Automated Forex Trading systems do have advantages over manual
human trading. Automated trading system can monitor the Forex markets 24
hours a day; automated systems are completely disciplined to the set of
system rules and never stray; automated trading systems are immune to
greed and fear and emotion never influences their trading decisions;
automated Forex systems always follow the money management rules defined
by the user. However, it is apparently very ironic that these basic
principles, that define the strengths of a system, are also many times
its downfall. Forex robots cannot 'analyze' the market price action like
a human being. Therefore, Forex Robots Enter Every Trade that meets a defined set of conditions. Human Traders Most Often Do Not!
Prevailing
sentiment contends that, out of all Forex traders, only a small
percentage are successful long term. The referenced figures vary
depending on the source cited, but the percentages consistently average
in the 5% to 8% range. In alignment with this figure, very few Forex
robots survive the tests of live account Forex trading, with a mere 1%
to 2% surviving more than a few months prior to their rule-sets becoming
obsolete, and the losses begin piling up. The ideal solution is
obvious. Combine the discipline and tireless availability of an
automated Forex robot with the savvy and experience of a successful
human trader.
It is in this vein that much of the groundbreaking
research on algorithmic Forex trading lies. By utilizing machine
learning to 'teach' an algorithm certain prevailing 'human' decisions
that affect trade entry, existing systems for trading Forex
automatically can be converted. Some research shows that training entry
tactics with machine learning strategies (Genetic Programming and Neural
Networks to name a few) do significantly improve the performance of
systems on out-of-sample data. These conclusions lend some early
credibility to the notion of Forex trading using machine learning.
The
concept that we discuss here departs from this strategy in that we use
the learning technologies to train sets of 'humanized' data as opposed
to raw data prior to a condition. By utilizing these datasets, the
learning becomes 'why did the human enter this trade?' vs. 'do the raw
data support entering a trade right now?' When the learning begins to
focus on more abstract data, the resulting systems tend to become more
robust, or tend to work better in varying market conditions than those
that simply attempt to identify winning Forex trades from raw indicator
data. The concept is that basic indicator conditions trigger a trade
Set-Up, for instance, a fast moving average crosses a slower moving
average. The learning algorithm then works to filter these set-ups using
the training it acquired from human training datasets. The automated
trading system says, "Based on what I've learned from my expert human
teacher, does this set-up look like a good deal?" Instead of, "The
computational result using all of the empirical data is greater than the
defined variable, get in or out?"
In summary, applying machine
learning strategies to teach 'human' tactics for automated Forex trading
system design, can be much more effective in producing robust Forex
systems than by utilizing the technologies in an attempt to forecast
market direction. In future articles I will expand on this method and
provide information on applications and technologies available to employ
these concepts.
B. Thomas Allen - Utilizing machine learning technologies in the
application of financial time-series forecasting. Innovating the modern
model of financial analysis.
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