  Financial organisations such as brokers face a significant challenge in
servicing the investment needs of thousands of their traders worldwide. This
task is further compounded since individual traders will have their own risk
appetite and investment goals. Traders may look to capture short-term trends in
the market which last only seconds to minutes, or they may have longer-term
views which last several days to months. To reduce the complexity of this task,
client trades can be clustered. By examining such clusters, we would likely
observe many traders following common patterns of investment, but how do these
patterns vary through time? Knowledge regarding the temporal distributions of
such clusters may help financial institutions manage the overall portfolio of
risk that accumulates from underlying trader positions. This study contributes
to the field by demonstrating that the distribution of clusters derived from
the real-world trades of 20k Foreign Exchange (FX) traders (from 2015 to 2017)
is described in accordance with Ewens' Sampling Distribution. Further, we show
that the Aggregating Algorithm (AA), an on-line prediction with expert advice
algorithm, can be applied to the aforementioned real-world data in order to
improve the returns of portfolios of trader risk. However we found that the AA
'struggles' when presented with too many trader ``experts'', especially when
there are many trades with similar overall patterns. To help overcome this
challenge, we have applied and compared the use of Statistically Validated
Networks (SVN) with a hierarchical clustering approach on a subset of the data,
demonstrating that both approaches can be used to significantly improve results
of the AA in terms of profitability and smoothness of returns.
