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. http://www.quantiful.co.nz/stories/saby-machine-learning

1. 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.

2. 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.

4. 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.

5. 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 optimizable 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.

6. 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.

7. 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 strategy’s
predictions. This greatly improved the profit factor (gross
profit divided by the gross loss) of our strategy. We use a
leverage to increase or decrease the risk and expected return.

9. 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.

11. 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.

12. 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.