Machine learning methods are vastly superior in analyzing potential customer churn across data from multiple sources such as transactional, social media, and CRM sources. High performance machine learning can analyze all of a Big Data set rather than a sample of it. http://www.quantiful.co.nz/stories/saby-machine-learning
3. Machine learning is the modern science of
finding patterns and making predictions
from data based on work in multivariate
statistics, data mining, pattern recognition
and advanced/predictive analytics.
4. For example, when detecting fraud in the millisecond
it takes to swipe a credit card, machine learning rules
not only on information associated with the
transaction, such as value and location, but also by
leveraging historical and social network data for
accurate evaluation of potential fraud.
5.
6. Machine learning methods are vastly superior in analyzing
potential customer churn across data from multiple sources
such as transactional, social media, and CRM sources. High
performance machine learning can analyze all of a Big Data
set rather than a sample of it. This scalability not only allows
predictive solutions based on sophisticated algorithms to be
more accurate, it also drives the importance of software’s
speed to interpret the billions of rows and columns in real-time
and to analyze live streaming data.
8. The neural nets attempt to predict a normalized profit factor
(gross profit dividedby the gross loss) on a single trade over a
certain period in the future. The period in question can range
between 3 and 10 days, it is an optimizeable parameter of the
strategy. Therefore,our strategy doesn’t necessarily use stop
losses and take profits, instead, we open a position for a
predetermined amount of time and close the position at the
end of that period, whatever happened. The net is graded by
the percentage of correct predictions weighed by it’s
accuracy.
9.
10. There are some common pitfalls to be aware of in such
strategies where the strategy seems to offer amazing profits
but is worthless in real life. The most important precaution is
that the period on which the strategy is tested should not be
the same as the period on which it is built. Otherwise we can
simply generate thousands of complex random strategies and
choose the one that works best on one particular period, but
it’s only when we have a positive result on an independent set
of data that we can start trusting our strategy.
12. Over 161 trades, the profit factor of our strategyon the test
period is 2.87! That means we obtain 2.87 times more profit
than drawdown in trades. Although we only get 60.24%
profitable trades, they are much more profitable than the
losing trades are un-profitable. The final statistics we find very
telling is the maximum consecutive drawdown, 5%, and the
maximum consecutive profit, 18% of the equity. We have a
live account running the strategy but it has been doing so for
far too small a time period to assess it this way.
14. The volume is a great indicator for that matter; it really gives us
an insight on the moment when the way an instrument is traded
changes. On the chart below you can observe the evolution of
volume for EURUSD in the last 16 years. A strategy built using
data that is too distant doesn’t work anymore. However, our
strategy has worked equally well on EUR/USD for the last few
years and nothing hints that it will change anytime soon. There
are two things we can do to guard against a sudden change in
the way forex instruments are traded.
15. First, we can monitor the market and wait for that moment when
our strategy doesn’t work anymore using the statistics that the
strategy should follow like the maximum consecutive drawdown
and by monitoring the volume. Secondly, we can do what’s
called on-line learning where our strategy is continuously being
optimized on new data. This second option is good practice but
it doesn’t guard against the sudden changes that are typical in
forex every few years.
16. Machine learning methods are vastly
superior in analyzing potential
customer churn across data from
multiple sources such as transactional,
social media, and CRM sources.
