# Mechanical trading systems - Weissman R.L.

ISBN 0-471-65435-3

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The process of choosing which parameter sets to trade almost always proves more difficult than that of eliminating suboptimal parameter sets. Why is deciding on a particular parameter set so tough? Because the top-performing parameter sets for one asset are rarely the top performers for other negatively and/or uncorrelated assets. Furthermore, top performers in the past are often the laggards of the future. These points are well illustrated by my optimization study on the two moving average crossover system for the same portfolio of assets in Chapter 3 (see Tables 7.1 to 7.20).

In my optimization study of the two moving average crossover system, I chose to examine shorter-term moving average values between 6 and 10 days using a one-step interval (i.e., 6, 7, 8, 9, and 10) and longer-term moving average values set between 20 and 32 days using three-step intervals (i.e., 20, 23, 26, 29, and 32). The reason behind choosing a particular set of values in an optimization study is a function of utility, distinctiveness, experience, and common sense.

For example, for shorter-term moving averages, there is quite a significant difference between a 6- and 7-day moving average (e.g., increases the data series by one-sixth). By contrast, for the longer-term moving average, use of one-step variations in our optimization study is probably not going to yield the same distinctiveness in our data series as will be achieved by staggering our steps by three. This is because the difference between a 31- and a 32-day moving average only increases the data series by 1/31st and is therefore minute.12

In choosing which values to include and exclude from the study, I began with the exclusion of nonsensical values (e.g., 2 days as a value for our short-term moving average) and worked forward based on values used most commonly by technicians in the markets (e.g., 7 days for the shorter-term moving average and 29 days for the longer-term moving average). Although this method is far from infallible, the goal in an optimization study is not perfection, but merely the identification of diverse, robust parameter sets and avoidance of suboptimal sets.

In examining the tables, notice how rarely the top performer of the 10-year “in-sample” period was the top performer in our “out-of-sample” period. Perhaps even more disturbing is the significant number of times that our worst-performing parameter set during our “in-sample” period

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MECHANICAL TRADING SYSTEMS

TABLE 7.1 Moving average crossover optimization for NYBOT cotton

(1993-2002).

Short Moving Average Long Moving Average P:MD

7 29 0.24

6 26 -0.06

7 32 -0.09

8 29 -0.11

8 26 -0.12

9 29 -0.12

10 26 -0.16

10 23 -0.21

9 26 -0.22

6 29 -0.22

9 32 -0.25

10 29 -0.26

8 32 -0.26

9 23 -0.28

8 23 -0.30

6 32 -0.35

7 26 -0.37

7 23 -0.38

10 32 -0.39

10 20 -0.43

9 20 -0.49

8 20 -0.63

6 23 -0.66

6 20 -0.69

7 20 -0.70

Note: All trade summaries include $100 round-turn trade deductions for slippage

and commissions. Data source: CQG, Inc.

System Development and Analysis 129

TABLE 7.2 Moving average crossover optimization for NYBOT cotton (2003).

Short Moving Average Long Moving Average P:MD

10 20 1.59

6 20 1.48

7 20 1.30

8 20 1.25

10 29 1.22

7 23 1.17

6 23 1.13

8 23 1.11

9 20 1.05

9 29 1.04

6 26 0.91

10 26 0.88

8 26 0.83

7 29 0.83

9 32 0.82

10 23 0.76

6 29 0.76

9 26 0.72

6 32 0.69

7 26 0.67

8 29 0.65

8 32 0.64

10 32 0.63

7 32 0.62

9 23 0.42

Note: All trade summaries include $100 round-turn trade deductions for slippage

and commissions. Data source: CQG, Inc.

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MECHANICAL TRADING SYSTEMS

TABLE 7.3 Moving average crossover optimization for NYMEX crude oil

(1993-2000).

Short Moving Average Long Moving Average P:MD

8 26 1.64

9 26 1.34

7 32 1.30

G 20 1.21

8 32 1.1G

8 23 1.14

g 2g 1.13

8 2g 1.0G

G 2G 1.04

7 2g 0.gg

7 2G 0.gG

g 23 0.g2

7 23 0.91

10 2g 0.g0

10 2G 0.87

10 20 0.84

G 32 0.81

g 32 0.G8

G 23 0.GG

G 2g 0.53

7 20 0.4g

10 23 0.45

8 20 0.43

g 20 0.43

10 32 0.39

Note: All trade summaries include $100 round-turn trade deductions for slippage

and commissions. Data source: CQG, Inc.

System Development and Analysis

131

TABLE 7.4 Moving average crossover optimization for NYMEX crude oil—out-of-

sample study (2003).

Short Moving Average Long Moving Average P:MD

6 32 0.14

6 29 -0.02

7 32 -0.13

7 29 -0.34

9 29 -0.34

10 29 -0.34

8 32 -0.37

7 26 -0.39

8 29 -0.42

6 26 -0.45

10 32 -0.52

9 20 -0.54

9 32 -0.54

7 23 -0.59

10 26 -0.60

9 26 -0.65

8 26 -0.66

10 23 -0.67

6 23 -0.68

10 20 -0.71

8 23 -0.71

8 20 -0.74

7 20 -0.75

9 23 -0.75

6 20 -0.76

Note: All trade summaries include $100 round-turn trade deductions for slippage

and commissions. Data source: CQG, Inc.

132

MECHANICAL TRADING SYSTEMS

TABLE 7.5 Moving average crossover optimization for CBOT T-notes

(1993-2002).

Short Moving Average Long Moving Average P:MD

10 23 2.28

9 29 2.28

10 29 2.18

8 26 2.03

10 26 2.02

9 26 1.63

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