in
the proper regulations to register
algos and get in proper testing
which is good. It will also help cli-
ents with algo risk management.”
The positive aspects of MiFID II
on algorithmic development are
countered by continued liquidity
fragmentation and greater venue
complexity in Europe as new
market venues come into a market
already crowded with a range of
exchanges, alternative trading
system (ATS) and dark pools. Nav-
igating this is a key consideration
for future algorithmic develop-
ment. Kurek says that clients have
been asking for algorithms to be
perfected for new venue trading.
Unstoppable development
“The microstructure in Eu-
rope is changing,” says Kurek.
“The [MiFID II] rules will lead
to diminished dark pool share to
be replaced by the large-in-scale
waiver although the time to declare
trades will be shorter under MiFID
II. While the large-in-scale will
improve the way we manage in-
formation leakage and gaming the
delay in trade reporting is going
the other way.”
But while there appears to be
a definite move to develop new