The detailed equity curve of our trading system seems not to have changed a lot because of the added time filter (Figure 3.9A). Instead, a look at the underwater equity curve reveals that the drawdowns within the 5 years of trading have increased from 8% before to 10% with the daytime filter. Furthermore, it now takes longer for our modified trading system to recover from these drawdowns. So what have we gained from our filter? You can evaluate the time filter impact with a closer look at the trading figures. If you look at the number of trades you see that they have been reduced dramatically by the inserted filter to 902, compared with nearly 3000 trades which the system generated before.
Together with the fact that the total net profit slightly increased to $115,000, compared with $100,000 without the time filter, this leads to a very important point for you when using this system:
The average profit per trade is now $128 (including $30 slippage and commissions) compared with the poor $33 which the system had gained before when trading was allowed around the clock. This is an improvement by a factor of four!
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Figure 3.9: LUXOR system results with added time filter. Entries only allowed in the 4-hour time window from 9.30am–1.30pm GMT. A: detailed equity curve. B: weekly underwater equity curve. British pound/US dollar (FOREX), 30 minute bars, 21/10/2002-4/7/2008.
Optimised input parameters in terms of net profit: SLOW=44, FAST=1. Test without exits.
Back-test includes $30 slippage and commission. Charts from TradeStation 8.
A
B
How to develop a trading system step-by-step – using the example of the British pound/US dollar pair
Table 3.3: Main system figures of the LUXOR system with added time filter: Entries only allowed in the 4-hour time window from 9.30am– 1.30pm GMT. British pound/US dollar (FOREX), 30 minute bars, 21/10/2002-4/7/2008. Optimised input parameters: SLOW=44, FAST=1. System without exits, always in the market, long or short. Back-test includes $30 slippage and commission.
All Trades Long Trades Short Trades
Total Net Profit $115,502 $82,050 $33,452
Gross Profit $428,864 $226,323 $202,541
Gross Loss ($313,362) ($144,273) ($169,089)
Profit Factor 1.37 1.57 1.2
Total Number of Trades 902 451 451
Percent Profitable 42.02% 44.79% 39.25%
Winning Trades 379 202 177
Losing Trades 523 249 274
Avg. Trade Net Profit $128 $182 $74
Avg. Winning Trade $1,132 $1,120 $1,144
Avg. Losing Trade ($599) ($579) ($617)
Ratio Avg. Win:Avg. Loss 1.89 1.93 1.85
Largest Winning Trade $6,748 $6,748 $5,728
Largest Losing Trade ($2,531) ($2,531) ($2,442)
Max. Consecutive Winning
Trades 6 7 6
Max. Consecutive Losing
Trades 13 8 11
Avg. Bars in Total Trades 78.82 78.44 79.2
Avg. Bars in Winning Trades 116.73 113.29 120.66
Avg. Bars in Losing Trades 51.34 50.16 52.41
Total Slippage $18,040 $9,020 $9,020
Total Commission $9,020 $4.510 $4.510
Percent of Time in the Market 99.92%
Max. Drawdown (Intra-day
Peak to Valley) $18,894 $9,402 $17,378
Date of Max. Drawdown 24-May-06
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As we already mentioned the drawdowns have become slightly bigger – $18,900 maximum drawdown compared with $13,400 before. But this drawback and the fact that the trading system equity is not as steady as before are not the worst features. The weak point of the trading system in its current state is simply that it is really dangerous since trade reversals are only allowed in the four-hour window between 9.30am and 1.30pm GMT. If you get a reversal signal outside of this window, e.g. in the night at 1am GMT, since the market is closed the system cannot exit or reverse its position. Outside of your trading window you have to stay in the market for the other 20 hours, regardless of what happens. Of course the system is not tradeable like this since the limitations and the risks of the system would be too high if you are forced to stay in the market for 20 hours irrespective of any developments during that time. We have to urgently change this situation and extend our trading system by adding exits. By adding exits we want to create not only a profitable trading system, but also one which can be controlled in terms of risk.
3.5 Determination of appropriate exits – risk management
Look at it this way: whether your trading system resembles a gunslinger shooting from the hip or a well-aimed sniper lying in ambush, knowing where your trades are heading could mean the difference between riding into the sunset or lying fa-tally wounded on a dusty street at high-noon. To stay alive you must know when to draw and when to run.
Thomas Stridsman [1]
Everybody knows that stops are necessary but nobody really likes them. Often you get the feeling that the stop has just thrown you out of the market before it turned in your direction and you missed the big move.
In this section we use statistical research to investigate exits quantitatively. In the course of all of our past statistical investigations it has become obvious that an exit can never be considered independently from the relative entry. It’s important to be aware that the dynamics of the entry have a substantial influence on the dynamics of a useful exit or a reversal of your position. Imagine an entry into a quiet, not volatile market with low trading volume and compare it with an entry which was triggered during a phase of high activity, e.g. a “news-breakout” (Figure 3.10). In the first case it could be best to take profits at a close profit target as the market moves sideways without any direction. In the second case a wide stop and no profit target could be much better since these two exits
How to develop a trading system step-by-step – using the example of the British pound/US dollar pair
give the trade enough room to develop. Every profit target or stop which is placed too close would throw you out of the profitable trade too early. The “best” exit in this case would be the end-of-day exit when the big trading volume has diminished and the breakout has obviously finished.
For this reason we don’t recommend testing exits with artificially generated entries, e.g.
with random entries or with entries taken at the opening of every trading day. We found that working with such random entries leads the statistical results into a wrong direction.
The outcome is dominated by market situations which occur most of the time but which are not the typical ones applicable to your own, special market strategy.
Figure 3.10: The Dynamic of Exits. In the phase of low volume and low volatility different exits are needed than in the phase of increasing volume with the short breakout. Chart example was taken from Light Crude Oil, 5 minute, NYMEX from 22 August 2008. Chart and datafeed from TradeStation 8.
There are no universal optimal exits! If you are working with a different type of system or on another time scale you cannot transfer your existing exits to a different entry logic.
Of course you can take such exits as a rough guide but you must spend time developing suitable exits for the different entry or time scale.
To find appropriate exits for the strategy developed above we take a small excursion into the field of statistics. We analyse the course of the single trades in order to determine useful stop-levels and profit targets. This analysis looks a little bit exotic at the beginning.
As soon as you are familiar with it, however, you’ll be rewarded with a good
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