Over the last few weeks in the Quantifiable Edges Subscriber Letter I’ve posted a number of studies related to Fed POMO activity. I’m not the first to look at POMO. It is a topic I first saw on Zerohedge and have seen discussed many other places since. For those unaware POMO stands for Permanent Open Market Operations and it is how the Fed goes into the open market to buy (or sell) treasury securities. The net effect of this buying is an influx of cash into the system. It appears a portion of that cash makes its way through the banking system and into the stock market. It also appears that the net effect of all this Fed buying is a positive influence on the stock market. Conversely, when the Fed sells securities in the open market then it is pulling money from the system. This appears to have a possible negative influence on the stock market.
Note how the market has performed in accordance with past POMO activity. According to the Fed’s website, they are tentatively slated to perform buying every trading day from now through December 9th. Either Tuesday or Wednesday we should see the 20-day running total as shown on the bottom indicator exceed the highest levels in 2009. Based on the above chart, (and a number of studies I’ve conducted) it appears the old adage “Don’t fight the Fed” still holds true. If this is the case, then the Fed’s recent and scheduled activity should act as a bullish influence in the days and weeks to come.
As I showed a few days ago Thanksgiving week has had some very bullish tendencies on both Wednesday and Friday. Interesting about the current week is that both Monday and Tuesday have closed down in the SPX. Going back to 1961 I looked at performance on Wed through Fri after Mon and Tues were lower. There were only eight other instances. They are listed below.
Instances are lower than I would prefer but stats are heavily lopsided to the short-term bullish case.
This post is the beginning in a series which will provide readers some ideas on how they can take some of the edges they see here (and elsewhere) and use them to their advantage in their own trading.
Before getting into a theoretical discussion it’s important that I make sure everyone understands what it is I’m presenting when I show these studies in the blog and the Subscriber Letter. Over time I have pretty much standardized the statistics that I show in my tables. I have tried to strike a balance between giving enough information to make the table useful and giving too much information which could make it messy and confusing. Below is a sample study (with real results but a bogus description). I’ll use this as an example to refer to.
The top box of the study always lays out the conditions. Everything that was taken into account is described there. One thing to note is that I always run the studies on $100k/trade. This is because most of them look at the S&P 500. Since it trades at about 1,200 there will always be some leftover when buying into a portfolio (you can’t buy a half a share in Tradestation). The $100k makes the rounding error small enough so that it doesn’t have much of an affect. $10,000 would have too large of a rounding error. $1,000,000 would be better but then the numbers get so large it makes it more difficult to read.
With an even $100k I find the results easy to interpret. $1000 = 1% in the results columns. So in the above example the “Average Trade” shows a gain of $733 after 3 days. This is almost 0.75%.
Now let’s briefly review each column in the results table.
“X Days” – Most tests I run out over a number of days to see how the market has performed after the test conditions were in place. “X Days” just shows the length of time from the entry. The entry is normally assumed to take place at the close. The exits are also assumed to be at the close. The number of days refers to trading days – not calendar days. Note this column reads from the bottom up, which means all columns do. No reason for that. It’s just how I started doing it a long time ago.
“Net Profit” – This is the net gain or loss for the entire sample of instances included in the study. One thing to note is that I always “Buy” the setup. This is not because I am only looking for long edges. It is because it makes the table easier to read. A quick glance can tell me if the edge is bullish or bearish. Lots of positive, green numbers is bullish. Lots of negative, red numbers is bearish. A long time ago I would sometimes set the entry condition to “short” at the close. Then I could see profits from shorting. Doing this required me to read the entry conditions carefully and would occasionally lead to some confusion when I didn’t. So for purposes of easily reading the study tables, everything assumes a long position.
“Total Trades” – This is the total number of instances that triggered based on the study conditions. As in the case above, this number will sometimes be larger for Day 1 and then you’ll see a declining number of instances as you look further out. If you’re wondering why this happens, check out the June 18, 2010 blog post.
“Winning Trades” – The total number of trades that were showing a gain “X Days” later.
“Losing Trades” – The total number of trades showing a loss “X Days” later. The wining plus the losing trades typically add up to the total trades. In those rare instances when it doesn’t it means a trade was breakeven on that day.
“% Profitable” – This column simply shows the winners / total trades. Sometimes a 50% profitable situation can still show a strong edge. That would mean gains outsized losses by a large degree (or vice-versa). % profitable is important from a trading standpoint though. If a setup is 90% profitable it is generally less likely to put you through an extended drawdown as a setup that is 55% profitable with the same size average trade.
“Avg Winning Trade” – This looks at all the “winning trades” and divides them by the gross profits on those trades. (Gross gain and gross loss columns are not shown.) So in the table above, the “Avg Winning Trade” was up $692 after day 1. This means that of the 14 instances that finished higher the next day, the average gained just under 0.7%.
“Avg Losing Trade” – Just like “Avg Winning Trade”, but it is looking just at the losers. In this case after day 1, the 7 losers dropped about 0.9% on average.
“Win/Loss Ratio” – This takes the value from the “Avg Winning Trade” column and divides by the value from the “Avg Losing Trade” column. It can help you determine whether the reaction was typically more explosive on moves up or down.
“Profit Factor” – This is the stat I am asked about the most. It is a stat often cited by system traders. Profit Factor = Gross Gains / Gross Losses. Profit factors above 1 occur when there are positive net results and below 1 occurs when there are negative net results from a study. When thinking about the importance of profit factor, it is easiest to consider how 2 systems may compare. Consider 2 systems made a hypothetical $10,000 each over a specified time period. System 1 had $15,000 in gains and $5,000 in losses. Its profit factor was 3. (15k/5k = 3). System 2 also made $10,000 but it was on $100,000 in gains and $90,000 in losses. Its profit factor was 1.11 (100/90). Most people would find system 1 more appealing as it seemed to make the $10,000 with less effort and risk.
“Avg Trade” – This is simply the net gains divided by the total trades. Under most circumstances, I’ll use the information in this column to help generate estimates.
Last but not least I will often place a statement with additional information in a box below the results. This is typically information that can’t be seen in the table. A common bit of information I put here is how often the market might close up (or down) from the entry price at some point in the next few days.
In the next installment of this series I’ll give a brief discussion of attributes that would make a study compelling to me and entice me to incorporate it in formulating my market bias.
When a short-term decline that is already a bit overdone experiences a downside acceleration it will often mean an upside reversal is ready to occur. QQQQ's current pattern is showing a potentially powerful example.
These very simple requirements have led to some very strong results, both short and intermediate-term. Four weeks out the average trade has produced a gain in the QQQQ of over 10%. Even if this apparent upside edge does play out, I don’t expect to see gains this strong over the next month. Often the outsized gains were partially due to the volatile environment that was present when the study triggered. Many of these occurred during the wild 2000 – 2002 bear market in the Nasdaq. The current environment is carrying low volatility, so my expectations are dampened.
This downside acceleration concept is one I've found useful before. It is included in a few of the systems available with a Quantifiable Edges Gold Subscription. More details on the above study (and others) are available in last night's Subscriber Letter. Click here for a free trial.
While I don't always show it I do always look at the equity curve when evaluating studies to include in my analysis. Last night while conducting my research I came across a great example of why this is important.
Inside days have generally suggested a bearish edge when the market is below the 200ma and no edge much better than upside drift when above the 200ma. I found it unusual that the inside day came with an unfilled gap down so I tested the possible effects under these circumstances.
At first glance the numbers seemed to suggest a downside edge. A closer look showed the numbers to be misleading. Here are the results in table format.
Base on this the next 1-3 days would seem to have a bearish inclination. But here is a picture of the equity curve.
As you can see it has been a long time since this setup has produced compelling odds. Researchers should always take a look at the equity curve when considering whether to incorporate results into their analysis.
Another blogger who often makes this point is Michael Stokes of MarketSci. He did it again in his recent Thanksgiving returns post yesterday.
Yesterday afternoon the Quantifinder identified an interesting study that I last wrote about in the 3/23/10 blog. It looked at back to back outside days in QQQQ. I’ve updated the study below.
I also sliced this a few different ways (above/below 200ma, up/down close, etc.) and found little difference in the results. Despite the low number of instances I find this a compelling setup. You'll also note that on December 7, 2009 I performed the same study on SPY and found compelling results there as well.
Most swing traders understand that the market has a tendency to oscillate. In other words, strongly oversold conditions will often lead to a bounce and strongly overbought conditions will often lead to a pullback. The trick in trading a swing time frame is understanding when the likelihood to reverse is strong and when it isn’t.
Trying to sell short when an uptrend gets overbought can be a dangerous endeavor. Often there will be no downside edge when trying to short into an overbought condition in an uptrend. When the market is strongly overbought due to a sharp acceleration in the trend as occurred late last week, it may even suggest an upside edge. Below is a study from last night’s subscriber letter that demonstrates this.
We see here a mild upside edge.
Actually the upside stretch is even more extreme that I show in this study. There were some momentum studies in last night’s letter suggesting an even greater bullish edge. There are also a few active studies that suggest a mild pullback could be in order. In any case, the point is that though the market is short-term overbought, this is by no means an ideal short setup. And in general odds seem to favor a continuation rather than a strong, immediate drop.
Moves to new highs are often followed by brief retracements. This has especially been true since around the year 2000. (This is when chop became favored over day to day trending activity as discussed here.) In last night's letter I showed a study that suggested strong breadth and volume in conjunction with a new high has favored further short-term upside. That study is below.
Now let's look at what has happened since 2000 when these new highs were not accompanied by strong breadth and volume.
Fed Days have generally exhibited an upside bias for about 30 years. Many times this has been thanks to the Fed giving a confidence boost to a struggling market. But what of those times where the market is already at an intermediate-term high. With the SPX closing at a new rally high yesterday this is the situation the market is now in. Below is a study that take a look.
What I see here is that there has been no tendency for the market to advance under these circumstances. There could even be a slight downside edge, but the numbers aren’t compelling enough for me to bank on that. I’d simply view it as neutral.
I've discussed before how the 1st day of the month tends to have a bullish bias. This has been the case since the late 80s. (Perhaps due to the rise in popularity of the 401k.) In July 2009 I looked back to 1987 and broke down the 1st day returns by month.
For whatever reason, the 1st trading day of November has shown a positive bias for a bit longer than most months. Below are the results looking back to 1978.