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Mechanical trading systems - Weissman R.L.

Weissman R.L. Mechanical trading systems - Wiley publishing , 2005 . - 240 p.
ISBN 0-471-65435-3
Download (direct link): mechanicaltradingsystems2005.pdf
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In fact, it is quite common for asset allocators to dedicate additional funds to a trading system that is in the midst of a sizable equity drawdown. The reasoning here is that if the fund manager maintains a strict, disciplined adherence to the trading system and the market subsequently fails to exceed the historical worst peak-to-valley equity drawdown, then commitment of additional investment dollars into such an environment actually represents lower risk and a greater potential for profit than could normally be achieved through implementation of the system.
But what if the largest historical drawdown from the backtested period is violated? Either this can mean that our system’s integrity remains intact and we simply need to scale down our volumetric exposure to adjust for unprecedented levels of performance volatility, or it can serve as a warning that the dynamics of the market may be shifting and our system should be modified or perhaps no longer traded. Determining which of these two possibilities we are facing illustrates the importance of pairing robust mechanical trading systems with experienced traders and risk managers who are well versed at utilizing the price risk management tools discussed in this chapter.
CHAPTER 9
Improving the Rate of Return
Improving Returns by Expanding the Comfort Zone
The superior man bends his attention to what is radical. That being established, all practical courses naturally grow up.
—Confucius
THREE TYPES OF DIVERSIFICATION

Diversification is among the most important and underutilized tools available to traders and investors because it allows improvement of our rates of return without proportionately increasing risk assumed to achieve these enhanced levels of performance. The most commonly employed type of diversification—asset class diversification—has already been discussed in Chapters 3 and 4, where we looked at how diversification among assets that had low correlations improved our overall performance. A review of Tables 3.2 to 3.13 and Tables 4.4 to 4.8 shows that diversification almost always yielded improvements when compared with the performance of individual assets.
This chapter focuses on the two other diversification methodologies: adaptation of different parameter sets for the same trading system and combining of negatively and/or uncorrelated trading systems.
DIVERSIFICATION OF PARAMETER SETS

Assuming that a trading account has adequate equity under management, it is preferable to diversify parameter sets rather than to trade multiple contracts with the same parameter set. Although there maybe strong positive
177
178
MECHANICAL TRADING SYSTEMS
correlations between parameter sets of the same trading system, Tables 7.1 to 7.20 show that even minor modifications to parameter sets can make the difference between an overall profitable or losing outcome. Furthermore, as shown in Chapter 7, because we can never be certain as to which parameter set will outperform in the future, parameter set diversification greatly aids in minimizing regret. Minimization of regret in this context strengthens our psychological ability to adhere to a disciplined and consistent (e.g., systematic and/or mechanical) approach toward trading.2
A comparison of Tables 9.1 and 9.2 exemplifies this final point. Table 9.1 shows the results of various parameter sets on the two moving average crossover system for IMM Swiss franc during the in-sample period of 1993
TABLE 9.1 Moving average crossover optimization for CME Swiss franc (1993-2002).
Short Moving Average Long Moving Average P:MD
10 29 1.76
9 29 1.44
8 32 1.41
9 32 1.39
10 32 1.30
10 26 1.10
8 29 1.07
8 26 0.91
9 26 0.72
9 23 0.56
6 26 0.48
7 26 0.44
7 29 0.35
10 20 0.25
8 23 -0.11
9 20 -0.15
10 23 -0.18
8 20 -0.19
7 32 -0.19
6 23 -0.24
6 29 -0.24
7 23 -0.36
6 32 -0.45
7 20 -0.73
6 20 -0.83
Note: All trade summaries include $100 round-turn trade deductions for slippage
and commissions. Data source: CQG, Inc.
Improving the Rate of Return 179
TABLE 9.2 Moving average crossover optimization for CME Swiss franc—out of sample study (2003).
Short Moving Average Long Moving Average P:MD
7 20 0.83
6 26 0.77
10 23 0.71
7 32 0.70
6 23 0.63
8 32 0.58
7 26 0.55
7 23 0.54
8 23 0.50
6 32 0.47
10 32 0.46
9 20 0.45
8 29 0.40
10 20 0.36
9 32 0.36
6 20 0.28
8 20 0.26
7 29 0.24
9 23 0.20
8 26 0.16
10 26 0.16
6 29 0.11
9 26 -0.02
9 29 -0.13
10 29 -0.36
Note: All trade summaries include $100 round-turn trade deductions for slippage and commissions. Data source: CQG, Inc.
to 2002. Notice that the best-performing parameter set in this Table was the
10- and 29-day moving average crossover; the second-to-worst-performer was the 7- and 20-day parameter set. Compare this with Table 9.2, which is the same system on the IMM Swiss franc for the out-of-sample year of 2003. Not only is the best-performing parameter set of our in-sample period now the worst performer, but also our second-to-worst in-sample performer has now become the top-performing parameter set.
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