Harnessing more intelligent FX Algorithms for superior order management and strategy execution
to reduce the implementation time involved. But for
these benefits there is a cost. The simplified algorithm
is less able to remain useful as the dynamics of the
system measured changes. For FX algorithms this
means less accuracy, less profits,”says Gula.
The growing number of people that want to use
algorithms for trading are getting used to needing
to change tactics, plans and tools more often than
they ever have before he says, and “as a consequence,
algorithms are being created, used when successful,
and then discarded when they get stale. Therefore,
there is a boom in demand for algorithms as each
algorithm ‘hit’ is less able to satisfy the customer for as
long as the last ‘hit’.”
“At Argyle, we believe that the constant moving on
Rich Mooreto the next best tool can be counter productive in
“Negative momentum termination models ‘push’ algosthe long run,” says Moore. “Instead, each algorithm
to execute into weakness, especially, where liquidity oftenshould be maintained and its success rate should
improves as more emotional sellers panic sell.”continue to be evaluated and correlated to whatever
larger attributes can be associated with market
conditions.The ‘best practice’ end-case would evaluate to execute into weakness, especially, where liquidity
market conditions, determine which algorithms are often improves as more emotional sellers panic sell.
best for the conditions, and then spread investment The opposite of course, works to some degree, but is
activity across those algorithms based on their relative more challenging to measure.”
success rates,” he says. With this approach, returns
have the best chance to be maximized and risks Cross-correlation models may help to highlight pairs
reduced says Moore. “We have found, however, that for trading, with a reliable expectation that if your move
market directional strategies add considerable value. them, liquidity will come, says Gula. “Algos that seek
Negative momentum termination models ‘push’ algos volatility may have better batting averages than those
that ignore volatility, or see it as a necessary evil. Price
rotation algos often show good performance after a sharp
price move over a limited time, like two recent February
events, the Chinese bank reserve announcement at 5am
EST, or the US Fed Reserve rate uptick at 5 pm EST.
Both produced very large magnitude moves in major
currency pairs like the EURUSD and the so-called
backwash became very tradeable.
“We wonder who got caught in these large and fast
moves but we doubt there are reliable algos that will
anticipate such announcements, although the old
days of primary desk trading might have purported to
have inside information on such moves and profited
from them. Lacking such information access, volatility
tends to increase at such announcements, and the
cross-correlation models do their stuff to focus trading
on the most volatile pair, assuming liquidity willRich Gula
follow. Ultimately, we feel that the most fruitful area“Even more challenging is that the basic dynamics
for research and trading models emphasise marketof the markets are changing faster than ever.
parameters like momentum, velocity, and futuresThis means that the average useful lifetime of a
trading factors derived from the equity futures marketsgiven algorithm can be shorter than ever.”
where ‘gaming’ is always part of the fun,” says Gula.
|april 2010 e -FOREX 85
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