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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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Perhaps most significant of all was the dramatic reduction of the worst drawdown. Although the trend-following mean reversion system was more robust in theory, its large maximum drawdown meant that the vast majority of smaller trading accounts would not have benefited from its superior P:MD.
RSI Crossover
This system generates long entry signals whenever the 14-period RSI was less than 25 two bars ago and then crosses above 25 on the prior bar. (Short entries are generated whenever RSI was above 75 two bars ago and then crossed below 75 on the prior bar.) Like the seven-period reversal method, this system generates signals only when the short-term trending action has reversed (thereby suggesting a reversion to the mean is under way).
Long Entry:
RSI(@,14)[-2] < 25 AND RSI(@,14)[-1] XABOVE 25 Long Exit—Condition #1 set “Price” field to:
EntryPrice(@,0,All,ThisTradeOnly)+(.03*
EntryPrice(@,0,All,ThisTradeOnly))
TABLE 5.8 Seven-bar reversal with 1% profit and stop exit.
Asset # Profit Trades # Days Max Draw MDD MCL P:MD P:L Ratio %W Time %

ND 55696 166 0.75 -43783 682 5 1.27 1.30 48.8 5.44
Note: results include a deduction of $100 per round-turn trade for slippage on daily
time frame and $75 per round-turn for shorter time frames. Data source: CQG, Inc.
98
MECHANICAL TRADING SYSTEMS
Long Exit—Condition #2 set “Price” field to:
EntryPrice(@,0,All,ThisTradeOnly)-(.01*
EntryPrice(@,0,All,ThisTradeOnly))
Short Entry:
RSI(@,14)[-2] > 75 AND RSI(@,14)[-1] XBELOW 75
For short exits, simply reverse the plus and minus signs used for long exits.
Notice how Table 5.8’s identical profit target and fail-safe stop loss resulted in its enjoyment of a higher winning percentage and smaller number of maximum consecutive losses than Table 5.9.
MEAN REVERSION SYSTEMS USING 30-MINUTE BARS

The data displayed for the 30-minute bar time frame include history from February 14, 2000, to January 30, 2004.
RSI Extremes with 100-Hour Moving Average Filter
Table 5.10 shows that one of our most successful and robust trading systems up until this point has failed miserably in this shorter time frame.
TABLE 5.9 RSI crossover with 3% profit exit and 1% stop.
Asset Profit # Trades # Days Max Draw MDD MCL P:L P:MD Ratio %W Time %
ND 51092 141 0.9 -46647 700 14 1.10 1.25 26.24 5.61
Note: results include a deduction of $100 per round-turn trade for slippage on daily time frame and $75 per round-turn for shorter time frames. Data source: CQG, Inc.
TABLE 5.10 RSI extremes with 100-hour moving average filter an 2.5% stop.
Asset Profit # Trades # Days Max Draw MDD MCL P:MD P:L Ratio %W Time %
ND -62385 275 1.125 -117578 860 8 -0.53 0.92 49.82 32.61
Note: results include a deduction of $100 per round-turn trade for slippage on daily
time frame and $75 per round-turn for shorter time frames. Data source: CQG, Inc.
Short-Term Systems
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Whenever a trading system shifts so dramatically from profitability, we need to ask why.
One possible answer could be that the fixed costs and smaller per-trade profits means that this system was destined to fail over shorter time frames. This deterioration as time frames are shortened was exemplified in our examination of IEURUSD with MACD in Table 5.1. These same factors may be the underlying cause of the failure occurring in Table 5.10; to determine if this is true, we must compare the conditions in Table 5.1 to those in Table 5.10.
A distinct difference between MACD and RSI extremes was that MACD’s entry and exit conditions were all based on indicators (exponential moving averages) that automatically adapted to whatever time frame was being traded. By contrast, one of the exit conditions in RSI extremes was based on a static percentage value of the asset at the time of our trade’s initiation. Since our profitable exit condition was based on the RSI indicator’s achievement of a specific level (which, like MACD, adapted to our change of time frames) and our fail-safe stop-loss was based on the static 2.5 percent of contract value at the trade’s initiation, this suggests the need to reduce the fail-safe stop-loss levels to compensate for declining volatility inherent in execution of this system over shorter time frames.
Therefore, it is reasonable to assume that this failure to adjust our stoploss level as volatility contracted over the shorter time frame should have resulted in smaller profits and occasional large losses. In fact, a comparison of Tables 5.5, 5.7, and 5.10 demonstrates that as time frames were shortened, the winning percentages increased steadily and dramatically. This proves that the static 2.5 percent stop loss, which was robust enough for the 2-hour and 60-minute time frames, needs to be adjusted for shorter time frames.
Table 5.11 shows the results of employing the same RSI extremes systems with a smaller, 1.5 percent fail-safe stop. As expected, both the profit to maximum drawdown ratios as well as the win/loss ratios is now comparable with those seen in Table 5.7.
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