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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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The next glaring observation is that equity indices perform poorly in trend-following systems (see Table 3.11). Here again my feeling is that these intermediate-term trend-following systems fall victim to the whipsaw nature of these assets. I believe equity indices tend to exhibit intermediateterm choppiness as a function of the dynamic among three separate groups of participants: large short-term speculators, institutional momentum followers, and smaller, undercapitalized momentum followers. Typically the
64
MECHANICAL TRADING SYSTEMS
TABLE 3.11 Asset classes: historical tendencies.
Trending" Mid Mean Reverting
Currencies vs. dollar Most physical Equity Indices
commodities
Short-term interest rates Mid- and long-term Most FX crosses*
rates
"These are historical tendencies that I have noticed as of this writing. The character and dynamics of various asset classes can and will change over time; therefore, continuous monitoring of these tendencies is essential.
*Foreign exchange crosses are defined as non-U.S. dollar-denominated cross rates, such are euro-yen; British pound-Swiss franc.
Data source: CQG, Inc.
interplay among these groups is one in which the large short-term speculators and institutional players push the market to new highs or lows. At this point smaller, undercapitalized momentum followers initiate new positions into these market extremes as large short-term speculators take profits and fade the breakout. This, in turn, leads to capitulation of small speculators and weaker institutional momentum followers. Such capitulation usually results in quick, sharp retracements following breakouts prior to the dominant trend’s reassertion.
Why do foreign exchange cross rates display a greater propensity toward mean reversion than other asset classes? My feeling is that because all currencies tend to trend against the U.S. dollar (for reasons stated earlier), as of this writing, they have exhibited a pronounced tendency toward mean reversion in relation to each other.
If certain asset classes exhibit these tendencies, why include choppy assets in trend-following system results? The simple answer is that the inclusion of assets like the S&P 500 Index ensures the robustness of our system. When our trending asset class enters a historically unprecedented and prolonged period of choppiness (e.g., IMM Japanese yen futures in 2003; for further information on this, compare Table 3.2 on page 51, earlier in this chapter, and Table 7.23 on page 153, later, in Chapter 7), we need to be confident that our price risk management tools are robust enough to ensure our survival. Inclusion of equity indices in our backtested results lets us stress test our system prior to the weathering of such an event. Obviously inclusion of mean reversion assets such as stock index futures in our backtested results has nothing to do with the composition of our real-time trend trading portfolio. In fact, I do not include such assets in a real-time portfolio, since my goal in live trading is maximizing the rate of return and minimizing risk.
Trend-Following Systems
65
CUTTING THE TAILS OF A SYSTEM’S DISTRIBUTION

Cutting Losses
One potential drawback to all indicator-driven trend-following systems is that losses tend to fluctuate on a daily basis and can be quite large if realized immediately following entry. An obvious solution to this problem is the introduction of the same type of loss limits (e.g., percentage of asset’s value at entry) examined in our discussion of channel breakout filters. I strongly encourage readers to experiment with various methods of cutting off the left (or loss) tail of their trend-following system’s distribution, especially if the system is intermediate to long term and per-trade losses suffered would otherwise be large in relation to average per-trade profits.
Figure 3.3 shows the backtested results from a simple stop-and-reverse 20-day channel breakout system; Figure 3.4 shows the same system with a stop-loss filter of 3 percent of asset value at entry.7
Although at first glance the simple stop and reverse channel breakout appears superior since it generated a larger total net profit, notice that the
U= 11111
TotalNetProf 11 - 58720 'iaxImunoWin - 21020 4axConsecLosses- 4
ClosedNet Prof i 1* 57010 AverageLoss ■ -2282 CurConsecLosses» 2
TotaiTraaeCount= 74 4aximumLoss = -9600 3rofitTcMaxDraw= 2.32
OpenPosition - -1000 4axCI osedDraw ■ -12500 ’rof i tLossRat iom 1.57
Percent Long = tu »iaxDrawAmount - -25350 ’ercentwinners = 39 19
AverageDurat ion- 16 4axDrawDurat ion- 565 3emoveToNeut ral- 5 48
AverageProfit = • «iaxConsecWl ns = rimePercentage = 100 00
AveraqeWin - 5565 CurConsecWins - 0 CurDrawDown - -5440
H 1°’ 107 I14 I21 |28 |01 |11 118 |25 |02 |09 116 |23 |30
FIGURE 3.3 Spot U.S. dollar/yen with 20-day channel breakout. Includes data from December 31, 1992, to December 31, 2002.
Note: All trade summaries include $100 round-turn trade deductions for slippage
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