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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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Because the original parameters proposed by Donchian do not account for shifts in market volatility per se, another worthwhile experiment is the examination of filters that would cut loses during periods of high volatility. A simple example of this approach would be the addition of a stop loss based on 1 to 5 percent of the asset’s value at the time of entry (see the “Cutting Losses” section later in this chapter).
Another potential drawback to Donchian’s approach is that signals are triggered at or just beyond horizontal support and resistance levels. This could potentially entice large speculative players to trigger stops positioned at these levels, resulting in false breakouts. Readers are encouraged to experiment with various solutions to the problem. One particularly simple and robust solution is offered by Art Collins, author of numerous articles on trading systems, who proposes the addition of a filter requiring the market to break the n period level by 20 percent of the prior trading day’s range.6
If you examine the programming code below closely, you will notice that I have made one very minor modification to the traditional channel breakout system: entry and exits at the prior 20-day high or low instead of the traditional greater than or less than 20-day high or low. Since many countertrend traders fade old resistance and support levels, this minor adjustment gives me greater confidence that our $100 slippage/commissions deduction will remain a realistic assumption.
Using CQG, the programming code for the 20-day stop and reverse channel breakout system is written in this way:
For Long Entry and Short Exit, set “Price” field to:
HiLevel(@,20)[-1]
For Short Entry and Long Exit, set “Price” field to:
LoLevel(@,20)[-1]
Table 3.9 presents the backtested portfolio results from December 31, 1992, to December 31, 2002, for this system.
60
MECHANICAL TRADING SYSTEMS
TABLE 3.9 Channel breakout.
Asset Profit # Trades # Days Max Draw MDD MCL P:MD P:L Ratio %W Time %
ES 11269 75 35 -27001 798 7 0.42 1.19 34.67 100
TY 28437 65 40 -15300 1252 5 1.86 1.67 43.08 100
ED -4125 85 31 -10080 1903 9 -0.41 0.83 25.88 100
SF 27812 68 38 -17625 561 5 1.58 1.33 45.59 100
JY 63475 74 35 -20125 994 4 3.15 1.59 39.19 100
CL 8130 76 34 -23190 743 6 0.35 1.12 42.11 100
GC -780 78 33 -9490 2250 7 -0.08 0.98 30.77 100
S 5337 78 33 -16375 1760 4 0.33 1.10 37.18 100
LH 36400 73 36 -10630 664 5 3.42 1.94 52.05 100
CT -16920 87 30 -38060 1947 7 -0.44 0.83 28.74 100
Total 159035 759 34.3 -44898 749 19 3.54 1.24 37.42 100
Note: All trade summaries include $100 round-turn trade deductions for slippage and commissions. Data source: CQG, Inc.
BOLLINGER BANDS

Here I offer a simple trend-following breakout system where entry signals are triggered by the market closing beyond the upper or lower bands. The system exits open positions when markets revert to the mean (e.g., the 20-day simple moving average). Using CQG, the programming code for this Bollinger band breakout system is written in this way:
Long Entry:
Close(@)[-1] > BHI(@,Sim,20,2.00)[-1]
Short Entry:
Close(@)[-1] < BL0(@,Sim,20,2.00)[-1]
Long Exit and Short Exit set “Price” field to:
BMA(@,Sim,20)[-1]
Table 3.10 presents the backtested portfolio results from December 31, 1992, to December 31, 2002, for this system.
Notice that although this system suffered through 17 consecutive losses, the low correlation of assets within the portfolio still resulted in the endurance of a less severe worst drawdown than that experienced by trading the E-mini S&P 500 by itself.
Trend-Following Systems
61
SOME COMPARISONS

Bollinger Band System and the Three Moving Average Ichimoku
Although a glance at the total net profit column might suggest the three moving average Ichimoku crossover (Table 3.5) was the superior performer, this conclusion is incorrect. While it is true that our Bollinger band system (Table 3.10) produced only a total net profit of $107,396 versus $153,229 for the three moving average Ichimoku, this does not tell the whole story.
Assuming $200,000 equity under management, the three moving average Ichimoku enjoyed an average annualized return on investment of 7.66 percent with a 25.46 percent worst drawdown. By contrast, based on the same assumptions, the Bollinger Band system would have experienced a similar 5.37 percent average annualized rate of return while enduring almost 50 percent less risk (its maximum drawdown was 14.16 percent). In other words, if we examine total net profit in relation to the risks endured to achieve those profits, Bollinger Bands were the better performer. This is illustrated by its superior profit to maximum drawdown (P:MD) ratio of 3.79 percent versus 3.01 percent for the three moving average Ichimoku.
This comparison shows the importance of not analyzing total net profit in a vacuum. By itself, this measure is meaningless. It must always be viewed in relation to maximum drawdown to gauge reward in relation to risk. Moreover, although its results were inferior to the three moving average crossover in terms of total net profit, the Bollinger bands system achieved its superior P:MD while tying up investment capital less often.
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