Historical testing is the primary method that some people rely on to develop confidence in trading but it is replete with pitfalls. For example, I talked about the data pitfall in last month’s article. As a result, my goal in the next few articles is to take a trading methodology that I feel confident in and determine what might be involved in doing historical testing with it. I’ll probably devote at least three articles to that topic, but the exact number will depend upon how the research develops.

First, I feel confident that if you buy stocks that show efficient uptrends (i.e., they are basically fairly straight lines going up), with a 25% trailing stop and risking 1% then you will make nice profits under most market conditions. I illustrated this in the Market Mastery newsletter in 2001 and in the recent evaluation of our portfolio. However, a number of questions remain. 

1) Is it possible to automate this form of trading? In other words, how do you take something that’s fairly discretionary and turn it into something that’s objective? Right now, it’s my subjective judgment that determines whether or not something is a good efficient stock. (This question, by the way, is probably the most difficult question for most trading systems.)

2) If the method can be automated, what is the formula that will totally define an efficient stock for us?

3) How can we overcome some of the data problems that are present in historical stock data?

4) What market conditions are favorable for this method and what market conditions should be avoided?

5) And what can we expect from this method long term?

I don’t expect to be able to answer all of those questions in these studies, but if you can begin to understand some of the problems involved in backtesting, then I’ve met my objective.

STUDY: First an Attempt to Automate Efficiency

In this study I’ve been working with Bob Spear, the developer of Mechanica software, to develop an algorithm to buy efficient stocks automatically. We decided to work with the S&P 500 database, which presented our first problem because it basically represents today’s S&P 500 even though the database goes back to 1980. Wouldn’t you love to know what the S&P 500 of 25 years from now would consist of? What new stocks will be so strong that they will be considered one of America’s top 500 stocks? All you’d have to do is just buy and hold the stocks that are not on it now and you’d make a fortune. But that’s what we were dealing with. Furthermore, all of the stocks were split and dividend adjusted. That means that a stock like Microsoft, which came out in 1986 begins at a price of about 15 cents (which also isn’t very realistic). But this is taken care by virtue of the fact that we buy an outsized position (10s of thousands of shares) so the accounting arithmetic works.

Automatic Entry

The problem with automatic entry is that my efficiency algorithm, which is a composite of four efficiency algorithms, is just a preliminary screening tool. You could probably use a channel breakout or a list of stocks making new highs and do a similar screening. It doesn’t matter because I still have to look at the charts to pick the stocks I want to buy. Our first solution to that was to do some sort of smoothing function. Bob programmed Mechanica to rank the S&P 500 in terms of smoothness for each day of the 25 year database and store that ranking for later use. Our first algorithm was to rank the inverse of the standard deviation of the change in daily prices. We took the inverse because we assumed that the smallest standard deviation would be the smoothest.

Mechanica, then did the following: on the first trading day of each month, it calculated the composite efficiency of each of the stocks in S&P 500. The first thing it did was determine if the composite efficiency was greater than 8. If it was greater than 8, it then purchased the top 10% of them in terms of smoothness. That was it – our automated screening process. But did it work?

Exit

The exit in our model was very simple -- we used a 25% trailing stop. Thus, if the price dropped 25% from our entry, we were out. However, whenever the stock made a new closing high, the stop was adjusted, now being 25% away from that new high. It was always raised and never lowered. Thus, the stop became both a reason to abort a trade and our profit taking exit. I used the 25% trailing stop because it seems to be an excellent substitute for buy and hold.

Position Sizing

We began each position with a 1% risk based upon the Total Cash available. Since our portfolio started out with $100,000, each position would risk $1000 and be a total investment of $4000. When our total cash changed, our 1% risk would always reflect that change.

Commissions and Slippage

Some of our positions were huge. For example, if you were to risk $1000 with a 25% stop on a split adjusted MSFT starting out at say 16 cents, then our initial risk would be 4 cents. We’d be buying 25,000 shares. The cost of buying those shares, especially in the mid 1980s would be tremendous. However, we wouldn’t really be buying a split adjusted stock. Instead, we’d probably be buying a $25 stock. As a result, we elected to include a 1% cost per trade (1% in and 1% out) for commissions and slippage. 

Results

The results of this study made us look like geniuses. These results are summarized in Table 1. We are able to turn $100K into $265 million in 25 years.

Table 1: Summary Results
Initial Balance	100,000		$ Won	330,803,321
Net Win Loss	265,541,946		$ Lost	65,261,375
Ending Equity	265,641,946		Incentive + Fees	0
ROI	265542%		Other Credits	0
Compound Annual ROI	37.83%		Commission/slippage netted	0
Max Drawdown %	38.65%		Other Debits	0
Max Drawdown % Date	19871204
Longest Drawdown in years	1.49		Long Wins	382
Longest Drawdown Start Date	20020410		Long Losses	292
Longest Drawdown End Date	20031006		Short Wins	0
MAR Ratio	0.98		Short Losses	0
Sharpe Ratio	1.93		Long $ Won	330,803,321
Return Retracement Ratio	5.5		Long $ Lost	65,261,375
Sterling Ratio	0.54		Short $ Won	0
Std. Dev. Daily % Returns	1.21%		Short $ Lost	0
Value at Risk (99% confidence)	3.33%		Largest Winning Trade	22,969,429
Average Expectation Value	33.27		Largest Losing Trade	2,209,232
Expectation	176.28%		Transactions netted at open	0
Kelly	0.45		Average Winning Trade	865,977
Sum of Up % / Sum of Down %	1.36		Average Losing Trade	223,498
Percent New Highs	16.74%		Max Consecutive Wins	16
			Max Consecutive Losses	13
Trades	674		Days Winning	3,465
Trades Rejected	701		Days Losing	2,729
Wins	382		Average Days in Winning Trade	378
Losses	292		Average Days in Losing Trade	80
Percent Wins	56.68%
Avg $Win to Avg $Loss	3.87		Number of Margin Calls	0
			$ Largest Margin Call	0
Start Date	19801001
End Date	20050422		Size Adjustments	0
Max Items Held	16,487,507		Size Adjusted Items	0
Total Items Traded	117,196,789
Total Slippage + Commission	9,000,400		Process time (H:M:S)	0:28:15

Notice that we made a compounded annual return of 38.8%. How many people did that from 1980 through 2005? Our worst drawdown was 38.7%, which occurred in 1987. It made 674 trades and rejected 701 trades (because we were fully invested). 56.7% of our trades made money and the average win was 3.87 times bigger than the average loss. We also spent 378 days in a winning trade versus 80 days in a losing trade. And we spent  nine million four hundred dollars in trading costs, so you can’t say that low costs influenced our results.

The Sharpe ratio of this system is nearly two and the System Quality Number is close to Holy Grail range.

Figure 1 shows the distribution of yearly returns. Notice that we only have two years of negative returns, 2002 and the last year of trading. Two losing years out of 25 is an exceptional, especially with no screening for bear markets. And during nine of the 25 years we made over 50%.

Yearly Returns from Our Efficiency System.

So what is your reaction to these results? Do you need more information or do you want to jump on this and trade it? Remember, I’ve already told you that I believe the efficiency model is an excellent long term trading system.

Perhaps you’d like more information. After all, the maximum drawdown is nearly as big as our compounded annual return. Let’s look at the drawdowns produced by this system. 

These are shown below.

Drawdowns in our Efficiency System

Notice that most drawdowns did not go over 15%, but twice we exceeded 35% and we exceeded 25% quite a few times. Was that due to our risk? Well, the table below plots the risk against the equity curve.

Risk Versus the Equity Curve

Our maximum initial risk should only be 25%, but since one position could get very large and have a significant risk, you’ll notice that the total risk sometimes gets bigger than 40% (e.g., in 2002-3).  Nevertheless, risk is still about what we’d expect it to be in this system.

So would you trade it?  All you have to do is buy stocks that are going up in a straight line and put a 25% trailing stop on them, risking 1% of your portfolio with each.  You should be able to do that, but will you?  Why or why not?

Problems with the Study

Many of you might be salivating to trade this system.  Based upon these results I could probably sell it to others.  However, I see a number of problems with the study, which we’ll be addressing in future articles with additional studies.  Let me just list the problems here to see how many you might have caught.  (Oh, and if you see some I didn’t mention, please let me know. Email van@iitm.com).

1. Why are the worst performing years 2003 and 2005?  We had a bear market in 1980 through 1982.  We had the crash in 1987.  We had a nasty market in 1990-1.  What happened with those and why didn’t we have drawdowns in those years?  Some of the problems below might explain that.
2. We have the S&P 500 data problem.  We are basically trading a limited universe that includes the very best performing stocks during that time period.  And those stocks were pre-selected.  I believe that the efficiency method is good enough to find the S&P 500 that will exist 20 years from now, but that still doesn’t negate the selection problem with these data.  To solve it, we’ll be looking at a database of stocks that includes the real S&P 500, including those stocks which no longer exist.  Finding such a database is very difficult, but we have succeeded in creating it!.
3. Perhaps any trend-following algorithm would produce similar results with this database.  We’ll soon test a 180 day breakout system to see how it compares.  At least with that system, I’ll know that the stock is doing what I expect it to be doing when we buy it, making a new 180 day high.
4. Our smoothing algorithm is flawed.  Remember that we are basically ranking stocks according to the standard deviation of daily changes in price.  The smaller the standard deviation the greater the ranking, which is quite interesting because it tends to favor the low-priced split adjusted stocks in our database.  Thus, it is biasing us to buy the very stocks that will go up the most.
5. Third, one of my Super Traders looked over a list of the trades made during the study.  He concluded that the stocks being purchased were anything but efficient.  Thus, our algorithm that we automated doesn’t seem to be doing what we wanted it to do.
6. Fourth, we will have the most cash during the short bear markets of this 25 year period.  Thus we should be situated to accelerate out of each drawdown.  Now that’s good, since most of the period under consideration was a secular BULL market.  But today we’re in a secular bear market.  What if we have a 3-4 year down period?  We’d be buying stocks as the market continues to go down.  We could have a 25% loss on top of a 25% loss on top of a 25% loss.  That would be a disaster.  Can we filter out the bear market periods and make this perform better?

During the next few months I’ll be presenting one or two additional studies each month to address these issues.  But the important lesson for you was if you saw the flaws in our backtesting.  Or did the result make you just want to jump on the system and trade it?  These types of flaws occur all the time and that’s one of the things I’d like to point out.

By the way, if you have some interest in Mechanica, which we are using in these tests, then  go to the Mechanica web site -- http://www.mechanicasoftware.com.  Mechanica is the new windows version of Trading Recipes.

 

Until next week, this is Van Tharp.

 

 

In some of the educational stories I have written, I discussed my "primary" trading tools and my "secondary" trading tools. I also mentioned that the more tools one has in his or her "Trading Toolbox," the better the odds for trading success. In this educational feature, I want to focus on one of the most basic--yet most powerful--trading tools: the trend line.

As a refresher, I'll reiterate that my "primary" trading tools are basic chart patterns, such as triangles, double-tops and bottoms, head-and-shoulders formations, flags, pennants, etc.--and of course, trend lines. I also consider fundamental analysis a primary trading tool. My "secondary" trading tools are the computer-generated technical indicators, such as moving averages, Slow Stochastics, MACD, RSI, DMI, etc. Volume and open interest are also categorized as my secondary indicators.

I believe that futures and stock traders can still be successful without the aid of computers. Two of the most famous and successful traders never touched a computer--Jesse Livermore and Richard Wyckoff. When I first got into this fascinating business, I had no computer to give me an RSI or DMI or moving averages. I had a weekly chart service that was mailed (U.S. Postal Service, not email!). On the markets I was following, I plotted the daily high, low and close on the daily bar chart and drew trend lines with a ruler and pencil. For the longer-term monthly and weekly continuation charts for nearby futures, the chart service would send out updates about once a quarter.

I'm sure there are still some traders who use a chart service and trade successfully. Certainly, the evolution of computer trading and charting software the past 15 years has made technical analysis much easier. But the point I make here is that while computers have made the chore of technical analysis and charting easier, they have not made trading success any easier.

In the early 1990s the "neural networks" were the buzzwords in futures trading. Magazine articles espoused the wonders of using "artificial intelligence" to virtually do your all of your trading for you. That fad seems to have come and gone--thank goodness! Now, the "back-to­basics" approach to trading has regained popularity. (To many of us, this approach never lost popularity!)

Before discussing trend lines, I want to share with you one anecdote, regarding all those computer-generated, whiz-bang and bells-and-whistles technical trading indicators. Many of them remind me of the Sears Robo-Grip pliers I got for Christmas a couple years ago. These pliers were touted as a break-through wonder tool that does it all. However, in reality, when you've got a tough nut or bolt to loosen, you head for the toolbox and your trusty old box-end wrench or vise-grips. In trading, my box-end wrenches and vise-grips are the basic chart patterns that you can plot (by hand if you have to) on a chart.

Now on to the venerable trend line. Here is what respected technical analyst John J. Murphy says about trend lines in his excellent book, Technical Analysis of the Futures Markets: "The importance of trading in the direction of the major trend cannot be overstated. The danger in placing too much importance on oscillators, by themselves, is the temptation to use divergence as an excuse to initiate trades contrary to the general trend. This action generally proves a costly and painful exercise. The oscillator, as useful as it is, is just one tool among many others and must always be used as an aid, not a substitute, for basic trend analysis."

On drawing trend lines on the charts, the methodologies vary--and there are really no hard and fast rules. Like much of technical analysis, drawing trend lines is more art than science. When drawing an uptrend line, you draw a straight line up to the right along successive "reaction" lows. A downtrend line is drawn to the right along successive rally peaks. It's important to note that the more times the rally peaks or reaction lows touch the trend line, the more powerful the trend line becomes. The rule I use for the negating of trend lines is that prices must penetrate the trend line resistance or support level--and then see follow-through strength or weakness the next trading session. However, if prices make a big push above or below the trend line, then that trend line is negated without needing follow-through confirmation.

John Murphy's book, which I mentioned above, has much more detail on trendline analysis as well as other basic chart patterns.

That is it for this week. You can also visit my daily blog at www.traderblogs.com. Have a great weekend!

 

Over the past week, the dollar has struggled to sustain gains triggered by firmer than expected data releases. Sentiment remained weak amid fears of a longer-term central bank and institutional switch away from the US currency.

Following the stronger than expected employment data at the end of last week, markets cut the chances of an October interest rate reduction.

The FOMC minutes from September confirmed that GDP growth and inflation forecasts had been downgraded. The Fed was concerned over the housing sector and stated that future policy would be dependent on the economic data. There were mixed comments from Fed governors, although the overall remarks indicated reduced fears over inflationary pressures.

There were strong suspicions that the Fed would err on the side of a further cut in rates with markets pricing in a 70% chance of a fourth-quarter cut.

The US trade deficit fell to US$57.6bn in August from a revised US$59.0bn the previous month with the underlying deficit continuing to decline.

US jobless claims fell to 308,000 in the latest reporting week from a revised 320,000 previously while retail sales rose 0.6% in September with a core 0.4% increase, although sales were inflated by high gasoline sales.

The ECB was concerned over inflationary pressure with Bundesbank head Weber stating that the bank may need to pursue a restrictive policy.

ECB officials continued to warn over excessive currency movements. Their main focus was on the need for Asian currency gains rather than protesting against the dollar’s value which prevented any dollar benefit.

Fed officials and US Treasury Secretary Paulson made some warning remarks over the weak dollar. Eurogroup head Juncker warned that intervention did not have to wait for the G7 meetings from October 19. The Euro dip in US trading on Thursday invited some speculation over covert central bank intervention.

The Bank of Japan left interest rates on hold at 0.50% following the latest policy meeting by an 8-1 vote with Mizuno calling for higher rates. Bank Governor Fukui repeated his comments that policy should be adjusted gradually with some hints that a December rate increase was realistic.

The yen was still undermined by fresh interest in carry trades with the capital account recording net outflows in the latest week. The Japanese currency weakened to lows beyond 167.50 against the Euro and 117.60 against the dollar.

The Swiss currency was also undermined by renewed selling pressure on low-yield currencies and retreated to record lows against the Euro beyond 1.68.
 
Sterling had a tough week as fears over capital flows were compounded by growth concerns. There was some evidence of Sterling selling associated with the RBS-led takeover for ABN Amro and underlying investment flows appeared negative.

The latest RICS survey of agents reported a net balance of 14.6% reporting a drop in house prices for September from a revised 3.3% the previous month, the weakest survey for two years.

There was a stronger reading for retail sales with a 3.0% like-for-like annual increase according to the British Retail Consortium (BRC), although confidence in future spending trends deteriorated.

Input producer prices rose strongly in September with a 3.2% monthly increase, but output increase was subdued with a core annual increase of 2.2%. The August trade deficit fell to GBP6.85bn from an upwardly-revised GBP7.4bn in July.

The UK currency rallied to near 0.69 against the Euro before weakening sharply back towards 0.70. The UK currency also failed to hold above 2.04 against the dollar.

Commodity-related currencies had a generally strong tone over the week as prices remained buoyant. The Australian dollar tested 23-year highs above 0.90 against the US dollar with a peak around 0.9050 while the Canadian dollar strengthened to highs beyond 0.98 against the US currency.

Have a great day and a wonderful weekend.