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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 use of a fixed dollar amount stop loss is also an imperfect solution. Because the asset’s value over the length of the backtested period will vary significantly, using fixed dollar stops could underestimate or overestimate the true risk inherent in these trades (as demonstrated in Chapter 3’s percentage value comparison of natural gas when futures prices rose from $1.50 to $6.00). Despite this potential distortion, I employ a fixed dollar stop level in one of the backtested portfolio results (see relative strength index extremes with 200-day moving average filter, page 77).
If the components within a portfolio are prone to significant distortions due to changes in asset valuation throughout the backtested period, I suggest employment of some indicator-driven stop loss, such as placement of stops 2.5 standard deviations beyond the 20-day moving average. (This is just one of many robust solutions to the backtested data problems. Readers are encouraged to experiment with other stop-level methodologies.)

Some trading systems can easily be tailored to capitalize on reversion to the mean while still trading in the direction of the long-term trend. Although they are not without drawbacks, these systems are often easier for new traders to stick with—assuming they have already mastered the discipline required to fade mass psychology—because they enter at recent market extremes (selling recent highs or buying recent lows), while simultaneously trading in the direction of the longer-term trend (which results in greater confidence during drawdowns).
In addition, because these are mean reversion systems, traders exit with profits once the market reverts back to the average. Because both risk and reward are quantified at the time of the trade’s initiation, one of the
Mean Reversion Systems
most difficult psychological obstacles to successful trend trading—letting profits run—has been eliminated.
Relative Strength Index Extremes with 200-Day Moving Average Filter
This system waits for the market to achieve extreme overbought or oversold relative strength index (RSI) levels while still trading in the direction of the long-term trend through its use of a 200-day moving average as a filter. Because we are trading the direction of the long-term trend, we can place our exit with profit criteria levels somewhere beyond the mean. In this case we exit long positions when the 14-day RSI crosses beyond the 60 level and exit shorts when the 40 level is breached.
As discussed in Chapter 2, we will need to include a second, fail-safe exit condition to protect us against unlimited loss in the event that the trend changes and the market does not revert to its mean.
Using CQG, the programming code for the trend-following mean reversion system with RSI extremes, 200-day moving average filter, and 2.5 percent stop loss is written as:
Long Entry:
RSI(@,9)[-1] < 35 AND Close(@)[-1] > MA(@,Sim,200)[-1]
Long Exit—Condition #1:
RSI(@,14)[-1] XABOVE 60
Long Exit—Condition #2:
Short Entry:
RSI(@,9)[-1] > 65 AND Close(@)[-1] < MA(@,Sim,200)[-1]
Short Exit—Condition #1:
RSI(@,14)[-1] XBELOW 40
Short Exit—Condition #2 set “Price” field to:
EntryPrice(@,0,All,ThisTradeOnly)+(.025* EntryPrice(@,0,All, ThisTradeOnly))
The programming code just shown is fine for backtesting of cash contracts. However, if we want to backtest this system on our futures portfolio, we cannot use stop losses based on percentage of contract value. Instead we will employ fixed dollar amount stop losses. Here is the long and short exit programming code for a fixed $2,500 stop loss:
Set “Price” field to:
OpenPositionAverageEntryPrice(@,ThisTradeOnly) -Dollar2Price(@,2500) / OpenPositionSize(@,ThisTradeOnly)
Due to the lack of volatility of certain assets within the portfolio, use of the $2,500 is not always realistic. Table 4.1 shows the various fixed dollar amount stops used to trade the futures portfolio.
Table 4.2 presents the results on our backtested portfolio.
Notice that the results are quite unimpressive when compared to the systems shown throughout Chapter 3. One reason for this inferior performance is that our portfolio includes eurodollars, which are not volatile enough to be profitable (especially since we have deducted $100 in slippage and commissions on a per-trade basis). How could eurodollars be volatile enough to work for our trend-following systems and not for mean reversion systems? In Chapter 3 we allowed profits to run; here we are cutting profits somewhere around the mean. Since eurodollars do not display exceptional volatility, cutting winning trades at the mean usually produces negligible per-trade profits. Subsequently, when the trend changes and the market triggers its fail-safe stop loss, such losses are substantial when compared with the small profits attained through mean reversion, resulting in an overall negative return on investment.
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