to be in, we can calculate how far away from the entry price we need to place MMP
to make that possible by dividing the desired number of positions by ffict. This will
place MMP a fixed distance away from the entry price, but by letting the distance
for MMP vary around a desired average distance, we can vary the number of max-
imum positions with the behavior of the markets.
For example, if the volatility is high, we can place MMP further away from
the entry price than the desired average distance and thereby make each position
smaller. This makes room for more open positions and better diversification dur-
ing times of turmoil, and vice versa. At the same time, a dynamic stop loss will
keep us from getting stopped out too frequently during times of turmoil, while it
will stick closer to the price of the stock in calmer times, when our positions are
larger.Conclusion
Once we understood the process behind the DRMM, we were able to build a basic
spreadsheet for all the necessary calculations. With the help of this spreadsheet,
you can test a few system–market combinations traded within one portfolio using
the same account equity. We also took a closer look at a more professional spread-
sheet and how it compared to a Web-based evaluation of the real-time trading
records of professional fund managers and CTAs.
With the help of the professional spreadsheet, we ended our research by tak-
ing a look at a few combinations of the systems and filters we worked with
throughout the book. By adding the money management and trading all the sys-
tem–market combinations using a shared account equity, we shifted the focus from
the individual system to the complete strategy.
For most of the strategies, we tried to find the fictive fto risk per trade that
gave us an equity curve that was as smooth as possible in relation to the growth
rate. It’s important to note that nothing forces you to trade the exact optimal ffor
the highest growth rate. Many times this may still be perceived as too risky. The
important thing is to not take any unnecessary risk by trading above the optimal f,
when the goal is to decrease the risk by optisizing on any other variable.
The first two strategies produced very good and low-risk results, although
the current bear market lowered the performance somewhat over the last several
months. The conclusion is that the meander indicator does a very good job in find-
ing high-probability trading opportunities with high profit potentials.
Comparing Strategies 3 and 1, it’s easy to see that Strategy 3 didn’t come
anywhere close to the performance of Strategy 1. Even so, it still outperforms a
benchmark buy-and-hold strategy and could probably be trusted for real-time trad-
ing if combined with a system for trading the short side. Strategy 4 was optisized
on the maximum equity growth, rather than the Sharpe ratio and the smoothness
of the equity curve, which resulted in an average annual return of 32.5 percent.382 PART 4 Money Management