Section 4

Auction Market Value Analysis

It is a given of this theory that one effect of an auction market's complexity is that market knowledge must come from empirical sources, observation of the data, rather than from exploration of the mathematics of a distribution function or prediction of market variables such as interest rates. Value is a variable that can be divined from market activity; from volume if audited data is available and from tick data for markets for which ticks are available. Value, read from the market activity is the second level information derived from the primary data (time, volume, ticks, etc.).

Most of the world is complex. To arrive at useable solutions, it is required to collect observations, analyze the data, suggest an explanation, collect more data, refine the explanation and so forth until there is enough information at hand to propose a theory. This is the 'I think I understand it' point, be it in auction market value analysis or fashion merchandising for a department store. It comes from experience, an understanding of the field and reduction of the data. The thrust of this work, then, is to use market data to reach a level of understanding; re-evaluate the market data to get to the next level, and so on, ultimately reaching the theory proposed here.

The Path of Analysis
In Auction Market Value Theory the path leads from Market Profile value (4, 8), to the less restrictive Meta-Profile value (12, 13, 14) and then to the Overlay Demand Curve (16, 18, 20) to find longer time frame value and market condition. The key is value. Whenever value can be identified, a large amount of information about the market becomes available.

The basis for auction market value analysis lies in three major discoveries. These three take us from a bewildering set of data generated by a complex market every minute to a rational and reasonable explanation of how the market behaves.

The first discovery, Market Profile, found that the analytical link data is a daily trading audit data base released by the Chicago Board of Trade. This data base, The Liquidity Data Bank (LDB) (4, part 6), carries each future traded at CBOT on an end-of-day report, listing each price, the volume and times traded.

Meta-Profile identified the analytical link between market activity (ticks) and value (12, 13, 14, 15). Source data for Meta-Profile is a record of trades or ticks, either real time, end-of-day or historical. In addition to value, an activity graphic similar to that found for the Market Profile can be constructed from the tick data, although the Meta-Profile graphic is used for somewhat different purposes. Meta-Profile opens value analysis to all tick producing markets, futures, forex, options, equities, etc. Time frames for analysis can be adjusted for day markets, for night markets or a combination as desired.

Thirdly, the Overlay Demand Curve extends Meta-Profile value analysis to multi-day timeframes (16, 20). Longer timeframe value is found from frequency of ticks over the extended timeframe, just as with the daily Meta-Profile. Overlays are notable for identifying both longer term value and market condition. Market condition identifies those times of balance, the periods of directionality (trend) and the transitions in between.

These three discoveries are discussed in detail in Appendix 1. They provide the ability to decode the high density market feedback, resulting in location of value, of market condition and thus, the ability to track markets through their four phases: balance, transition from balance to directionality, directionality and the transition from directionality to balance. This information has opened a door to further research and a deeper understanding of market structure and behavior. Some of this work is reported in articles and books and links to internet published literature. Some are listed in the References Section of this report and the References page of the CISCO website (www.cisco-futures.com).

Basic Principles of Auction Market Value Theory

Applying the three major discoveries to auction market data leads to the elements of auction markets, collected in Table 1. Data in the table, while generally applicable to all auction markets, are from futures markets (indexes, interest rates, forex, etc.).

The information in Table 1 is collected from experiments, data collection, cataloging, disappointments and retries. Much of the continuing work is reported on the References page on the CISCO website. A portion of this work has been published in trade journals, other work is on the website, reachable via links.

Axioms, Definitions, Observations and General Market Knowledge
1) No general market distribution function is likely to be found
2) Markets are self organizing and driven by feedback
3) Market response time to a pulse is non-zero (feedback takes time)
4) Demand is a complex function of physical and psychological factors
5) Value is a function of price over time
6) The central 70% of activity measures value (in stable markets)
7) Markets display a run, pause, run, pause price pattern
8) A trend is the 'run' portion of a run, pause price pattern
9) A balance is the 'pause' portion of a run, pause price pattern
10) Trends and balances are defined within the context of particular time frames

Table 1. Axioms, Observations and General Market Knowledge
The ten elements that are the foundation for auction market value theory.

Discussion of the Elements of Table 1
Item 1. Distribution Functions
The normal give and take of auction markets coupled with the sometimes extreme behavior at the tails of the market (huge fast losses, as in the October 14 - 19 1987 period), maps out a distribution that cannot be fit by even an exponential curve. The problem is one of risk. Much of the time most markets are trading in a well-behaved, orderly region that possibly could be modeled by a gaussian distribution. But, assuming a market distribution can be misleading. A case to point is the Black-Scholes Option Valuation procedure. Their derivation relied on the probability density of a gaussian distribution. Subsequently, a hedge fund, Long Term Capital Markets, employed that methology with, it is assumed, some of their own wrinkles. Nevertheless, the methodology resulted in the largest financial failure ever known ($1.3 trillion), as reported by Lowenstein (5).

Long Term Capital Markets initially was quite profitable. The cause of the failure was market forces that hit many trading groups the same way: "There were two reasons for the lack of diversity of opinion in the market. The first is that virtually all of the sophisticated models being run by the leveraged players said the same thing......" (6). The "sophisticated models" are no doubt the Black - Scholes methodology modified at least for volatility and probably other parameters as well.

Item 2. Markets are self organizing and driven by feedback
The recent thrust in economic thinking has been guided by the behavioral economists. The shift is from general equilibrium to multiple equilibriums and out of equilibrium, i.e. a self-organizing system. The econphysicists arrive at the same description mathematically, emphasizing the feedback.

Item 3. Market response time to a pulse is non-zero (feedback takes time)
This point is an observable, contrary to the efficient market hypothesis. As Steidlmayer pointed out, the market is not efficient, it is effective (7, p14). Much earlier, Jones identified a situation where the market acted in a predictable manner, wholly out of character for an efficient, stochastic distribution (27).

Logically, the concept of instant feedback (efficient market) defies the laws of nature. It takes time for a trader to accept a price change, evaluate it, decide on a course of action and respond. Steidlmayer explicitly selected a 30 minute feedback time, which research has supported to some degree (24).

Item 4. Demand is a complex function of real and psychological factors
This is an assumption, supported by observation of 'herd behavior'. For an example, see 'Day Trading the London Blast, July 7, 8 2005' (25). It is in the 'References' list on the CISCO homepage. As of the July 6, 2005 close, the Dow Jones Index value range was 10402 - 10323 (CBOT mini-sized Dow). On July 7, after the blast the market opened at 10152 and shortly dropped to 10142. As the situation became more clear, the panic selling began to dissipate, with the day closing at 10333. Note, closing price is back inside the previous day (July 6) value. The next day, July 8, the open was at 10364, again inside the July 6 value. Price moved steadily higher, closing at 10477. By the close, the market was decidedly directional up. The movement:
1. July 6 close Value range is 10482 - 10323
2. London subway blast occurs prior to open on July 7.
3. July 7 Open at 10152
4. July 7 Close at 10333
5. July 8 Open at 10364
6. July 8 Close at 10477
The uncertainty imposed by the blast drove market participants to cover (dumping their longs). The herd effect, so obvious in large market drops, e.g. the October 14 -19 1987 panic, was at work in the Dow Index market of July 7. As the news improved, so did the market. The spectacular drop was immediately followed by a spectacular rise.

Item 5. Value is a function of price over time
Value of the type defined by market activity is based upon the concept of price-over-time, the relative frequency with which the market revisits a traded price. Steidlmayer based that definition on cleared volume (4). Later work used market activity (12), and added the caveat that the measured market had to be in balance.

As defined by Jones (10, P2), "value is the most frequently occurring price (region) within a period of price stability". This definition requires a market in balance, plus it implies sampling. The normal period is one market day, typically six or seven trading hours, although one could use any arbitrary period so long as consistency is maintained.

Item 6. The central 70% of market activity measures value (value area)
Steidlmayer defined the value area as +/- the first standard deviation of a market profile which is itself defined as a gaussian curve (4); hence the 70 percent number (actually a little less). In this work we do not posit that a market profile forms a gaussian distribution. We do observe a clumping of market activity in the middle of a day's price range. We accept that the central 70% of market activity defines value. Doubtless, this working definition might be improved by more detailed research. That is left for the graduate student. Value can only be defined in a balanced market (see Item 9, below). A directional market (trend, see Item 8) has changing value which is difficult, if not impossible to measure in the general case.

Item 7. Markets display a run, pause, run, pause price pattern
Run, pause price patterns are observed. This will be demonstrated in the section on profiles. The run, pause pattern is most likely a look into the decision process of the collective trader, with it's uncertainties on display.

Item 8. A trend is the 'run' portion of a run, pause price pattern
Markets spend their time in either one of two states (trend (run) or balance) or in transition between the two states. Runs, pauses and balances will be demonstrated in the profiles section. A run may include one or more pauses.

Item 9. A balance is related to the 'pause' portion of a run, pause price pattern
This is the same concept as for Item 8. The pause itself demonstrates an uncertainty within the collective. A run terminates when a pause lengthens into a congestion. A congestion is recognized within the constraints of the current market--it is a balance period substantially longer than the period of the average pause (see Item 10).

Item 10. Trends and balances are defined within the context of particular time frames
Markets display a limited fractal nature. Self similarity is seen intra-day and day to multiple days. In the futures data we use, the self similarity typically disappears after about 15 days. Intra-day, the fractal resemblance may go down to 10 or so minutes. This behavior is most likely market specific so far as the timing is concerned. It is not unusual for a market to be trending on a 3 day basis while balancing on a longer term. There are at least two scenarios: A market may be in balance on several time scales, say 20, 14, 8 and 5 days. A trend may begin in the shorter term period and continue to move through longer and longer time balances, until all four timeframes are directional. Or, secondly, the short timeframe balance is broken, the market is directional on the short timeframe only, the trend stops and balance is restored to the short timeframe, while the longer timeframes remain in balance through the entire process.

Limitations
As in all empirical systems, there must be adequate observations to offer an valid measure. Typically, something like 100 ticks are needed for each 30 minute period to have adequate liquidity; about 1000 ticks per day is a reasonable minimum. Less activity may still provide adequate liquidity, requiring a judgement call on the part of the analyst.

Discussion

Table 1. is not necessarily the final word on the basics of auction markets. The scientific, observational/empirical approach recognizes that there is usually more to be learned and future work can advance the state of knowledge. Auction Market Value Theory is what it says, a theory. As in any valid scientific theory, it is "falsifiable", meaning it's elements can be tested and may be disproved or modified. As with any scientific study, Table 1. is offered as "where we are now". The nature of science is to constantly question findings and assumptions, revising and deepening the understanding of the phenomena. Particularly, unravelling market behavior and structure in the presence of human psychology holds many challenges and possibilities.

Unlike other ways of analyzing markets, the empirical method may appear somewhat lacking. Some methods try to 'know' things about the market based on supra-market information; items like value, direction, reasons for a change in direction and even predictors like, e.g., Elliott waves (11). Unfortunately, the predictors tend to be of the sort "the world will end tomorrow". Then, when tomorrow comes and there is no end, that necessitates a recalcualtion. A case of this sort may have occurred in Elliott wave in 1987. The huge October 1987 crash was seen as the end of a stock market cycle begun in 1932; the peak of wave 5. When the market quickly had a strong recovery (no, the world did not end), waves 1 through 5 had to be re-evaluated (26). The low on Oct. 20 (SP = 181) has been followed by a generally rising market (by August 2005 the SP Index was above 1200).

Auction theory has the modest goal of understanding a market on the basis of it's behavior. Since auction theory does not predict future behavior, it is easy to question the theory's utility. One answer lies in the Practices of Section 3. With a proper theory, one may discern which practices are permitted by the theory and which are not. One is offered the correct path, avoiding many meaningless or incorrect, costly detours in market analysis.

As discussed earlier, 'Know your business' is an appropriate dictum in any business, and certainly so for auction markets. For example, current value offers a marker for value change. Knowing that current value is changing offers information on directionality and what to look for, i.e., the onset of congestion. Compared to predicting the market several years out, attempting to understand current value may seem mundane. Getting a better price for one's product by understanding value is the very spirit of markets and hardly mundane. An old Russian proverb is apropos: There are two fools in every market: one pays too much, the other sells too cheaply. The one who knows value gets a fair price (or better!) and is not the fool. To a fund manager who must rebalance a portfolio, it would be attractive to sell the old equities near their highest price of the recent few days and buy the new equities near their lowest price of the same period.

A difficulty not present in the physical sciences is that auction market data has a strong psychological component. Price varies with the trader's perceptions. Those perceptions are reflected in the market feedback; volume, the tick flow or other short timeframe measures. Decoding the market activity reads the collective trader's mind. That mind may change, often not depending on (real) value but on fear and greed. A trader who can understand the market's feedback is in a far better position than one who cannot (for example, see ref 25).

Another characteristic of auction markets is a time delay in a market's response to a pulse of activity. Feedback from the widely scattered agents/traders takes time: each trader acts within his/her own interpretation of the market action and within his own timeframe. In a market driven by feedback the time to respond is variable depending on the impetus, but there is a norm, an average. A relaxation time, on average, can be found. For markets studied to date the average relaxation time is of the order of 30 minutes, but that obviously can change with market phase and local conditions.

Much of market econonomics has developed from defining general truths or 'authority' and then continuing to build from there (1). It is hard to argue with a general truth, as the behavioral economists have found. Starting from scratch with the data, and developing a knowledge base piece by piece has the advantage of being testable. This, and not relying on general truths, is the scientific approach.

Auction Market Value Theory (AMVT) is a relatively new concept. It's true utility as an economic theory is yet to be gauged. What can be said so far is that the methodology is consistent and opens new avenues for analyzing markets. It is helpful to practitioners by uncovering market structure. Appendix 2 gives a sense of the wide range of subjects AMVT can treat: the market psychology of a panic, how the market creates "units" of balance - directional - balance, etc., an examination of waves, market response times, efficient versus effective market activity and many others. A wider and continually growing list can be found on the CISCO website (www.cisco-futures.com, link to References).

References

1. Portfolio Theory & Capital Markets, W. Sharpe, McGraw Hill, 1970

2. Why Stock Markets Crash, D. Sornette, Princeton, 2005

3. Financial Market Complexity, N. Johnson, P. Jeffries, P. Hui, Oxford, 2003

4. CBOT Market Profile (J.P. Steidlmayer) CBOT internal pub. 1985, 1991

5. When Genius Failed, R. Lowenstein, Random House, 2000

6. www.erisk.com/Learning/CaseStudies/ref_case_ltcm.asp

7. Markets and Market Logic, Steidlmayer & Koy, Porcupine Press, 1986

8. Steidlmayer on Markets, Wiley 1989

9. Steidlmayer on Markets, 2nd Ed. Steidlmayer & Hawkins, Wiley 2003

10. Value Based Power Trading, Jones, Probus, 1993

         Available from www.cisco-futures.com
          Value Based Power Trading

11. Elliott Wave Principle, R. Prechter, A.J. Frost, NCL 1978

12. Determining the TPO Value Area, Don Jones, Market Logic School Alumni Letter, April 13, 1987, P4

13. Estimating the Market Profile Value Area for Intraday Trading, D.L. Jones, S&C Sep. 1987

14. Day Trading With Market Value, D.L. Jones, S&C May 2005 P16.
          Related discussion at:

15. Value in Trends from Meta and Market Profiles

16. Overlay Detection of Long Term Market Condition, D.L. Jones,
          The Profile Report, Vol 2, Oct. 1988

17. Intraday Trading with Tick Based Profiles, March 1990
          Intra-day Trading with Tick Based Profiles

18. Overlay Detection of Long Term Market Condition, D.L. Jones,
          The Profile Report, Vol 2, Oct. 1988

19. The Overlay Profile for Current Market Analysis, D.L. Jones & C.J. Young, S&C June 1990/July 1990

20. Overlay Demand Curves, the Missing Link, D.L. Jones, Market Profile Soc. Intl. March 1992
          Overlay Demand Curve Background

21. Value Based Power Trading with the Overlay Demand Curve, D.L. Jones, Market Profile Soc. Intl. Sep. 1992

22. Overlay Demand Curves (tm), D.L. Jones, December 19, 2004
          Overlay Demand Curve Background

23. Mind Over Markets, J.F. Dalton, E. Jones, R. Dalton, Probus, 1991
          Available from Amazon.com

24. Unpublished research at CISCO Futures

25. Meta-Profile/Overlay Trading the London Blast

26. http://pages.stern.nyu.edu/~adamodar/New_Home_Page/articles/elliottwave.html).

27. Persistence of Trends, Commodities Magazine, Feb. 1973

28. Complexity, Risk and Financial Markets, E. Peters, Wiley, 1999

Appendix 1. Discoveries

          COMMODITY  --  DJIA (CBOT)  DAY      JUN 04  April 1, 2004


             Price  Brackets 
           103950 D          | Upper Tail
           103900 DE          |
           103850 DE          | Upper Range 
           103800 DE          | Extension
           103750 DEFK        |
           103700 DFJKLP      |
           103650 CGJKLP       |  
           103600 CGHJKLMP     |
           103550 zCHIKLMNP    |                |
           103500 zCHILMNP     | Value Area     | Initial
           103450 zACHIMN      | 70% of Trade   | Balance (first two periods)
           103400 zABCHMN      |                |
           103350 yzABCMN      |                |
           103300 ABCN         |
           103250 BC          | Lower Range
           103200 BC          | Extension

    Some prices are omitted to condense the display.

The Tick-TPO or Meta-Profile, D2

Requiring audited (cleared) data for Market Profile methodology excluded most of the auction market world. This problem was solved in 1987 when the Tick-TPO Profile was announced (12, 13). Tick-TPO Profile, later called Meta-Profile, is a methodology that generates the profile graphic and value area from tick data, either within a day or at end-of-day.

The Meta-Profile Market Graphic
Meta-Profiles generated by ticks create both a profile graphic and a value area. Profile graphics from the LDB audit data and from ticks are usually quite similar and often give virtually identical profile charts. Value area, too, is quite close between the two methods, except in directional markets (15). Unlike the Market Profile, Meta-Profile construction and analysis does not rely in any way on the tick data having a gaussian distribution. Other features of Market Profile analysis, such as day types and reliance on the gaussian distribution of the profile graphic are not used in Meta-Profile based analyses. Meta-Profile value comes from TPO counts, with no reference to the shape of the Market Profile type graphic. TPO counts play the same role for Meta-Profile value that volume does for the Market Profile value area.

A Tick - TPO Profile With Half-Hour Timeframes
A Meta-Profile representation of the data for the Dow Index of figure 1 is in figure 3. Price bar is on the left, then the TPO count (#), next are the Profile TPOs, followed by all the half-hour vertical bars, stated in TPOs. Value at any point in time (indicated by the TPO half-hour bar columns dashed lines) is the middle seventy percent of the TPO counts up to that time. The vertical bars on the TPO columns, the value area to that point, change as value changes throughout the day.

For instance, at the end of 'A' period there is one TPO at 105700, two at 105650, two at 105600, three at 105550, three at 105500, two at 105450 and one at 105400. The center is at 105550, indicated by the arrow '>', and the value area is 105600 to 105450 (the vertical bars).

At end of day, the value area lies between 106100 and 105530. The center of trading for the day is at 105750, with 9 TPOs (BDEGHIJKL). By comparison, the Liquidity Data Bank of figure 1 reports a value area of 105980 to 105330, with the peak volume of 2076 at 105550. There is good agreement between the two, considering that two different data sets are used and there is rounding from the large price compression.

                 Meta-PROFILE* REPORT FOR 11 28 00
             COMMODITY  --  DJIA (CBOT)  DAY      DEC 00


  Price   #  Brackets               Segmented Auction
 106550   1  C                             C
 106500   1  C                             C
 106450   1  C                             C
 106400   1  C                             C
 106350   1  C                             C
 106300   1  C                             C
 106250   1  C                             C
 106200   1  C                             C
 106150   5  BCDJK                      B  C  D                 J  K
 106100   8  BCDEHIJK                   B  C  D  E        H  I |J |K |  |  |  |
 106050   9  BCDEHIJKL                  B  C  D  E        H  I |J |K |L |  |  |
 106000   9  BCDEHIJKL                  B  C |D  E        H  I |J |K |L |  |  |
 105950   9  BCDEHIJKL                  B |C |D |E |     |H |I |J |K |L |  |  |
 105900   8  BDEHIJKL                   B |  |D |E |  |  |H |I |J |K |L |  |  |
 105850   9  BDEGHIJKL                  B |  |D |E |  |G |H |I |J |K |L |  |  |
 105800   9  BDEGHIJKL                  B |  |D |E |  |G |H |I |J |K |L |  |  |
 105750  10  BDEGHIJKLM                |B |  |D |E |  |G |H |I |J |K |L >M >  >
 105700   8  ABEGHKLM                A |B |  |  |E |  |G |H |  |  |K |L |M |  |
 105650   9  yABEFGHLM         y     A |B |  |  |E |F |G >H >  |  |  |L |M |  |
 105600   8  yABEFGHM         |y    |A |B |  |  |E |F |G |H |  |     |  |M |  |
 105550  10  yzABEFGMNP       >y |z >A >B >  >  >E >F >G |  |            M |N |P
 105500  10  yzABEFGMNP       |y |z |A |B |  |  |E |F |G |  |            M  N  P
 105450   8  yzBEFMNP          y |z |  |B |  |  |E |F |  |  |            M  N  P
 105400   7  zBEFMNP              z    |B |  |  |E |F |  |               M  N  P
 105350   6  BEFMNP                    |B |      E |F |                  M  N  P
 105300   4  BMNP                       B |                              M  N  P
 105250   2  NP                                                             N  P
 105200   2  NP                                                             N  P
 105150   1  P                                                                 P

TPO Analysis
CENTER      105750

VALUE AREA FROM TPOS
 UPPER      106100
 LOWER      105530
 
Figure 3. Meta-Profile for DJ Index, November 28, 2000
Source data is ticks.


Meta-Profile is copyright of CISCO, 1987, 1990
Some prices are omitted to condense the display.

                   The CISCO five day Meta-Profile* display

     DJIA (CBOT)  DAY       DEC 00   First date: 10 31  0   Last date: 11  6  0

             31              1              2              3              6
     110800                                                         N             
     110700                                                         LN            
     110600                                                         HKLN          
     110500 LM                                                      FHIKLN        
     110400 KLM           ABC                                       EFGHIJKLN     
     110300 KLMN          ABC                                       EFGHIJKLMN    
     110200 IJKLMN        ABC                                       DEGIJKLMN     
     110100 IJKLN         ABCD          zB                          CDEJMN        
     110000 IJK           ABCD          yzB                         CDEM          
     109900 HIJ           yzABCDEFN     yzABGK                      CEM           
     109800 HI            yzACDEFGHN    yABCFGHK      y             C             
     109700 H             yDEFGHKMN     ABCDFGHJKLM   y             C             
     109600 GH            DEFGHIKMN     ACDEFGHIJKLMN yz            BC            
     109500 AG            GHIJKM        ACDEFHIJKLMN  yz            BC            
     109400 AEFG          IJKLM         DEFILN        yzA           BC            
     109300 yzABEFG       IJLM          LN            yA            B             
     109200 yzABEFG       JLM           N             ACD           B             
     109100 yABCDE        L                           ABCD          AB            
     109000 yABCDE        L                           ABCDMN        yzAB          
     108900 BCD                                       ABDEMN        yzAB          
     108800 BCD                                       ABDEMN        AB            
     108700 BC                                        ABEFIMN       A             
     108600 B                                         EFILM                       
     108500                                           EGHIJKL                     
     108400                                           EGHIJKL                     
     108300                                           GHJKL                       
     108200                                           GK                          


Figure 4. Five days of the DJ Index in a bounded, balanced market.
Five day Range 110800 to 108200 is $2,600.

*Meta-Profile is copyright CISCO 1987, 1990
Some prices are omitted to condense the display.

                   The CISCO five day Meta-Profile* display

     DJIA (CBOT)  DAY       DEC 00   First date: 11  7  0   Last date: 11 13  0

              7              8              9             10             13
     110700               yz                                                      
     110600               yzAE                                                    
     110500 yzABFG        zAEF                                                    
     110400 yzABFGHIJN    zABEFGHI                                                
     110300 yABEFGHIJKN   zABEGHIJK                                               
     110200 ABDEFGHIJKN   BCDEGHIJKL                                              
     110100 BCDEFJKLMN    BCDEIJKLMN                                              
     110000 BCDEKLM       BCDIJKLMN                                               
     109900 CDLM          BCIKMN                                                  
     109800 C             BCIMN                                                   
     109700               BMN                                                     
     109600               BN                                                      
     109500               N                                                       
     109400               N             yC                                        
     109300               N             yCM                                       
     109200               N             yCDMN                                     
     109100                             yCDEFMN                                   
     109000                             yBCDEFLMN                                 
     108900                             yzBCDEFLMN                                
     108800                             yzABCDEFGKLMN                             
     108700                             yzABCGKLMN                                
     108600                             ABCGKLMN                                  
     108500                             ABCGHKLMN                                 
     108400                             AGHKLMN                                   
     108300                             AGHKLM                                    
     108200                             AHIKL                                     
     108100                             AHIK          ACD                         
     108000                             AIK           yzACD                       
     107900                             AIK           yzABCD                      
     107800                             IJK           yzABCDE                     
     107700                             IJK           yzABCDE                     
     107600                             IJK           yABCDEG                     
     107500                             IJK           ABCEFGHI                    
     107400                             IJ            BCEFGHIJ                    
     107300                             IJ            BCEFGHIJLM                  
     107200                             IJ            BFGHIJKLM                   
     107100                             IJ            GHJKLM                      
     107000                             IJ            GJKLMN                      
     106900                             IJ            JKLMN                       
     106800                             IJ            JKN                         
     106700                             IJ            JKN                         
     106600                             J             KN                          
     106500                             J             N                           
     106400                                           N             LM            
     106300                                                         LM            
     106200                                                         LM            
     106100                                                         BLM           
     106000                                                         BKLMN         
     105900                                                         BKLMN         
     105800                                                         BCKLMN        
     105700                                                         BCKLMN        
     105600                                                         ABCKLN        
     105500                                                         ABCJKN        
     105400                                                         yzABCIJK      
     105300                                                         yzABCDIJK     
     105200                                                         yzABCDIJK     
     105100                                                         zABDIJK       
     105000                                                         zABDEIJ       
     104900                                                         ADEHIJ        
     104800                                                         DEHI          
     104700                                                         DEFGHI        
     104600                                                         EFGH          
     104500                                                         EFGH          
     104400                                                         EFGH          
     104300                                                         FGH           
     104200                                                         FGH           
     104100                                                         H             

Figure 5. Five days of the DJ Index in a directional (down) market. 
Four day range (Nov 8 - 13) is 110700 to 104100 or $6,600.

*Meta-Profile is copyright CISCO 1987, 1990
Some prices are omitted to condense the display.

The Overlay Demand Curve, D3

Multi-Day Value in Auction Markets, the Overlay Demand Curve Graphic

From the inception of Market Profile the emphasis has been on intra-day trading. Steidlmayer was a floor trader (local) who developed the Market Profile for a time horizon mostly limited to the current day, based on value location from the previous trading day. While profiles reliably find day value in balanced markets, even in such balanced markets day-to-day profile value may fluctuate substantially.

The Overlay Demand Curve (18, 19, 20) was developed to extend the one-day range of the Meta-Profile to longer time-frames. An Overlay is a linear aggregation of profiles. Whereas the profile finds one day value (value area), the Overlay locates value for the time period desired (days or parts of days). Overlay's are especially valuable in identifying balanced markets. They show upper and lower limits (support and resistance), estimate risk and provide a distribution that can be analyzed (the shape can show how demand comes into the market).

Markets are known to be not serially correlated on a day-to-day basis (see ref8, pg 21 for an example). Simply comparing one day's profile to the next offers no firm ground for analysis if the market condition has changed. The goal is to find market condition, where the market is in it's evolution from balance to trend. Markets go through a four step process (9), which can start with balance and end back at balance. A balance will move into the trend phase over a period of time, a part of a day or more. A trend's end and the beginning of a new balance also traverses a transition period governed solely by the behavior of market participants. An orderly four step progression often occurs, but it is not a path the market always takes. There may be a break out from a balance that is quickly followed by a return to balance (false breakout). Or a trend may pause and retrace before continuing on. Any of the four steps may happen in a time as short as minutes or as long as days.

A Balanced Overlay Demand Curve
A way to examine time frames longer than a day is to linearly combine the requisite number of Meta-Profiles, forming an Overlay Demand Curve (16). The composite telescopes temporal differences into a master, multi-day time frame. As an example, the five days of Meta-Profiles in figure 4 are combined into the Overlay Demand Curve of figure 6.

                         Overlay Demand Curve*

DJIA (CBOT)  DAY       DEC 00   First date: 10 31  0   Last date: 11  6  0
 VOL DETAIL   TK VOL  HITS  TIME: 1=NEAR, 2=NEXT BACK,...
   110800          9   1 1
   110700         20   2 11
   110600         53   4 1111
   110500        155   8 11111155
   110400        274  15 111111111444555
   110300        401  17 11111111114445555
   110200        299  18 111111111444555555
   110100        412  17 11111133444455555
   110000        317  14 11113334444555
   109900        223  21 111333333444444444555
   109800        372  22 1233333333444444444455
   109700        472  23 12333333333334444444445
   109600        385  28 1122333333333333344444444455
   109500        381  24 112233333333333344444455
   109400        299  20 11222333333444445555
   109300        340  16 1223344445555555
   109200        263  15 122234445555555
   109100        228  13 1122224555555
   109000        296  17 11112222224555555
   108900        255  13 1111222222555
   108800        213  11 11222222555
   108700        127  10 1222222255
   108600         77   6 222225
   108500        117   7 2222222
   108400         95   7 2222222
   108300         71   5 22222
   108200         22   2 22

Figure 6. Overlay Demand Curve of DJ Index, Dec 00, for 5 Days
Data is for Oct 31, (5), Nov 1 (4), Nov 2 (3), Nov 3 (2) and Nov 6 (1).
Column 1 is price, column 2 is tick count, column 3 is the number of 
occurrences (TPO's) at each price and column 4 is the days for the TPO's.
For instance, the price 110000 experienced 14 hits (TPO's) of which 4 
came on day 1 (Nov 6), 3 hits came on day 3 (Nov 2), 4 hits on day 4 (Nov 1) and
3 hits on day 5 (Oct 31).

Overlay Demand Curve is a trademark of CISCO Futures 1987.
Some prices are omitted to condense the display.

                       Overlay Demand Curve

DJIA (CBOT)  DAY       DEC 00   First date: 11  7  0   Last date: 11 13  0
 VOL DETAIL  VOL   HITS  1=NEAR, 2=NEXT BK,...
   110700         28   2 44
   110600         24   4 4444
   110500        131  10 4444555555
   110400        265  18 444444445555555555
   110300        263  20 44444444455555555555
   110200        285  21 444444444455555555555
   110100        345  20 44444444445555555555
   110000        322  16 4444444445555555
   109900        214  10 4444445555
   109800         82   6 444445
   109700         26   3 444
   109600         10   2 44
   109500         20   1 4
   109400         54   3 334
   109300         54   4 3334
   109200         54   6 333334
   109100         90   7 3333333
   109000        155   9 333333333
   108900        158  10 3333333333
   108800        183  13 3333333333333
   108700        148  10 3333333333
   108600        122   8 33333333
   108500        108   9 333333333
   108400         92   7 3333333
   108300         71   6 333333
   108200         86   5 33333
   108100        113   7 2223333
   108000        133   8 22222333
   107900        143   9 222222333
   107800        168  10 2222222333
   107700        164  10 2222222333
   107600        160  10 2222222333
   107500        113  11 22222222333
   107400        137  10 2222222233
   107300        189  12 222222222233
   107200        161  11 22222222233
   107100        109   8 22222233
   107000        114   8 22222233
   106900         90   7 2222233
   106800         93   5 22233
   106700         54   5 22233
   106600         28   3 223
   106500         32   2 23
   106400         52   3 112
   106300         15   2 11
   106200          8   2 11
   106100         13   3 111
   106000         37   5 11111
   105900         60   5 11111
   105800         55   6 111111
   105700         49   6 111111
   105600         64   6 111111
   105500         41   6 111111
   105400         73   8 11111111
   105300        106   9 111111111
   105200         56   9 111111111
   105100         78   7 1111111
   105000         60   7 1111111
   104900         45   6 111111
   104800         44   4 1111
   104700         12   6 111111
   104600         37   4 1111
   104500         53   4 1111
   104400         34   4 1111
   104300         48   3 111
   104200         19   3 111
   104100          1   1 1

Figure 7. Overlay Demand Curve of DJ Index, Dec 00, for 5 Days
Data is for Nov 7, (5), Nov 8 (4), Nov 9 (3), Nov 10 (2) and Nov 13 (1).
Column 1 is price, column 2 is tick count, column 3 is the number of 
occurrences (TPO's) at each price and column 4 is the days for the TPO's.
For instance, the price 109000 experienced 9 hits (TPO's) of which all 
came on day 3 (Nov 9). 


Overlay Demand Curve is a trademark of CISCO Futures 1987.
Some prices are omitted to condense the display.

A Balance: 3 DAY OVERLAY 

META-PROFILE OVERLAY*
DEC 00 DJIA (CBOT)  DAY      
11 06 00 TO 11 08 00

 PRICE DYS  L/F ROT PROFILE *  TPOS TPO VOL OVERLAY *
 
110800  1   6     6               1 X
110700  2   68    68              4 XXXX  <== Upper Limit
110600  2   68    68              5 XXXXX
110500  3   68    678            14 XXXXXXXXXXXXXX
110400  3   68    678            21 XXXXXXXXXXXXXXXXXXXXX
110300  3   68    678            27 XXXXXXXXXXXXXXXXXXXXXXXXXXX
110200  3   68    678            29 XXXXXXXXXXXXXXXXXXXXXXXXXXXXX
110100  3   68    678            23 XXXXXXXXXXXXXXXXXXXXXXX
110000  3   68    678            20 XXXXXXXXXXXXXXXXXXXX
109900  3   68    678            13 XXXXXXXXXXXXX
109800  3   68    678             7 XXXXXXX
109700  2   68    68              4 XXXX
109600  2   68    68              3 XXX
109500  2   68    68              3 XXX
109400  2   68    68              3 XXX   <== Lower Limit and Close Nov 8
109300  2   68    68              2 XX
109200  2   68    68              2 XX
109100  1   6     6               1 X
109000  1   6     6               3 XXX
108900  1   6     6               4 XXXX
108800  1   6     6               2 XX
108700  1   6     6               1 X

Figure 8. Three day Overlay for DJ Index, Nov 6, 7, 8.
This figure is similar to figures 6 and 7, with 'TPO VOL OVERLAY'
replacing the column  '1=NEAR, 2=NEXT BK,...' and 'ROT PROFILE'
indicating days (6 = Nov 6, 7 = Nov 7 and 8 = Nov 8).
This three day Overlay uses some of the same data as found in Figures 4 - 7.
Shown is a three day balance (single distribution).

Overlay Demand Curve is a trademark of CISCO Futures 1987.
Some prices are omitted to condense the display.

Breakout from a 3 Day Balance: 

META-PROFILE OVERLAY*
DEC 00 DJIA (CBOT)  DAY      
11 06 00 TO 11 09 00

 PRICE DYS  L/F ROT PROFILE *  TPOS TPO VOL OVERLAY *
 
110800  1   6     6               1 X
110700  2   6     68              4 XXXX
110600  2   6     68              5 XXXXX
110500  3   6     678            14 XXXXXXXXXXXXXX
110400  3   6     678            21 XXXXXXXXXXXXXXXXXXXXX
110300  3   6     678            27 XXXXXXXXXXXXXXXXXXXXXXXXXXX
110200  3   6     678            29 XXXXXXXXXXXXXXXXXXXXXXXXXXXXX
110100  3   6     678            23 XXXXXXXXXXXXXXXXXXXXXXX
110000  3   6     678            20 XXXXXXXXXXXXXXXXXXXX
109900  3   6     678            13 XXXXXXXXXXXXX
109800  3   6     678             7 XXXXXXX
109700  2   6     68              4 XXXX
109600  2   6     68              3 XXX
109500  2   6     68              3 XXX
109400  3   69    689             5 XXXXX <== Lower Balance Limit for Nov 8
109300  3   69    689             5 XXXXX       Breakout within 10 minutes of open
109200  3   69    689             5 XXXXX
109100  2   69    69              7 XXXXXXX
109000  2   69    69             11 XXXXXXXXXXX
108900  2   69    69             14 XXXXXXXXXXXXXX
108800  2   69    69             12 XXXXXXXXXXXX
108700  2   69    69             11 XXXXXXXXXXX
108600  1    9    9               8 XXXXXXXX    <== Close Nov 9
108500  1    9    9               9 XXXXXXXXX
108400  1    9    9               7 XXXXXXX
108300  1    9    9               6 XXXXXX
108200  1    9    9               4 XXXX
108100  1    9    9               4 XXXX
108000  1    9    9               3 XXX
107900  1    9    9               3 XXX
107800  1    9    9               2 XX
107700  1    9    9               3 XXX
107600  1    9    9               3 XXX
107500  1    9    9               3 XXX
107400  1    9    9               2 XX
107300  1    9    9               2 XX
107200  1    9    9               2 XX
107100  1    9    9               2 XX
107000  1    9    9               2 XX
106900  1    9    9               2 XX
106800  1    9    9               2 XX
106700  1    9    9               2 XX
106600  1    9    9               1 X
106500  1    9    9               1 X
 
Figure 9. Four day Overlay for DJ Index, Nov 6, 7, 8, 9.
'ROT PROFILE' indicates days (6 = Nov 6, 7 = Nov 7, 8 = Nov 8 and 9 = Nov 9).
This three day Overlay uses some of the same data as found in Figures 4 - 7.
The three day balance (single distribution) of figure 8 is augmented with the
data of Nov 9, illustrating a breakout. The single distribution of figure 8
is centered at 110200. That distribution still shows, but another has been
added, centered at 108900. The breakout below 109400 gave an alert that the
market was transitioning from balance to directional. The formation of the
second, lower distribution confirmed the change in market condition.

Overlay Demand Curve is a trademark of CISCO Futures 1987.
Some prices are omitted to condense the display.

META-PROFILE OVERLAY 
DEC 00 DJIA (CBOT)  DAY      
11 13 00 TO 11 16 00

 PRICE DYS  L/F ROT PROFILE *  TPOS TPO VOL OVERLAY *
 
108500  1         7               1 X
108400  1         7               1 X
108300  1         7               4 XXXX  <== Upper Limit
108200  1         7               5 XXXXX
108100  2         67              5 XXXXX
108000  3    8    678             7 XXXXXXX
107900  3    8    678             8 XXXXXXXX
107800  3    8    678             9 XXXXXXXXX
107700  3    8    678            14 XXXXXXXXXXXXXX
107600  3    8    678            17 XXXXXXXXXXXXXXXXX
107500  3    8    678            15 XXXXXXXXXXXXXXX
107400  3    8    678            18 XXXXXXXXXXXXXXXXXX
107300  3    8    678            23 XXXXXXXXXXXXXXXXXXXXXXX
107200  3    8    678            25 XXXXXXXXXXXXXXXXXXXXXXXXX
107100  3    8    678            20 XXXXXXXXXXXXXXXXXXXX
107000  3    8    678            16 XXXXXXXXXXXXXXXX
106900  3    8    678            15 XXXXXXXXXXXXXXX
106800  3    8    678            12 XXXXXXXXXXXX
106700  2    8    68              9 XXXXXXXXX
106600  1         6               7 XXXXXXX
106500  1         6               5 XXXXX  <== Lower Limit (est)
106400  2   5     56              6 XXXXXX
106300  2   5     56              5 XXXXX
106200  2   5     56              5 XXXXX
106100  2   5     56              5 XXXXX
106000  1   5     5               4 XXXX
105900  1   5     5               5 XXXXX
105800  1   5     5               6 XXXXXX
105700  1   5     5               6 XXXXXX
105600  1   5     5               5 XXXXX
105500  1   5     5               5 XXXXX
105400  1   5     5               7 XXXXXXX
105300  1   5     5               8 XXXXXXXX
105200  1   5     5               9 XXXXXXXXX
105100  1   5     5               7 XXXXXXX
105000  1   5     5               6 XXXXXX
104900  1   5     5               5 XXXXX
104800  1   5     5               4 XXXX
104700  1   5     5               4 XXXX
104600  1   5     5               4 XXXX
104500  1   5     5               4 XXXX
104400  1   5     5               4 XXXX
104300  1   5     5               3 XXX
104200  1   5     5               3 XXX
104100  1   5     5               1 X
 
Figure 10. Four day Overlay for DJ Index, Nov 13 , 14, 15, 16.
 
Overlay Demand Curve is a trademark of CISCO Futures 1987.
Some prices are omitted to condense the display.

Appendix 2. Practices

Psychology is an agreed effect in markets, but one that is hard to measure. The recent tech bubble was identified many times and by many people. Still, it's end was a surprise. The end of a long time-frame market effect can probably not be predicted with any accuracy. However, on a day-to-day basis value theory can be a useful guide. An example is the short time large psychological market effect of the London Subway bombing.
Meta-Profile/Overlay Trading the London Blast


The Market Unit is a construct of auction theory. It separates the balances from the trends. The combination of a balance period followed by a directional phase defines a unit. Market Unit is a fundamental market element.
The Market Unit


A large effort is spent on 'technical', or numeric market analysis. A substantial part of this effort is on cycles. The question is, does the market data support these efforts; is the data conditioned for the task. If a market has no 14 day cycle, a smoothing 14 day moving average is smoothing noise.
Market Waves and Tech Anal


Large returns ()both negative and positive) are not rare. This was the clue that led the econophysicists to suspect the application of the gaussian distribution in CAPM. Large returns can be demonstrated with ordinary, everyday data.
CISCO internal report

'Noise traders' is the name economists give to speculators. The inference is that the other class 'insiders' know what they are doing (are profitable traders). The performance of mutual funds belies the definition.
From Noise Trader to Insider


Market response time is a normal adjunct of market feedback. But what is that time? Value theory analyses imply the time is variable with market condition, but an average of about 30 minutes is found.
CISCO internal report

Multiple trading opportunities within a day are desired by all traders. Meta-Profile identifies them. Such opportunities can be qualified by the Overlay Demand Curve market condition.
Markets do NOT Turn on a Dime


Trading models are desired by most short timeframe (day) traders. A value theory methodology finds 'market' opportunity from basic principles. But, a trading model is incomplete without 'trader strategy', unique to each trader.
Developing a Trading Model


Market efficiency suggests markets process all new information instantly and hence, markets cannot be successfully traded. A test is the probablility of a new high following today's contract high. The chance of yet another higher high following today's new high within the next ten days is 80 percent; 60 percent for two higher highs and 50 percent for three higher highs.
Ref: Persistence of trends: Commodities Mag. Feb 1973
Markets do NOT Turn on a Dime

Another test of market efficiency examines the day to day price correlation within a trend. In the trend