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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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environments, sometimes unprecedented shifts in market dynamics are too dramatic to enable utilization of previously successful trading systems (examples of such shifts occurred in agricultural markets in the 1970s and in metals markets in 1979 to 1980). In such instances, those who can quickly identify the paradigm shift in market behavior and make the necessary adjustments will outperform the remainder of the pack. This is why the ability to analyze the historical performance of trading systems is invaluable in distinguishing between an equity drawdown within “normal” system tolerances and a long-term and/or permanent paradigm shift in market behavior.
Limitations to the System Development and Data Analysis Process
I have already alluded to the fact that our ideals in terms of performance results are often at odds with the kinds of market behavior that trading systems are attempting to capitalize on (e.g., trend-following—a few large profits and many small losses). Due to this reality in performance numbers, sometimes system developers shy away from trend trading despite the overall superiority and psychological compatibility of these trading programs. System developers sometimes strive toward perfection in the performance results of their systems. Although it is satisfying to see smooth and even dis-
System Development and Analysis
151
tributions of profits and losses in performance numbers, system developers should not sacrifice solid profits and modest drawdowns in favor of smoother distributions in performance results.
Such sacrifices are usually nothing more than another manifestation of the perfect trader syndrome. Instead of seeking unattainable perfection in profit and loss distribution, it is far better to choose robust trading systems that are attuned to our psychological center of gravity. By implementing trading systems that are most compatible with our individual personalities, we ensure the greatest likelihood of our adherence to the system’s trading signals during its inevitable periods of equity drawdown.
DATA ANALYSIS PROCESS

Overview
Now that we have clearly established the limitations and benefits of various data analysis processes, we can examine the distinct levels of data analysis. Although there may be other methodologies by which to delineate our data, I have found that generally there are three different levels to data analysis of trading systems: analysis by asset classes, year-by-year analysis of insample data, and analysis of out-of-sample data.
Data Analysis by Asset Classes
For examples of analysis of data by asset classes, I refer the reader back to Tables 3.2 to 3.10. Ideally, system developers would like to see smooth and evenly distributed profits throughout all assets within these tables. However, it is more important that a system displays solid performance vis-à-vis risk than that such performance is evenly distributed throughout all assets in our backtested data history. With this caveat in mind, let us compare the various trading system results shown in Tables 3.2 to 3.10 and attempt to draw some conclusions regarding the data.
For now, we will narrow the field of study to those systems that generated a profit to maximum drawdown ratio of 3.0 or higher for the entire portfolio (shown here as Tables 7.21 to 7.25). Narrowing the field of study ensures that we do not waste our time and energy analyzing marginally performing trading systems, which we have no intention of trading in real time.
Table 7.21 not only generated the largest net profits, but it also exhibited the smoothest distribution of net profits among the various asset classes studied. Tables 7.22 and 7.23 also showed fairly smooth distributions of net profits throughout the various assets. Tables 7.24 and 7.25 are probably the most questionable of the tables analyzed in our asset-by-asset
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MECHANICAL TRADING SYSTEMS
TABLE 7.21 MACD.
Asset Profit # Trades # Days Max Draw MDD MCL P:MD P:L Ratio %W Time %
ES -4242 19 133 -42446 1089 4 -0.10 0.91 36.84 100
TY 35678 19 132 -12875 810 5 2.77 3.10 47.37 100
ED 9097 15 165 -6812 1827 8 1.34 2.60 26.67 100
SF 58225 14 179 -20225 516 3 2.88 3.72 57.14 100
JY 37 18 137 -41500 1098 2 0.00 1.00 44.44 100
CL 61080 14 179 -19840 521 5 3.08 4.75 42.86 100
GC 740 22 113 -13810 985 6 0.05 1.04 36.36 100
S -18812 23 110 -35325 2378 5 -0.53 0.61 34.78 100
LH 21440 18 139 -11690 688 4 1.83 1.94 50.00 100
CT 56255 13 193 -13990 510 1 4.02 6.43 61.54 100
Total 219498 175 142.9 -42554 686 7 5.16 2.34 42.85 100
Note: All trade summaries include $100 round-turn trade deductions for slippage and commissions. Data source: CQG, Inc.
TABLE 7.22 Two moving average crossover.
# # Max P:L Time
Asset Profit Trades Days Draw MDD MCL P:MD Ratio %W %
ES 6023 117 22 (24621) 1122 7 0.24 1.07 35.90 100
TY 10678 94 27 (10681) 1032 5 1.00 1.18 37.23 100
ED 5952 88 28 (5606) 1577 9 1.06 1.41 32.95 100
SF 15650 121 22 (30350) 565 7 0.52 1.14 40.50 100
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