A Closer Look at Profit Targets

I’ve decided to postpone the ATR stop studies one more week and first take a closer look at percentage based profit targets in more detail. Percentage based stops are a more intuitively familiar and thus a better way to lay out some fundamental concepts we will also be addressing when we look at ATR stops. It will also provide us with data that we can compare and benchmark the ATR stops against. Many of the concepts are probably familiar to most but looking at the data in this way will make it possible to arrive at some basic mathematical “truths” when it comes to stop combinations. Fortunately, these “truths” are universal and applicable on any time-frame, from short-term day-trades to long-term buy-and-hold investing. It is imperative that a trader understand how the manipulation of these criteria affect the way your system will behave and what to expect out of it.

This week also makes it abundantly clear why it is absolutely critical that a trader or investor has a specific agenda when it comes to putting together one’s rule set. There is no magical combination of numbers, no “Holy Grail”. With certain benefits come certain drawbacks and understanding these concepts is crucial for putting together a system or set of rules that work specifically for what one is attempting to achieve. Perhaps the most important questions one must ask are: What time-frame am I most comfortable trading in? How often do I want to trade? How often do I need to be “right”?

As we will see, a protective/profit stop pair provides an enormously flexible technique for adapting a system fit the answers to these questions.

This week we will use some of the entry criteria collected in last weeks tests. I have chosen numbers that are balanced between providing us enough trades for statistical relevancy while still offering us enhanced performance over some of our earliest tests.

As usual, we will run the test on three separate portfolios to increase our confidence that the results are systemic and all tests will be run from 01/01/1998-12/31/2007.

Our entry criteria will a liquidity condition requiring 10-day average daily volume to be greater than 100,000 shares. In addition, we will require a volume surge over 10-day average daily volume to be >= 200% and a price breakout to be >= 2x the 10-day ATR. Trades will be entered at open with a market order the day following the entry signal and $1000 worth of shares are purchased for each trade..

Let’s being by looking at EDR=Expectancy per dollar risked. It is calculated by Average Trade/Average Loss. The results are in dollars with .28 representing 28 cents per dollar risk.







These tests are remarkably similar and allow us to begin to make some basic assumptions as they relate to the implementation and selection of stops. In addition to these charts, I will also post the raw results at the end of this blog entry that should be used to supplement the graphics.

From these results, we can state the following with reasonable certainty:

The higher our profit target, the higher our expectancy per dollar risk, irregardless of what protective stops we use. This can be clearly associated with upward price drift. The higher our price target, the longer our average winning trade and thus the increased benefit from this upward price drift. We also know however that with increased trade duration comes increased risk.

Referring to the raw data, we see that the greater the spread between the protective stop and the profit target, the lower our win rate. For our Nasdaq portfolio, we see that a 2% protective stop, coupled with a 40% profit target had the highest expectancy but only produced winning trades 13.79% of the time. Could anyone actually tolerate trading a system with that win rate? Probably not but the numbers do make sense, our winning trade will be 20 times our average loss and since we are correct 13.79% of the time, a sizable profit is derived from that difference.

The inverse is obviously true as well. In our Nasdaq portfolio, a 10% protective stop (our largest), coupled with a 4% profit target (our smallest) produces winners 75% of the time but our average trade is only $9.06, compared to $80.89 for the 10%-40% combination, with an EDR of only .09 cents per dollar risk.

Ironically, with any fixed pair of stops, an increase in win% is the only way to increase net profit for a given set of stops. Your average loss is fixed, your average win is fixed, and therefore winning more often is the mechanism by which more profit is generated. In the previous two entry studies here and here, an 8% protective stop with a 20% profit target was held consistent. The increase in performance with the ATR entries can be associated with an increased win% at these stops.

Now lets look at a more conventional performance barometer, PF=Profit Factor. This ratio is derived simply by dividing gross profit by gross loss.







In these test, we benefit from upward price drift with both our protective and profit stops but unfortunately, as with EDR, a higher PF comes with a lower win rate. This paradox is perhaps what has allowed trend following to continue working for so long. There is a psychological barrier that exploits a fundamental characteristic among most of us, the need to be “right”.

The last set of tests does however offer a different perspective.
EER=Efficient Expectancy Ratio. This is our EDR/Avg. Days in Winning trades. This number is meant to reveal the most efficient use if capital as it calculates expectancy per dollar risk per day in trade. The results are in dollars with .01 representing 1 cent per dollar risk per day in trade.







Here we begin to see a different picture emerge. One of the most critical considerations for an active trader is the efficient use of capital. These tests begin to prove a less conventional, but more useful barometer for deploying this capital to quickly roll into and out-of trades, maximizing profit and minimizing dead-money in go-nowhere trades. Using this ratio as a gage, we see that a very tight protective stop, coupled with a modest price target is the most efficient in this regard. Again referring to our Nasdaq portfolio, a 2%-12% combo is the retrospective “ideal”. In this scenario, our win rate is only 25% but our average win is 6x our average loss. Our average trade duration for winners is also only 13-days which means with a 12% profit target, our winners are running at an average rate of nearly 1% per day. Trades that don’t immediately move in our favor get cut quickly and we are taking profits before trades start to linger.

RAW DATA







Everything we looked at today certainly provides credibility to the popular adage that stops have a larger impact on a system than do entries and the manipulation of stop combinations is one of the most effective methods of tailoring a system to one’s needs. So which if these techniques are “best”? I do not believe there is a single answer to this question and a position sizing strategy designed to take advantage of whatever stop combination one is comfortable with will have a tremendous impact on the bottom line, which is really what we are all concerned with. In a few weeks, after further building up our trading “kit-of-parts”, we will look at a few money-management/position sizing combinations to really see what these stop combinations could really make with trading cost, slippage, and compounding added to the equation.