optimizing the systems, but rather trying to find the most robust solution that
should work on average equally as well on as many markets as possible, without
really taking the actual profitability into question until the very last moment.
Second, the little “optimizing” I do (if you still call it that), is very coarse and
based more on my own reasoning and understanding of the systems’ underlying
logic than on actually stepping through each and every input combination. Finally,
the systems are tested on as many as 65 different markets, and I don’t care which
markets the systems are profitable on or not, or on which markets the systems will
be traded in the future.
There are ways to decide that as well, among which one major way is to study
the correlation between all systems and markets and the portfolio as a whole. We
will talk more about this in Part 4, so for now I will only say that sometimes it is
a good thing to trade a losing market. More specifically, when the losing market
still behaves in a way that it performs well when the portfolio as a whole performs
badly, it will decrease the risk for the overall portfolio and sometimes even add
positively to the bottom line. In this case, the risk is measured in standard devia-
tions away from the average equity growth, both for the portfolio as a whole and
for the individual market.
In all the research done in this part of the book, I have not deducted any
money for commissions. As already discussed in Part 1, the most important thing
for now is to see how well the improved or altered systems capture the moves they
are intended to catch, no matter the values of the moves in dollars. Our focus is to
make sure that the average profit per trade is large enough to make trading worth-
while, knowing that the commissions need to be considered at a later stage. We
will, however, deal with this in more detail in Part 4, so in the end, all results will
be burdened with the cost of trading.
Another parameter that will be of importance during this stage of the testing
is the time spent in the market. The more time each individual stock spends in a
trade, either the fewer stocks we can be in simultaneously or the smaller each posi-
tion has to be. But the fewer the shares in each position, the greater the risk per
share must be for the entire position to reach the desired risk level as a specified
percentage of the overall equity. Consequently, if the risk per share is too large, it
won’t make sense for a short-term trading strategy. Therefore, the trade length and
the amount of time spent in a trade need to be in proportion to the amount risked
per share. We have already touched on this subject in Chapter 1 and will work with
it extensively again throughout Parts 3 and 4.
Before we move on, I would like to point out that the TradeStation code fol-
lowing each system modification is the code for the modified version of the sys-
tem that I decided to take with me to Parts 3 and 4.82 PART 2 Trading System Development