[ T H O U G H T
L E A D E R S H I P
There are several other features
that we can construct to help de-
scribe the trading strategy. The key
is to have as few features as possi-
ble while maximising information
capturing, meaning we need the
important features, but we want
to get rid of the superfluous
features.
We can use some ML tech-
niques to decide on which
features are important, so it is
better to try as many features
as possible, while only keeping
those with the best explanatory
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L I Q U I D M E T R I X ]
power.
There are other things we
can use these features for. As
an example, maybe we want to
compare the trading strategies
of two different algos, or how an
algo’s trading strategy may vary
for securities that have different
liquidity characteristics. We
don’t have to use ML to get value
out of features like these, but the
features are very useful if and
when we decide to implement an
ML program.
By computing features like
Figure 1: The set of four charts on the left show 4
trading strategies. The top left shows a front-loaded
strategy where volume is traded quickly at the begin-
ning of the order and then the pace of trading slows
down as the order progress. The top right strategy
shows a back-loaded strategy where the pace of trading
accelerates as the order is completed – like trading a
‘Close’ strategy. The bottom right strategy is uniform
in time where trading occurs at a constant pace and
the volume profile is symmetric in time. The bottom
28 // TheTrade // Summer 2019
these and then curating them, we
can build up libraries of analytics
that can help us not only apply
ML algorithms to find patterns
in our data, but also to compare
how those analytics vary through
time as market structure evolves.
It is important to keep in mind
that when our order is being
executed in the market, the
market doesn’t care if the algo
had a label, it cares instead about
the orders that interact with
the order book and potentially
become fills.
left strategy is symmetric but trades at a variable pace.
The charts on the right show the unfilled portion of the
order over time. The area under the curve represents the
‘exposure’ of the unfilled shares to opportunity risk. The
top left has a low value, the top left has a high value
and the bottom row are both intermediate. The bottom
right shows a smooth profile while the bottom right is
rougher. Our second metric, ‘roughness’ characterises
the difference of the trading strategy from a smooth
strategy.