“Normal Volume and Range”

BarroMetrics Views:  “Normal Volume and Range”

Robert, a Forum-Twitter subscriber, asked me how I work out ‘normal’ range and volume.

The software I use, Market Analyst, calculates the mean and standard deviation of average true range of the bars bounded by two dates. I use Barros Swings to identify the dates.

My historical  norm is usually determined by the structure of the Second Higher Timeframe. Since I trade the 18-day swing (monthly trend), my second higher timeframe is the 12-month swing (yearly trend). Figure 1 shows that the yearly trend has a confirmed sideways structure. Hence my calculations will begin from the March 2000 high.

The next thing I do is start a data set from the most recent First Higher Timeframe extreme – for the 18-day trader, this is the 13-week extreme. In this case. I started a set at the March 2009 low. Finally I have a set that defines the trader’s timeframe (me, 18-day) structures that have taken place since the  most recent First Higher Timeframe extreme.

Figure 2 shows the various calculations.

  • The Green shows the 12-month data (from : March 2000 high). The ATR is 17 to 18 with a standard deviation of 11 to 12.
  • The Purple shows the 13-week data (from March 2009 low). The ATR is 16 to 17with a standard deviation of 7 to 8
  • The Red shows the 18-day data (from July 7 2009 low). The ATR is 14 with a standard deviation of 6.
  • The Brown shows the latest 5-day data (From Feb 5 2010 low). The ATR is 12 with a standard deviation of 5.

Normal for me is mean +1 to mean -1/2 standard deviation. Thus Normal range in the current move is 17 to 10; the Normal Range historically is 30 (18 + 12) to 11 (17 – 6).

Generally I like to see current ranges and data close to their historical data.

I do the same calculations for Normalised Volume.

Figure 2 also shows why I use Normalised Volume rather than Volume. You’ll notice the “U” shape brought about by the expiry of futures contracts. The Volume tends to peak in the week of expiry and then levels off.

Figures 3 shows the Normalised Volume. As you can see, there is no ‘U’ shape.  I have added Figure 4 which shows the usual “U” shape without the ATR data.

By the way, a friend of mine sells a software program that creates Normalised Volume off csv data. You can contact him at:

“Kym Haines” <khaines@jordanit.com.au>. 

The software is not ‘pretty’; in fact it still has a DOS interface. But it is effective and that’s what counts in my book.

I make the usual disclaimer: I obtain no benefit from recommending the program. I just happen to think it’s great value.




FIGURE 2 S&P Mean & Std


Figure 3 S&P Normalised Volume


Figure 4 S&P Volume

The Death Zone

BarroMetrics Views: ‘The Death Zone

Baz asked: “When is a Death Zone not a Death Zone?”

To answer that question, I’ll identify the zone, explain the rational and assumptions, then answer Baz’s question. The blog must necessarily be a summary of the material in Nature of Trends that ran for most of a chapter.

The Death Zone occurs in a probable sideways market  at between the 33.3% and 50% or 66.7% and 50%. A probable sideways market is signaled when:

  1. There is a retracement of at least 78.6% of the prior impulse move and/or
  2. There has been a successful retest of one of the extremes.

Figure 1 shows the two conditions. ‘C’ must be at least 78.6% of AB to qualify as a successful test.

Figure 2 shows the S&P with 12-month swings (yearly trend, Green), 60-month swings (5 year trend, magenta), and the equivalent of 13-week swings (black). The chart shows that when their lines turned down at C, both the yearly and 5-yearly trends were probably in a sideways market.  The yearly trend confirmed a down trend with the line turn at D.

The normal Death Zone is the opposite end of the Value Area from the most current extreme. In Figure 2, this is the  zone 33.33% and 50%, 1304 to 1177.

To understand my rationale behind the Death Zone, you need to understand my view of the price action within a sideways market.

  1. In a sideways market, the market rotates from Primary Sell Zone to  Primary Buy Zone to Primary Sell Zone etc until a breakout occurs.
  2. In a sideways market, when the market accepts beyond one Value Area Extreme, we can reasonably expect it to continue to the other end. In Figure 2, with acceptance above 1051, we should see prices move the 50% and into the Death  Zone.
  3. Until the market accepts beyond one of the Value Area Extremes, we can expect the market to treat the Value Area as a mini-sideways zone.
  4. To confirm that the sideways ranges are intact, the breakout from the Value Area should be in the same direction as the original entry into the Value Area. In Figure 2, the market came from the Primary Buy Zone. Hence if the sideways structure is intact, eventually we should see a breakup above the top of Value (33.33%) and a move towards the Primary Sell Zone.
  5. Once the market accepts beyond one Value Area extreme, we should see a move beyond the opposite extreme before it accepts beyond the originating Value Area Extreme. Thus in Figure 2, once the market accepts above 1051 (66.67%), eventually we should see  a move to 1156 to 1461 (Primary Sell Zone) BEFORE we see re-acceptance below 1051.

Point (5) contains the answer to Baz’s question: A Death Zone is not a Death Zone when the market accepts beyond the Value Area Boundary. In Figure 2, the Death Zone is not a Death Zone if we accept above 1304 before we see re-acceptance below 1051.


FIGURE 1 Sideways Formation


FIGURE 2 S&P Monthly

BTW Just received NOT ranks #2 at Amazon:By Idkit

NOT #2

The Evolution of the Market Profile

BarroMetrics Views: The Evolution of the Market Profile

The ideas underpinning the Market Profile as well as the actual charting technique helped bring about my trading success.

Many are unaware that the Profile has undergone at least 3 stages:

  • Stage 1: Traditional Market Profile – fixed periods of 30-minutes that begin and end with the day’s ‘pit’ session.
  • Stage 2: The Steidlmayer Distribution (now also called Market Profile)
  • Stage 3: Cap Flow software

I stopped ‘following’ the Profile at the Cap Flow stage. Knowing Pete, I am sure there have been have been enhancements to Profile theory of which I am unaware.

In this blog, I’ll briefly talk about Stage 2.

Stage 2 was a major breakthrough  (at least so far as I am concerned).

Prior to Stage 2, Profile traders relied on Long Term Activity Charts (see Mind Over Markets) for the the longer term perspective. I found LTA lacking in flexibility and difficult to assess. The Steidlmayer Distribution (SD) is a much easier tool to understand and apply.

The key to the SD is to be aware that it begins and ends because of its own structure – not because of any fixed period. A structure begins with what I call an Initial Price Movement (IPM). This is a directional move that usually begins at the Point of Control of the previous structure and usually comprises of single prints. A structure ends when either a new IPM begins or the structure turns from a Bullish (or Bearish) structure to a neutral one (i.e. a normal bell curve).

Figure 1 shows the current combined E-mini that I started from Feb 1, ‘L’ period -that’s when I believe a new IPM began. The directional move during yesterday’s ‘L’ period. After that development (rotation) began. As long as we don’t see acceptance below 1091 (50% of IPM), a BULL profile will form. Once the development completes, I assume that we will IPM up.

Should we accept below 1091, then:

  1. We can expect the ES to go to the 3rd stdev of the former bull pattern: 1082 to 1081.
  2. If we do see the ES reach 1082 to 1081 then either:
  • a sideways congestion will form between 1101 to 1081 or
  • a new directional move will form.

To understand the logic of the moves, you need to know that one of the learnings from Market Profile is markets generally go from Bull to Sideways to Bear and vice-versa. You seldom see a Bull to Bear change in trend. This is an important observation that many traders fail to appreciate.

(Figure 2 shows the Bull, Bear and Sideways SD Patterns)


FIGURE 1 Combine Market Profile


FIGURE 2 Profile

Nuances in Trading

Flashback to the early 80s: I had spent over 5 years, over A$500,000.00 in losses, and some unknown amount spent on books, courses and systems BUT still success in my chosen field was a far off dream. At the time I was using traditional technical tools for my trading: moving averages, RSI etc. Clearly I needed to change.

‘When the student is ready, the teacher will appear’.

I read a small ad in Futures for a Market Profile seminar to be run by Peter J Steidlmayer in Chicago.  Though it meant stretching our finances, I went off to ‘yet another seminar’. But this one was the one that helped turn my trading around.

Peter said many important things; some I did not understand until much later e.g.

“Once the reason for a trade is gone, you exit the trade.”

I thought then, “what could he mean by that”?For Peter made it clear he was not speaking about price. Indeed he was scathing about traders who relied only on price.

“Traders make money inspite of (traditional technical tools) not because them”.

I came to realize that Peter was referring to structure.  Did I find the idea useful? Let me put it this way: this saying has saved me money more times that I can remember.

The latest occasion was the EURUSD trade where I exited for a 3 point gain rather sitting through an adverse price movement of about 140 points, and just 20-points away from being stopped out for a 160-point loss.

Let’s look at the trade.

Figure 1 shows where I added to shorts. The EURUSD satisfied the three change in trend filters I mention in Nature of Trends. And, it had retested the breakout price at 1.4556.  For two days after entry, the market formed a 5-period, 290-minute sideways structure. I was comfortable with the way the market was behaving.

On Friday Jan 8,  the Non-Farm figures were due out.  In write in my daily service (http://tradingsuccess.com/barrotwitter), I wrote that I expected:

  1. The figure to be greater than the expected range suggested.
  2. That the initial knee-jerk reaction would be a drop in the US$
  3. That I expected the US$ to rally after the knee-jerk drop.
  4. That I expected the US$ to close strong.

Well I was right on all but point (4). And, it was the afternoon rally against the US$ that convinced me that the EURUSD would rally on Monday. So I exited all my US$ longs (i.e. covered my short US$ crosses).

My reasoning was this: my assumption was that the US$ was strong. If this was correct, then in the current environment. the Non-Farm ought to have produced a US$ rally. The fact that after an initial sell-off, and that the lack of US$ buyers probably brought in US$ sellers, meant that on Monday Jan 11, the US$ was likely to weaken.

I also reasoned that if I was wrong and the US$ did rally on Monday, I would re-enter and created scenarios for this.

The trade is a good example of Pete’s dictum. I entered the trade because I was looking for US$ strength. Once the price action suggested that this would not happen, my reason for being in the trade was gone and it was time to exit.

This sort of experience does not come overnight and it does not come automatically.  I had to record and review many an incorrect interpretation before I started reading the tape on a relatively consistent basis.  On the other hand, I can say that  the hard work does pay off.



FX Time of Day Trading

BarroMetrics Views: FX Time of Day Trading

I was surfing the net and came across an interesting free webinar-video by Barry Craig.  You can find the webinar here:


I’d agree with most of what he has to say, with one exception:

“that all pairs normally have 30 to 60 pips in the London and New York sessions, and all pairs would normally have 70 to 120 pips in the same sessions on news affected days.”

I disagree because different pairs have different volatilities during the same period, and the same pairs have different volatilities during different periods.  I’d suggest that you need to allow for this observation when applying the strategy.

You may find this resource useful for making the adjustment.

Some time ago, Quantum Research Management Group offered a ‘cheat sheet’ showing different ranges for different pairs at different times on different days. I have attached it here.  One caveat on its use.

The research was done in 2007 . You need to assess if the volatility today is the same. One way of doing this is to the compare the ATR of like periods in 2007 with the current data set.

For example, in Figure 1, you have the GBPUSD. I compare Dec 2007 with Dec 2009. The ATR was 170 compared to 185 today – that’s about a 9% increase in volatility. I would adjust the Quantum numbers accordingly.

Let me know if you like this blog.




Open-Gap Rule and The Death Zone

BarroMetrics Views: Open-Gap Rule and The Death Zone

I received a barrage of mail asking that I explain the two topics. Before I get into that, a comment: for some reason, I seem to invite e-mails rather than comments. Folks I would be grateful for comments, questions or requests at the blog rather than e-mails. Thanks for helping out.

Open-Gap Rule

I use the open-gap rule for the ES (S&P e-mini futures) and 30-Year Bonds. It works for Gold if you use the combined day and night sessions.

The ‘rule’ says that if the instrument has an open-gap by at least1/2 the standard deviation of a 10-day Average True Range then:

  1. If  greater than 50% of the open-gap fills in the first 60-minutes of trading, then we expect the whole of the open-gap to fill.
  2. If the gap has not filled within the first 60-min but is trading in extreme quadrant in the direction of the 50% of the open-gap,  allow another 30 minutes for 50% of the open-gap to fill.
  3. For example: in the ES there is an open-gap down and ES has not filled 50% of the gap in the 1st 60 minutes. However when the 60-min bar closes, the ES is trading in the bottom quadrant, I will extend the time to fill the open-gap by another 30 minutes.
  4. By ‘fill’, I mean ‘accept’. This is an important distinction. If the market moves beyond the 50% and forms a rejection extreme on a 15- minute bar, this is not ‘acceptance’.  The easiest form of acceptance is to have 2-consecutive closes (15-min bar) below the 50%.
  5. All things being equal, if 50% of the open-gap does not fill in the first 60-minutes, we can expect a trend day in the direction of the gap.

 Death Zone

In a congestion market, the death zone is the area that stops the market from reaching its Primary Zone. This is a clue that the congestion market may be coming to an end.  Let me use Figure 1 to illustrate. Let’s assume that the market is coming from D.

  1. If the market is coming from the Primary Buy Zone, the Death Zone is the 66.67% (Value Area High) to 50% retracement area. Reverse if coming from the Primary Sell Zone (see Figure 1).
  2. Confirmation that the Death Zone is in play takes place when the market accepts below 33% (Value Area Low).
  3. Once (2) occurs expect the ‘B’ will give way.
  4. The Death Zone is less robust when you have a Negative Development breach at D i.e. if at D we see B breached but D remains above the Maximum Extension.

I use the Death Zone when I expect a wave-5 Failure i.e. in an uptrend when I expect wave-5 to fail to make a new high (see yesterday’s blog on the Ray Wave). I also use it as a warning benchmark – to caution that the sideways condition has ended.

FIGURE 1 Death Zone

The Ray Wave

BarroMetrics Views: The Ray Wave

From time to time I get a request to write about the Ray Wave. For the most part, I have been reluctant to teach this subject: I withdrew both the video and the book from sale.  In my view it is a powerful tool for stock trading, forex trading and futures trading; but its strength is also its weakness. It is flexible enough within strict guidelines to deal with the chaotic nature of the markets. But unless you stick to the guidelines you can ‘make’ your trading charts say anything you want it to.

In this blog, I outline the first guideline, the need for price symmetry between wave-2 and wave-4 in a 5-wave structure.

I like the Elliott Wave for its flexibility. The ‘problem’ is there are few restrictions imposed for the classification of waves. You often see, for example, a very small wave-2 and a humongous wave-4. In this case, there is little to say that the waves belong to the same category. In fact, visually, they do not.

I was convinced that the Elliott was useful but you had to apply its ideas in similar magnitude structures. The question was how to define ‘similar magnitude’.

I borrowed an idea from Michael Gur: wave-4 and wave-2 had to relate by 20% in price. I went on to test various other percentages and found that 20% was as robust as percentages from 17% to 27%. So, I stuck with the 20%. To Gur’s idea, I slowly added my experience and ideas from Elliott, Neely and Wyckoff  to create the Ray Wave. Later I added some of Jim Kane’s ratios.

To show you what I mean about guidelines, let’s take the current GBPUSD. Figure 1 shows the count. The clear symbols are a higher category than the filled-in ones.

Figure 1 shows that I believe that we shall see a 5-wave failure in the GBPUSD. We see also that:

  1. Wave-2 and Wave-4 are within 20% of one another.
  2. We have termination price targets for both wave-3 and wave-5
  3. We have a 2-4 trend line. The market must accept price levels below the trendline in no more than the amount of time it took wave-5 to form. Since wave-5 took 24 trading days to form, for my count to be correct, the breakdown  needs to occur in 24 days. In other words we need to see the 2-4 trendline broken within the next 8 trading days. If this does not occur, the count is incorrect.

Points (1) and (3) are what I mean by objective guidelines. The Ray Wave is replete with checks and balances to ensure we are placing market information within an ‘objective’ structure rather  one that is a function of our imagination. If used in this way, the Ray Wave is a powerful tool; if we just randomly label a structure – well, GIGO (garbage in, garbage out). But I love the Ray Wave. When used with the Barros Swing, the Ray Wave provides a reliable roadmap to future price action – it gives me benchmarks by which to determine which of the scenarios I have imagined is the one most likely to occur.



Swing Strength Comparison

BarroMetrics Views: Swing Strength Comparison

I received a few e-mails asking why I keep saying that the 13-week swing direction is statistically overbought.

I have two reasons:

  1. The LOG percentage changes and
  2. Swing Strength

In Figure 1, I have shown the LOG percentage change of the larger 13-week impulse swings. Notice that even among the sample of 10, the current move is 5th. If you include all the swings since 1952 (inception of the S&P), you’ll appreciate that the current swing lies in the 3rd standard deviation. In addition, we are not comparing apples with apples.

I have used swings in a strong trending market rather than swings in a sideways market (where impulse moves are smaller).  Had I used the DJIA and compared the 1966 to 1982 and 1937 to 1942 with the current swing, you’ll find that the current swing is among the top 5% using log percentage change.

The second reason is the swing strength formula I use to measure the strength between swings. In Figure 2, I compare the 2002 rally to the current one.  The current swing is the 2nd strongest in this structure and 2.5 the strength of the rally from the 766 low to the 1560 high.

So the above are the reasons why I view the 13-week rally off the March low as being overbought. This does mean I would be seeking to short it. It does mean I would not go long without a 13-week correction unless there are compelling reasons to go long.

(The swing strength formula:The difference in the prices squared divided by the difference in the number of Bars)





Barros Swings and Re-labelling

 BarroMetrics Views: Barros Swings and Re-labelling

Baz wrote:

Hi Ray,Would your A,B,C, (fig1)labels change/renamed, because the %20 extension was exceeded? I actually took this trade and what stood out to me,initially, was the doji but i had also in my mindseye relabeled it and figured it was in the buy zone if my relabeling was correct.But it was seeing the doji that caused me to investgate further and try blending,”NOT”,to make a hybrid. cheers baz

The info is in the Nature of Trends (you read it, right? [G]). So, here I’ll do a quick summary why I would not do what you did for the reasons you stated.

To understand why, you need to understand the functions of the Boundaries of Congestion (see Figure 1). They:

  1. Provide the all important Primary Buy and Sell Zones.
  2. Provide the Maximum Extension Zones that act as a price filter for breakouts and the Spring (Upthrust) Change in Trend  Patterns.
  3. Provide the zones I call the Top and Bottom of Value
  4. Provide the ‘Death Zone’.

In this blog, it’s point (2) that I want to address. Once the market accepts beyond the Maximum Extension, I deem that it has passed the price filter test for a valid breakout. If I keep extending the boundaries of congestion on a breakout, then I shall be forever be in a contra-trend zone.

For example in Figure 2, we have the breakout of the S&P.  Right now we have (basis cash):

  1. The Maximum Extension at 1087.6
  2. The Boundaries of Congestion at 1075.5 to 1015.

If we extend the Boundaries of Congestion to 1092.75 to 1015, then we are automatically in a sell zone. What’s wrong with that? Well in a strongly trending market, we’ll keep looking for contra-trend trades. For example in Figure 3, we have the BPJY breaking a small congestion zone AB (197.44 to 186.48). If we extended the Boundary to 174.10, the low of the breakdown, then we are automatically in the buy zone and perhaps looking to buy?

The net  effect of the strategy would be to constantly looking for buy signals in a downtrend. That’s why I don’t extend the boundaries once a breakout occurs.







The Pivot Point Formula

BarroMetrics Views: The Pivot Point Formula

The Pivot Point Formula arose in the days when Pit Trading was in full swing and computers were still to be invented. The locals used it to provide some idea of resistance and support for the next day’s trading.

Many think there is only formula, the Classic (using the range of the pit session)

PP = (HIGH + LOW + CLOSE) / 3
S1 = (2 * PP) – HIGH
S3 = S2 – RANGE
R1 = (2 * PP) – LOW
R3 = R2 + RANGE

But actually, there are quite a few:

  1. The Standard 24hr (same as above but using Globex and pit session i.e. 24hr session)
  2. Camarilla (http://www.mypivots.com/investopedia/42/camarilla-pivot-points)
  3. Woodie (http://www.mataf.net/en/tools/pivot-points-woodie)
  4. Fibonacci (http://www.mataf.net/en/tools/pivot-points-fibonacci)
  5. DeMark (http://www.mypivots.com/investopedia/57/demark-pivot-points)

All the formulas use the previous day’s bar in their calculations. Except for the DeMark formula, they all use the same calculation, ignoring the location of the day’s open to its close. DeMark’s formula is dependent on whether the close is above, below or equal to the previous day’s.

I tried the Classic formula (pit session only) when I first started trading, abandoned it and returned to test it about 5/6 years ago. On the second occasion I backtested the classic (pit and 24 hour session) and found that the results produced were no better than random.

If you think about it, the problem lies with using the previous day’s range.  The assumption behind the pivot formula is this: today’s range will be about the same as yesterday’s.

But this is not always the case. In fact,  in the appropriate context, a small range today may mean a larger than normal range tomorrow; and a large range today may mean a smaller than normal range tomorrow.

Armed with this idea, I had the same pivot formula tested using the Average True Range for the pit session. But I made one change: this time I assessed whether the day’s Average True Range was to be normal, above normal or below normal. I then used mean for normal, mean +1 stdev for above normal, and mean -1stdev for below normal and I used this figure to assess the ‘PP” value in the formula.

The results obtained were superior to using yesterday’s range. This means of course the trader has to have some skills at assessing the likely strength day’s range.

In the way I use it, the formula’s of R1 and S1 tend to identify the high and low for the day. R2 and S2 tend to identify stronger or weaker than anticipated day and suggest  I substitute the appropriate value (e.g. mean +1 rather than mean  for the PP’s calculation). A market’s acceptance below R2 or above S2, (where PP is normal), suggests a trend day (i.e. a strong directional day).

While I would not use this idea as a stand-alone  tool, it does play a part in helping me identify support and resistance for the various timeframes.

By the way, there is a site (free) that calculates in the traditional way most of the different types of Pivot areas and combines them with moving averages, Market Profile areas, and Fibo zones and gaps to produce a cluster of support and resistance zones. You can find it at: www.pivotfarm.com