I have been thinking about one writer’s critique of my series on randomness and the problem of finding a trading edge.  His charge was essentially that I had copped out in my last post in that series by not actually providing anything of value to answer the questions I had raised, beyond merely saying that “every edge we have as technical traders comes from an imbalance of buying and selling pressure.”  This raises a few interesting points.  First of all, I actually think that sentence sums up the whole of technical analysis pretty well and is the single most important thing for any technical trader to keep in mind.  It is the first and the last thing that anyone trading any kind of pattern needs to think about.  Trade patterns with discipline and consistency, and you may make some money.  Trade those same patterns, but focus on applying them in environments where there is a real imbalance, or define your patterns to already filter for those imbalances, and you will likely do much better.  It is no exaggeration to say that this idea is the very essence of the entire discipline of technical analysis.

Now, I know this answer smells a little bit like a simple platitude and traders, being practical people, want better answers to the question of where to actually buy and sell.  Right?  Well, I think the problem (if there was one) with my earlier answer is that it may be too vague… it may leave too many other questions unanswered.  Let’s address that today by taking one very specific case and thinking through the problem of how to identify buying and selling imbalances.  I think this will be interesting and useful to many of our readers, and may give you some ideas you can apply to your own trading ideas.  (Also, before we start, I want to define the scope of this post clearly.  This is about the concepts and thought processes behind quantifying the kind of trading edge that may or may not exist around a specific kind of imbalance.  This is not a math lesson.  I will cut corners and oversimplify a lot of the stats and math in this article just to make the points with more clarity.)

Last week I was a bit under the weather and traded from home a few days, so I had some time to think about a couple new ideas.  I realized that I have been tweeting the In-Play list from the SMB Radar every morning around 9:45, so we now have a real-time record of what we had identified as being In Play early in the day.  Joe P and I created the Radar specifically for the purpose of finding stocks with unusual order flow because we knew they offer the best opportunities for daytraders, so, theoretically at least, this In Play list I have been tweeting should have some tradable information in it.  At this stage of the process you have to spend some time finding the right questions to ask:  what do I mean by tradable information?  This is a non-directional list of In Play names–we need some clear, systematic rules for which ones to buy and which ones to short.  Where do we execute?  Where do we get out?  How do we choose which ones to trade?  I spent a few hours obsessing over these questions myself, and then one of the very nice guys I mentor had the misfortune of sending me an IM to ask how I was feeling and if I was over my cold.  Much to his dismay, I’m sure, he received a half hour unbroken download of every random thought I had about the In Play list and I started nailing some of these questions down.  We would go back manually and copy the list from my tweets, and then we would keep it simple — we will find the price of each stock at the time of my tweet and about 6 other prices at other points in that day.  And, oh yeah, this has to be done by hand so good luck, get some help, and you don’t have any way to know this yet, but it’s really a 4 day project and I’m going to be checking in with you about noon tomorrow asking why it’s not done… 🙂

Now, I think there are a few good lessons here.  We started with broad questions and worked down to some more concrete definitions.  I knew in general that I wanted to be long strong stocks and short weak stocks, so I had him get all the opening prints for that day for each stock.  Maybe it also makes sense to look at whether we opened up or down, so get the previous close too.  And we need to measure performance through the day, so let’s just grab some random prices at 12:00 2:00 and the closing print.  It is also important to notice that this is very, very coarse analytical work.  I do not know that 12:00 is the “right” time to mark these trades, but I just need to look at enough of them to be able to see if we have something that warrants further investigation.  At this stage, the goal is not to define a system, but simply to check for a real statistical tendency.  (And, the bad news is, these tests fail well over 95% of the time in my experience.  Things we think should work usually don’t, at least in terms of hard, cold stats.)  I sent an email to my entire group of trader trainees asking for help with this project, and I had a very important ulterior motive for doing that.  Many of the things I have learned about markets have come from this kind of manual research.  Your brain absorbs patterns differently when you actually go through charts and write down prices… a different level of comprehension and assimilation takes place.

Let’s fast forward a bit and just say that the results we saw from this limited manual test were actually pretty astounding.  I won’t bore you with the numbers, but we saw enough of an edge to justify digging a little deeper.  The next stage was to standardize these results.  I tweeted at 9:45 some days, 9:37 some days, 9:58 others… these random times did not really make sense, but were just what I could manage as I was trying to trade and do a thousand things at once.  So, for a better backtest, I wrote code that went back and grabbed the in play rank for the complete stock universe at a specific time each day.  In addition, I was now able to take some prices from other times as well so we could see how the trades develop through the day.  What started out as a handful of stocks has now quickly grown to 40 days * 450 stocks (even though we’re just looking at the top 10 or so each day) * 20+ data fields for each stock… but let’s skip over a lot of those ugly details and go right to where it gets interesting…

After doing a little more number crunching, I zeroed in on the top 10 In Play stocks from the SMB Radar for each trading day in January, and applied a simple rule set of entering a trade at one time and exiting a few hours later.  (I’m not going to give the exact times, but they don’t really matter.)  This is still a pretty primitive measure because there is no stop and also no profit taking mechanism in between these two times; we are still simply trying to nail down the edge a little bit better.  Let’s take a look at the trade stats:

	Simple	Filtered
N=	80	17
Mean	0.22%	0.84%
Median	(0.03%)	0.12%
StDev	1.59%	1.65%
Largest	4.66%	4.66%
Smallest	(3.05%)	(1.59%)
Win%	50.0%	64.7%
Avg Win	1.43%	1.75%
Avg Loss	(0.92%)	(0.54%)
Ratio W/L	1.6	3.2

The column labeled “simple” shows the returns if you did this for each of the top 10 In Play stocks at this time, with the entry criteria that you went long stocks that were up from the opening print and shorted stocks that were down from the opening print.  Not bad… 50% win ratio with my average win 1.6 times my average loss.  Can’t complain about that.  Average trade is 0.22%… not thrilled about that but it is tradable from a purely systematic standpoint.  So, this is great.  This is concrete proof of concept, and I think is an illustration of my claim that imbalances create trading edges.  If you simply bought and shorted all stocks with no regard to In Play rank (yeah… Idid that test too) at these same times, following the same entry criteria, you would find no edge whatsoever.  Keep in mind, stocks get on that list because they are displaying unusual trading activity in the first place… because they have shown evidence of a buying / selling pressure imbalance.

But what is this “filtered” column I see?  Exact same stocks, except the entry criteria was now modified so that you could only buy if the stock was up 2% more from the open than the S&P 500 (vice versa for shorts).  Think about the logic behind this idea of using “excess” (in excess of the broad market) price movement to trigger trades.  If a stock is up 3%, but the S&P is also up 3%, is there really buying pressure in that stock?  Maybe, but it’s much more clear if the stock is up 3% over the same time the market is down 2%… now we have something interesting.  Applying this very simply price action rule (and, again, let me stress that it is price action that demonstrates an imbalance of buying / selling pressure) reduces the number of trades in the system by nearly 80%, doubles the average win/avg loss ratio, raises the win ratio to a very respectable 64%, and raises the average trade to a solid 0.84%.  For short-term intraday stock systems, these are pretty solid numbers.

I apologize for the length of this post (somehow it got to be 1:15 AM… I think the Calvados has something to do with that…), but I hope this has been interesting and useful.  I will continue to try to tweet this In Play list from the Radar every morning, and it might be worth spending some time thinking about how to trade ideas from that list in light of some of the ideas in this post.