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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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TABLE 5.11 RSI extremes with 100-hour moving average filter and 1.5% stop.
Asset Profit # Trades # Days Max Draw MDD MCL P:MD P:L Ratio %W Time %
ND 93635 336 0.875 -5421 5 273 12 1.73 1.14 38.99 30.14
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.
100
MECHANICAL TRADING SYSTEMS
RSI Crossover with Stops and Profit Exits Set to 1 Percent
All programming code for this system is the same as shown in the 60-minute time frame with the exception of long and short exit conditions #1, which have been reduced from 3 percent to 1 percent beyond entry price, thereby equalizing profit and stop-loss levels.
Although this system obviously produced drastically inferior results from its 60-minute bar counterpart, remember that it includes only four years’ as opposed to five years’ performance for the 120- and 60-minute bar systems. This raises several questions; first, why did we lose the data from 1999 when we shortened our time frame? Data vendors can store only a finite amount of history. Therefore, as time frames are shortened, the storage of an equal number of historical data bars will yield a smaller time frame of history. For example, as of this writing, CQG stores 15,000 bars, which is five years’ worth of 60-minute bars but only four years’ worth of 30-minute bars.
Would inclusion of 1999’s data have improved performance for the 30-minute time frame in Table 5.12? Although it is reasonable to assume this to be the case since these systems were profitable overall, the fact that Table 5.1 showed deterioration over shorter time frames for a different asset on a negatively and/or uncorrelated (trend-following) system makes me reasonably confident that inclusion of 1999’s performance would not have changed the overall pattern of declining P:MD as time frames are shortened.
The next question is how to generate comparable “synthetic” data histories through equalization of results to compensate for the loss of 1999 on our 30-minute systems. Although we can never know with certainty exactly how 1999 would have impacted the system’s performance in this time frame, we can equalize our results to those generated by our 60-minute time frame by dividing each system’s total net profit by the number of years displayed in its data history. For example, the annualized total net profit for Table 5.9 would be roughly $10,218.40, whereas the annualized total net profit for Table 5.12 was around $2,539.50. (As before, we should still assume the same maximum drawdowns for both tables.)
TABLE 5.12 RSI crossover with 1% stop loss and profit.
Asset Profit # Trades # Days Max Draw MDD MCL P:MD P:L Ratio %W Time %
ND 10158 204 0.9 -56738 601 12 0.18 1.03 25.00 18.92
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
101
15-MINUTE BAR SYSTEMS: RSI EXTREMES WITH 50-HOUR MOVING AVERAGE FILTER

The data displayed in Table 5.13 for the 15-minute bar time frame includes history from February 5, 2002, to January 30, 2004. Because our time frame was again shortened, we reduced our fail-safe stop-loss level to 1 percent of entry price for ND. In addition, because SP is less volatile than ND, we reduced its fail-safe stop loss to 0.5 percent of entry price.
Performance deterioration over this time frame was so dramatic that I had to include SP to show a profitable trading asset for our mechanical trading systems. Furthermore, such profits were achievable only with the trend-following mean reversion system, and even here the P:MD was only moderately successful. Although nondirectionally biased mean reversion systems can work with 15- and 5-minute bars, in general, I have found that mean reversion systems containing a trend-following filter tend to be the most robust over these time frames.
5-MINUTE BAR SYSTEMS: RSI EXTREMES WITH 16.67-HOUR MOVING AVERAGE FILTER

The data displayed in Table 5.14 for the 5-minute bar time frame includes history from June 25, 2003, to January 30, 2004.
When comparing the performance in Table 5.14 to that of Table 5.13, two notable improvements occurred as the time frames of our bars were shortened: the maximum number of consecutive losses and win/loss ratio for the system. This was a direct result of our maintaining the same RSI and percentage stop-loss parameters despite the shortening of time frames. Because mean reversion on shorter time frames requires a smaller magnitude of price movement, likelihood of mean reversion became greater vis-à-vis the probability of achieving the identical fail-safe stop level. As a result we saw improvements in win/loss ratio and consecutive losses as our P:MD dropped.
TABLE 5.13 RSI extremes with 50-hour moving average filter and 1% stop for ND; 0.5% stop for SP.
# # Max P:L Time
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