Machine learning can help us optimize automatic trading strategies..

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Machine learning can help us optimize automatic
trading strategies..
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• 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.

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Machine learning methods are particularly
effective in situations where deep and predictive
insights need to be uncovered from data sets that
are large, diverse and fast changing Big Data.
Across these types of data, machine learning
easily outperforms traditional methods on
accuracy, scale, and speed. 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.
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• 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 softwares speed to
  interpret the billions of rows and columns in
  real-time and to analyze live streaming data.

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Automatically finding a winning speculative
strategy on eurusd
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
doesnt 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 its accuracy.
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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 its
only when we have a positive result on an
independent set of data that we can start
trusting our strategy.
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An optimal strategy tested with a recognized
simulator
Our strategy obtains a theoretical 62.5 correct
bets on EUR/USD. But we can obtain a better
assessment of the strategy with a good simulation
and a real life application of the strategy. For
this reason we implemented the strategy using the
JForex API and tested it on the jForex platform.
Once again, we were careful not to mix the period
we used to optimize our strategy and the period
we used to test it. We also refined our strategy
some more adjusting the amount invested on each
position to reflect the strategys predictions.
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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.
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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
doesnt 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.
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First, we can monitor the market and wait for
that moment when our strategy doesnt work
anymore using the statistics that the strategy
should follow like the maximum consecutive
drawdown and by monitoring the volume. Secondly,
we can do whats called on-line learning where
our strategy is continuously being optimized on
new data. This second option is good practice but
it doesnt guard against the sudden changes that
are typical in forex every few years.
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Machine learning methods are vastly superior in
analyzing potential customer churn across data
from multiple sources such as transactional,
social media, and CRM sources.
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Learn More www.quantiful.co.nz/stories/saby-ma
chine-learning