Ultimate Algorithmic Trading System

TradeStation’s and AmiBroker’s genetic optimization tool is a very important
tool in the development of robust trading algorithms. Hopefully, this first part has
demystified genetic algorithms and provided sufficient evidence that it can help a
trader develop a better mousetrap—one that catches the mouse more often than not.
■ Walk-Forward Optimizer: Is It Worth the Extra
Work and Time?
The idea of periodic reoptimization of a trading system’s parameters is the one
concept that divides the algorithmic trading community more so than any other. You
are either pro or con; there is very little middle ground. Walk-forward optimization
(WFO) has been around since the beginning of trading algorithms but until lately it
has been extremely difficult to implement. TradeStation has done a wonderful job
in providing the TS walk-forward optimizer. It does all the work for you.
Before describing the potential benefits of WFO, the concept and process needs
to be explained. In a nutshell, it is a technique in which a system’s parameters
are optimized on a segment of historical data (in-sample), then tested and verified
by using those parameters on a walk-forward basis (out-of-sample). Results from
in-sample testing will usually be very good due to the benefit of hindsight. It’s the
results from testing on unseen data that provides a true reflection of a system’s
validity. This process is done periodically in hopes the trading algorithm will be
using the most optimal parameters available at any given time.
WFO Example 1
Another example will definitely help explain the concept. Take a Turtle-like system
that buys at the highest high of the past 40 days and sells at the lowest low of the
past 40 days. Throughout history, there have been times when a longer (greater
than 40-day) breakout or a shorter (less than 40-day) worked better. The magic
questions are, of course, when to change the length of the breakouts, and by how
much? These questions are easily answered with the use of a time machine.
Assume a trader trades this Turtle-like system for two years and it is somewhat
profitable. Being an inquisitive trader he asks, ‘‘Was a 40-day break the best option?’’
An optimization run is set up and the 40-day parameter is optimized over the past
two years of data, and lo and behold, the trader finds out that a 30-day breakout
produced twice as much profit with much less drawdown. With this knowledge, he
changes the trading algorithm to utilize a 30-day breakout in place of the 40-day.
Another two profitable years go by, and he again asks the same question. This
time, the best parameter turned out be the exact one he had implemented; 30
turned out to be the optimal parameter. He then carries the parameter forward
another two years, but this time he suffers a large loss and a severe drawdown.
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GENETIC OPTIMIZATION, WALK FORWARD
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