($) Trades Winners ($) ($) Losers ($)
| Coffee | 1,837 | 27,065 | -11,215 | ^4,931 | |||
| Cotton | -98,725 | 4,955 | -2,800 | -102,205 | |||
| Crude oil, | -61,940 | 5,210 | -7,850 | -63,180 | |||
| light | |||||||
| Gold, | -29,830 | 2,630 | -2,920 | -31,150 | |||
| Comex | |||||||
| Japanese | -47,713 | 8,633 | -2,762 | -60,81 3 | |||
| yen | |||||||
| Swiss franc | -55,350 | 9,175 | -3,225 | -63,51 3 | |||
| U.S. Bond | -49,313 | 4,400 | -1,694 | -61,469 |
end of the range, then oscillator values are below 20. We assume that the next move will take prices toward the top of the range. The "range" between the .r-day high and low changes continuously. Hence, this oscillator cannot predict the amplitude of the next move.
The system tested uses a 10-day period to calculate the so-called fast-K and fast-D moving averages. When the fast-K is above the fast-D line, the system buys on the open and vice versa. The System Writer Plus™ software guide gives the exact method for the calculations.
This example uses continuous contracts for seven unrelated markets, allows $100 for slippage and commissions, and uses a $1,500 initial money management stop. The test period was from May 26, 1989, through June 30, 1995. This simple system was a net loser over these markets. It also had substantial drawdowns, largely due to the many successive losing trades. Note the large number of trades and the relatively low proportion of winners.
The main implication of these calculations is that although markets may trend for short periods only, the profits during trending periods can far exceed the profits during trading ranges. The reason for this is that the amplitude of price moves during trends is many times the amplitude during trading ranges.
This example assumes that you pay the "discounted" trading commissions offered on the street. If your trading commissions are very low or negligible, then the antitrend strategy, with its high trading frequency, takes on a different dimension.
46 Foundations of System Design
Table 3.3 Impact of trading costs on profitability of antitrend trading strategies (dollars)
| Market | Paper Profit $100SScC | Paper Profit noS&C |
| Coffee | 1,837 | 29,438 |
| Cotton | -98,725 | -69,125 |
| Crude oil, light | -61,940 | -31,840 |
| Gold, Comex | -29,830 | -A,230 |
| Japanese yen | ^7,713 | -16,813 |
| Swiss franc | -55,350 | -26,850 |
| U.S. bond | -t9,313 | -18,313 |
Table 3.3 compares paper profits with and without slippage and commissions (S&C). The difference in profitability is striking. The stochastic oscillator system performance improved significantly with low commissions. This result indicates that an antitrend strategy would not be attractive if you had to pay high commissions.
There are a number of "antitrend" strategies. Table 3.4 presents another set of calculations using a different trading strategy to illustrate this point. The moving average crossover (MAXO) system is the simplest trend-following strategy, but it can also be used as an antitrend strategy. For example, if the shorter moving average crosses over the longer moving average, you can go short in an antitrend strategy. Of course, this "upside" crossover would be a signal to buy long in a trend-following strategy.
Table 3.4 Comparison of trading systems using 5-day and 20-day simple MAXO tests, 5/89-6/95 (dollars)
| Antitrend Trading MAXO | Trend-Following MAXO | |||
| Paper Profit, $100SStC | Maximum Intraday Drawdown | Paper Profit, $100 S&C | Maximum Intraday Drawdown | |
| Coffee | ^2,719 | -59,344 | 59,241 | -17,216 |
| Cotton | -14,670 | -36,895 | -6,845 | -18,010 |
| Crude oil, light | 2,580 | -21,500 | -30,730 | -35,460 |
| Gold, Comex | -12,740 | -21,780 | -8,560 | -12,950 |
| Japanese yen | -34,650 | -58,540 | -9,025 | -22,738 |
| Swiss franc | -7,812 | -45,688 | -23,500 | -40,175 |
| U.S. bond | -28,119 | -33,019 | -9,643 | -23,568 |
| Average | -19,733 | -39,538 | -4,152 | -24,302 |
To Follow the Trend or Not?47
Here we have arbitrarily picked 5-day and 20-day moving averages as examples of short- to intermediate-term averages. The test period was from May 26, 1989, through June 30, 1995, with $100 for slippage and commissions and a $1,500 initial stop. The antitrend strategy was a net loser on average, with significant potential for intraday drawdowns. The trend-following strategy cut the average loss by 79 percent and drawdown is lower by 39 percent—a better situation on both counts.
Table 3.5 presents another combination: the moving average antitrend and trend-following strategies with 7-day and 50-day simple moving averages. This combination is good for no-nonsense trend following. The assumptions are the same as before: $100 for slippage and commissions and a $1,500 initial stop with the calculations performed from May 26, 1989, through June 30, 1995.
Under antitrend trading, the 7/50-day SMA combination was also a net loser. On the other hand, it was a net winner with trend following, with profitability across all seven markets. The trend-following strategy had approximately one-fifth the drawdowns of the antitrend approach. Thus, the trend-following approach was the better choice on both counts.
These calculations show that a trend-following strategy is probably the better choice for the average position trader. However, the antitrend strategy may be attractive if you have low commission costs and little slippage.
The example tests in this chapter used arbitrary combinations of moving averages. However, you can test your system over historical data
Table 3.5 Comparison of performance for 7-day and 50-day simple MAXO tests, 5/89-6/95 (dollars)
| Antitrend Trading MAXO | Trend-Following MAXO | |||
| Paper Profit $100 S&C | Maximum Intraday Drawdown | Paper Profit $100 SScC | Maximum Intraday Drawdown | |
| Coffee | -22,716 | -68,534 | 38,689 | -27,615 |
| Cotton | -44,375 | -52,275 | 23,155 | -9,795 |
| Crude oil, light | ^t3,440 | -47,570 | 20,430 | -5,020 |
| Gold, Comex | -14,540 | -20,980 | 4,560 | -5,730 |
| Japanese yen | -39,663 | -71,225 | 23,662 | -23,075 |
| Swiss franc | -49,325 | -70,800 | 32,988 | -13,163 |
| U.S. bond | -34,606 | -36,756 | 18,131 | -14,619 |
| Average | -37,658 | -49,934 | 20,488 | -11,900 |
48 Foundations of System Design
to find other combinations with better performance. Optimization is the process of finding the "best" performing variable set on historical data. The next section examines whether optimization is a good design strategy.