Auction Market Value Theory & Analytics

TPO Count The Time Price Opportunity is the smallest unit of market measurement. As mentioned earlier, TPO stands for Time, the market's regulator, and Price, the marker's advertiser, together creating the Opportunity to buy or sell at a given price at a particular time.

Monitoring the TPO count helps evaluate other timeframe control within the developing value area. Specifically, the TPO count measures the level of imbalance (when such an imbalance exists) between the other timeframe participant and the day timeframe (mostly local) trader.

The key to understanding how an imbalance can occur is to recognize that the other timeframe buyer does not deal directly with the other timeframe seller. Recall that the local, or scalper, acts as a middleman between these two longer-term participants. Thus, the other timeframe buyer generally buys from a local, and the other timeframe seller typically sells to a local. Imbalance occurs when there are more other timeframe buyers than sellers or more other timeframe sellers than buyers, leaving the local with an imbalance. Before we can learn how to measure this imbalance, we must first gain a better understanding of how the locals conduct their business.

The locals position themselves between the flow of outside buy orders and outside sell orders (orders placed predominantly by off-floor, other timeframe participants). For example, suppose that a floor broker receives an order to sell 100 contracts at, say, $5.00 per contract. The locals would buy the contracts from the broker and then turn around and sell them to another floor broker who has an order to buy 100 contracts at $5.02. In this situation, the locals perform their role of facilitating trade and, in return, receive a small margin of profit.

The trade facilitation process is seldom so ideal, however. Often there are more other timeframe buyers than sellers—causing the local's inventory to become overloaded. For example, if an usually large number of sell orders enter the pit, the local's inventory begins to accumulate to a point where he gets "too long." In other words, the local has purchased too much from the other timeframe seller. If the other timeframe buyer does not appear relatively quickly, the local must bring his inventory back into balance in some other way. One's first thought would be that the local simply needs to sell off his excess inventory. However, this is not so easy, for reversing and selling would only serve to accentuate the selling that is already flowing into the market. Therefore, his first priority is to stop the flow of outside sell orders by dropping his bid in hopes that the market will stabilize and he can balance his inventory. If lower prices do not cut off selling, the local may then be forced to "Jump on the bandwagon" and sell (liquidate) his longs at a lower price.

Viewed from another perspective, suppose you decided to try to make extra spending money by scalping football tickets. A month before the chosen game you purchased twenty tickets from the box office at $10 each. In this case, the box office is the other timeframe seller and you are the day timeframe buyer. Over the ensuing month, your team loses every game and slips from first to fifth place. Not surprisingly, the attendance is dismal on the day of the game at which you intend to sell your tickets. Few other timeframe buyers (fans) show up. In order to get rid of your inventory and recover at least part of your costs, you are forced to sell your tickets at $8 instead of $12 or $15. In this example, there were more other timeframe seller than buyers. Consequently, price had to move lower to restore balance.

The same concepts apply to the futures marketplace. If we can identify imbalance before it corrects itself, then perhaps we can capitalize on it, too. Tails and range extension are more obvious forms of other timeframe presence, but on a volatile or choppy day, much of the imbalance occurs in more subtle ways within the value area.
The TPO count is an excellent method for evaluating day-to-day imbalance that occurs within the developing value area.

The "TPO count" is found by isolating the point of control (the longest line closest to the center of the range), summing all the TPOs above it and comparing that number to the total number of TPOs below it. Single print tails are excluded from the count, because their implications are clear and have already been considered when examining activity on the extremes. Remember, the point of control is significant, because it indicates the price where the most activity occurred during the day and is, therefore, the fairest price in the day timeframe (price x time= value).


The total TPO figure above the point of control represents other timeframe traders willing to sell and stay short above value, while total TPOs below the point of control represent other timeframe traders willing to buy and stay long below value. The resulting ratio is an estimate for buyer/seller imbalance in the value area. For example, a ratio of 32/24 breaks down to 32 selling TPOs above the point of control, and 24 buying TPOs below. Note that a value area is not specifically calculated. Rather, the methodology of the TPO count, (i.e., single-print rejections are not counted), implies value.

Figure 3-1 illustrates the TPO count.

Figure 3-2 are three developing versions of the treasury bond Profile of January 29,1988. Profile A displays time periods A-F of the day. The 29th saw an opening substantially higher than the close of the previous day, followed by an early morning sell-off as price attempted to auction down.

As the trading day proceeds, however, our concern is with discovering exactly who is more aggressive (who is in control) within the developing value area. Occasionally, activity reflected by range extension and tails will give us some clues regarding which participant is more aggressive on the extremes. But the value area battles are more subtle and often much slower to develop. Monitoring the TPO count through time helps us measure how the wins and losses are stacking up and gives some indication of who will be the victor in the battle for control in the body of the Profile.

Examine Profile A again. A-F period registers a 13/21 TPO count, which favors the other timeframe buyers. Consider what this 13/21 TPO count logically means: the fact that the TPO count below the point of control is growing larger indicates that, although price is spending time below the point of control, price is not going anywhere.

Had other timeframe sellers been aggressive within the value area, there would have been downside range extension. Range extension would have lowered the point of control (and the perception of value), therefore balancing the TPO count and quite probably shifting it in favor of the sellers. With a TPO count building in the bottom half of the value area (buyers) and no selling range extension, chances are that it is the floor traders, or locals, who are selling to the other timeframe buyers.

Profile B in Figure 3-2 shows the TPO count through H period moving back into balance at 21/22. This temporary return to balance indicates that the locals' inventories became too short—in essence, they had sold too much—and were forced to cover (buy back) some of their shorts to bring their inventories back into line. Consequently, price rotated up in G and H periods, as the locals restored balance to their inventories.

Again, in the final Profile, other timeframe sellers were not present as price continued to rotate toward the day's upper extreme. The L period TPO print at 93 21/32 (Bonds trade in 32nds of $1000), and subsequent L period range extension pulled the point of control higher, confirming a final TPO tally in favor of buyers, 30/47. This end-of-the-day imbalance often provides momentum into the following day.

Hi,

Excellent inferences (trading logic) MsVega. Thanks for posting. Learn't a lot.

Prem

Auction Market Value Analytics(tm) Report
Copyright CISCO 2007
Auction Market Value Analytics (tm)


Successful trading is easy to define when based on value. The breakout (initiative) trader enters on a change of value (a breakout when the market has been balancing) and exits when value stops changing (trend end, start of congestion). The trader of balances (responsive) seeks to close out when value begins changing (trend begins). The key, obviously, is to track value. Value, strictly defined, is constant only in balanced markets. (Imagine a strongly trending market: value is constantly changing and hence is not defined in a measurable way.)

Value Analytics(tm) is the quantification of profile and market condition variables, providing the data for finding value The multi-day structure of Value Analytics is based on the discovery that a single day's data is inadequate to define the development of a balanced market condition. The minimum time is of the order of three days.

Value Analytics views the market from a three day perspective. examining the flow of each profile variable over that time frame.

The first step for the serious trader is to understand value. In auction markets, value is an experimental measure: it is what the majority of traders think it is at that point in time. They vote by trading. Often overlooked in profile literature is the fact that value can generally only be found for a market in balance. Balance can be defined only in conjunction with a time frame: a 3 day balance, a 9 day balance, etc. Further, a market can be in a 10 day balance while trending in the 4 day time frame. Hence the beginning of market analysis must be an understanding of the market's condition (balance or imbalance). Value Analytics starts with a measure of market condition on a 2, 3, 5 and 10 day basis.

The CBOT Market Profile (1985) manual opened the door to single day value evaluation. Market Profile methodology is propounded more clearly in the CBOT Market Profile Manual (1991) . Auction Market Value Analytics) (AMVA) assumes that the user has at least a rudimentary knowledge of profile concepts and methodology (i.e., form of a profile display (bell curve), point of control, value area, initiative trading, responsive trading, etc.). AMVA integrates longer term Overlay measures, profile methodology and general market principles to find value and other trading reference points as a guide to the development of one's trading strategy (set-up).

Value is continuous and migratory, Price undergoes constant change (ticks); value change is much more measured. Still, the local value (daily value area) fluctuates even in bounded, balanced markets. If value changes slowly over a period of days (migrates), the upper and lower limits of the balance are adjusted at the end of each day's recalculation and balance is maintained. The balance boundaries may change somewhat. This is value 'creep'. A creeping market remains the province of the responsive trader.

A rapid change in value is evidenced by a price breakout from the established balance limits, with price continuing directionally. In this case the end of day recalculation does not find a balanced market (a trend is underway). Initiative traders work this arena. Obviously, the market's condition determines the appropriate method of attack.

Auction Market Value Analytics starts by identifying balanced market conditions, as stated. AMVA uses timeframes of 1, 2, 3, 5 and 10 days for the balance search. Balances, as they exist, give the information required for entry and exit decisions. The initiative trader is constantly seeking the point where price breaks out (for entry) and once in, immediately turns to looking for signs of congestion (for exit). The responsive trader is active in balanced conditions (selling highs and buying lows). When a (breakout) trend begins, the responsive trader goes to the sidelines. Market condition is thus the arbiter, the first level of analysis. Your knowledge of market condition gives you a broad unbrella for your trading activities: the go, no go signals of balance and imbalance.

As a trader (either responsive or initiative) continues with a trade, the market is changing (continuous and migratory, remember?). Market behavior reflects the collective intent of all participants; the problem lies in divining their intent from the data flow (ticks for the most part) as quickly as possible. Here techniques introduced by Market Profile*, Meta-Profile**, standard market analysis and CISCO research come into play. This second level consists of e.g.: starting with market condition and then comparing price with earlier value (value area or the limits of a balance); volatility change, initial balance behavior, profile value center and change, TPO analyses, etc. Each of the 30 or so reference points in the Value Analytics table have the potential for contributing information about the market's intent. Each profile variable, by itself, rarely dominates. It is the collective that counts. (This is the reason that traders who concentrate on value area alone find a high variability in their trading results.)

Not all reference points are important in any one case. You must learn about each of them and how to discriminate. This is called experience. In some profile teaching the operative word is 'holism', looking into the pot with some 30 or so elements and somehow divining which control the taste today. Value Analytics goes the other way, separating the elements, measuring them and their flow individually and eliminating those that do not apply to the case at hand.

Feedback is involved: if your understanding of a particular item (say trade facilitation) is in error and you continually make that error in your market strategy; your error will become apparent to you rather quickly. This illustrates the value of isolating the reference points and examining them individually.

The job of Auction Market Value Analytics is to integrate the two information levels, market condition (Level 1) and individual reference points (Level 2). This provides the trader the most current information available on value and the way it is changing. Both level 1 and level 2 informations are tabulated for the last three days, so that any reference point may be traced and its trajectory determined. This end-of-day data gives you the time to do a thorough pre-market analysis the night before (i.e. to develop a trading strategy for the new day). You are also prepared for the after-market, should you wish to trade it.

The trader has two jobs; First is to integrate the latest information (last three days) into a strategy for the coming day. This primarily draws on the Yesterday's Data on the Value Analytics pages. These data provide the flow of the market, where it is headed. The result is a trading strategy for tomorrow. Second is implementation of that strategy in trading tomorrow's market (Current Day).

Current Day market Value Analytics offers a current analysis of the market, including it's interaction with the yesterday data list. These data offer an answer to the trader's toughest question, "what is my market doing now (i.e. continuation or not)".

* Market Profile is a trademark of the CBOT.
** Meta-Profile is a trademark of CISCO. Meta-Profile methodology is copyrighted.

Value Analytics is the culmination of the Market Profile revolution begun by J.Peter Steidlmayer and the Chicago Board of Trade in 1985. Their focus on day value and the dynamics of profile formation had the stated goal of "giving the trader an edge". In the statistical universe of an auction market, even a modest edge can lead to substantial profits in the long run (look at what an 'edge' has done for the statistics based casino business).

As portrayed in the 1985 CBOT Market Profile Manual, Market Profile describes a market that traces out a bell shaped (normal distribution) curve: little trading at the higher and lower prices, with a lot of activity at the middle prices. Value is found as the central 70 percent of the cleared trading volume. Reference points such as day types, range extension and tails are defined. These analyses are based on the CBOT Liquidity Data Bank of cleared price, time and volume. This data bank is unique to the CBOT and the CBOT advertised a product, "The CBOT Market Profile Service is a Detailed Breakdown of Pit Activity on the Exchange Floor. Volume and Price Information...Measured Against Time." Market Profile parameters discussed below were all defined in terms of the CBOT Liquidity Data Bank (LDB) data. Data from the LDB volumes is used to find 'market condition', where 'trend' refers to intra-day behavior.

Market Condition in Value Analytics is based on multi-day (at least 3 days) behavior of the market; resulting in a market that is either in balance (with a well defined upper and lower limit) or one with changing value (a directional market).

Within the LDB framework, the myriads of daily market profile shapes are parsed out and meaning is inferred from their shapes and (cleared) volume. The process is primarily pattern recognition, as is abundantly clear from the many examples in the Manual and the various other Steidlmayer publications. And, of course, it is an end of day process (clearing was completed around 9 PM).

A practical problem for most traders was the limitation of profile concepts to CBOT futures. e.g. Value area is defined in terms of (cleared) volume and the CBOT LDB is the sole source. Indeed, all the profile concepts required the LDB. Not everyone wanted to trade just CBOT futures and CISCO as an information provider to traders quickly saw the need to be able to determine value for non-CBOT markets. This resulted in the development of the Tick-TPO or Meta-Profile from tick data and CISCO published on the concept in 1987 (twice).

Meta-Profile generalized the 'Market Profile' type analyses to all auction markets, with tick data taking the place of cleared volume. The CISCO Overlay Demand Curve (tm)(merged/composite profile) for locating market condition (balance, imbalance) made the leap from day value to longer time-period value.

A follow-on book to the 1985 CBOT Market Profile Manual, by Dalton, Jones and Dalton (Mind Over Markets, 'MOM, 1990'), more clearly organizes the profile field. Although their analyses were mostly on CBOT futures, they did follow the CISCO lead and applied profile analyses to several non-CBOT futures. (MOM, Appendix 1, explains the 'TPO Value-Area Calculation', without attribution to the source, CISCO.) 'MOM', too, is heavy on pattern recognition. And the same is true to an extreme in the new book by the same authors (Markets in Profile, 2007) .

Enter Auction Market Value Analytics (AMVA):
There is much worthwhile in the market insights of the Market Profile publications. The difficulty lies in the required pattern recognition methodology (holism) needed for their application to real world market analysis. Value Analytics is designed to combine the older Market Profile discoveries with the newer Meta-Profile and Market Condition methodology into an analytical technique that results in objective market measurements.

Where pattern recognition provides inferences, Value Analytics offers more solid guideposts. For instance, early termination of an auction (no tail at, say, the low) may tell pattern recognitionists that buyers took over, with the resulting group of potential consequences (e.g. the start of a trend).

By comparison, data from Value Analytics fills in the blanks:
1) Is the market in balance?, If yes the Overlay Demand Curves (merged/composite profile) provide
upper and lower limits
and the tail may have little meaning until and unless there is an upside breakout.
2) Is the market growing (increasing range and volume) or slowing? The AMVA trader
can tell by following:
the track of TPO counts, tick counts, the trade facilitation factor, etc.
3) Is a tail incomplete? AMVA gives you quantitative measures of
subsequent behavior.
4) Is the market starting to become directional?, i.e. is price nearing a balance limit?
The Overlay for various timeframes provides an answer.
5) And a 'MOM' favorite, what is the attempted direction? AMVA calculates it.

Okay mzvega, I have the theory, now I would like to try to make it practical. I tried to look at the old thread to get the indicator and templates, but it is a spaghetti dinner now. Since you have reintroduced the correct concept on a clean thread, what do I need to do to get charts to watch this process unfold?

My mind seems to relate this to point and figure charting, except we get to graphically see price value build along the horizontal axis as value is determined. I like the idea of not being constrained by time bars, as each one of those is different depending on where you start. With this type of analysis, the profile will look the same whether you are in Siberia or Washington D.C.

How can I get started?

You need a tpo chart that displays the individual daily profiles, you also need a indicator that has the ability to merge multiple days. If you can find one that does both, even better. The original creator of the chart made an indicator compatible to the 600 build found here. (no merge feature) http://www.mql5.com/en/market/product/3033

However, in the past I used a script created by hanover, to export the tpo’s (objects) to an excel sheet, and created a macro to do the rest.
The older indicators will work on the new build (no merge feature) use .v102 or v120. Look in the files of the different indicators (originals) if it does not have a .dll file, the indicator will work.

Bandung had created an“overlay” indicator, however it doesn’t work with the new 600 build. I don’t know where he’s at with it, or if he has plans to make it available to everyone. As you can tell by the other thread, there was obviously no “audience” for it there. That’s why I started this thread, Im hoping to lure a few of the members back, who might be willing to share, or come up with a longer term solution.

Yes PnF is very, very similar. Just like you can easily plot pnf on a spreadsheet, it’s almost the same (logic) you just concatenate.

In the holistic universe of profile pattern recognition a trader must have a very large amount of 'inferential' information, putting together a number of clues into a final diagnosis. If the market does not behave as expected, what part of the whole can be examined to explain the failure? On the other hand, AMVA analysis is built from discrete elements . These elements are put together only at the end of the study. If the analysis proves wrong it is easy to sort out and examine the offending element(s). In fact, as markets change, as some do over time, AMVA data reflects these changes and the trader can make adjustments.

The difference between the two disciplines (pattern recognition and AMVA) is most easy to understand by what is meant by trader self-understanding. Self understanding to the pattern recognitionist includes all the elements of the various patterns, how they interact and the trader's involvement in the process. Self understanding to the AMVA trader is simply a knowledge of the level of risk that trader is comfortable with.

What is Auction Market Value Analytics (AMVA)?
We have made some comparisons between pattern recognition and Value Analytics. In a larger sense AMVA represents market concepts first beginning as ideas and qualitative evaluations being transformed into more measurable, quantitative numbers. The move is from practical observational methodology developed through the genius of Steidlmayer for his own benefit as a trader, to the more analytical format of AMVA that can be applied by any educated trader. AMVA is not a formula, but it distills much of the pattern recognition information into useable numbers for a trader's strategy.

AMVA is the confluence of the early work on profiles by Steidlmayer with the subsequent analytical studies of CISCO. The insights of Steidlmayer broke new ground in the understanding of auction markets. His contribution cannot be diminished. Without his perceptive understanding of the markets from the perspective of being in the pits, there would possibly be no valid auction market analyses today. His intuitive approach developed truths that we believe are better applied by objective analyses to the (non-pit) markets of today. The bell shaped curve concept was critical to the early description of the market. Today we know that a true bell shaped market curve is a rarity. But the concept gave market profile it's original legs (value area). The field began rounding out with the discovery by CISCO that TPOs are valid replacements for the cleared volume of the original profile, for calculating value. That meant that all auction markets, not just the CBOT, could be analyzed. As noted above, CISCO's development of the Overlay Demand Curve (merged/composite profile) identifies a market's condition (balance). Putting it all together has produced a trading tool, AMVA. The AMVA trader can use objective measures to replace much of the intuitive, holistic market profile teaching of today. One thing has not changed. AMVA methodology, like the original CBOT Market Profile, gives the trader an edge, not a certainty.

The first goal of Value Analytics make a thorough analysis of a market prior to open, to offer corroborating (or not) pre-opening market behavior and finally, to track and understand a market as it trades throughout the day.

Heretofore, Market Profile has been the only candidate for market understanding, primarily, for most, via the value area. Value area remains important if used properly, but it is only one of the numerous elements needed to analyze a market. Typical questions answered objectively by Value Analytics are:
Is the market in balance? Always the primary question.
Is volume increasing, decreasing?
Is the price range increasing, decreasing?
How well is the market facilitating trade?
Is the Initial Balance widening, narrowing?
Is the point of control rising, falling?
Is the value area increasing, decreasing?
Are the tails showing completed auctions?
Are the range extensions consistent?
What is the market 'attempting' to do?

Limitations on Analysis
Any measurement depends on the quality of the data. Statisticians are concerned with sample size, knowing that a too small or poorly chosen sample will give unreliable results. In our analysis of market data, say ticks, we are sampling a market. Value for a day, for instance, is the central 70 percent of the TPOs. How many TPOs are needed? How many TPOs will make the cluster (quasi-bell) a reliable sample? TPOs come from ticks and as a starting point we count ticks. Restated, how many ticks are required for an adequate sample?

A simple rule of thumb minimum is 200 ticks per day. In a four hour market this is about a tick per minute. A 200 ticks per day market will have extended periods of inactivity. A more reasonable cutoff is in the neighborhood of 500 ticks per day (about 2 ticks per minute average). Typically, there are about 60 deliveries with 500 or more ticks per day. Here tick means 'change in price', the actual definition of ticks, not the transaction count (time and sales) so often carelessly called ticks.We don't want to confuse Tick Counts (price ticks) with Tick Volumes. Two hundred ticks in a six hour market is about one tick every two minutes. There are likely to be some fairly long periods with no trading at all; so that market is not facilitating trade very well--you might get some ugly fills on a market order.

It is easy to be fooled by a bar chart of a thin market. For example, the grains often will trade heavily in July and December (old crop, new crop) the October (e.g. soybean oil) may have little activity. However, the the high - low price range will approximate the heavier traded deliveries. Why? Arbitrage. The exchange posts the opening and closing prices to keep all deliveries in line. Even if there were no trades, a bar chart would show a range for the day. For there to be a valid auction for you to trade, someone needs to take the other side.

There are some 30 entries in the Value-Analytics Table. That may seem a lot and it is. But the market is complex and that is shown in a number of ways. At any time some of the 30 may apply, some may not. A problem with standard Market Profile analysis is that you, the trader, are expected to recognize (holistically) the current pattern the market is describing (day type). Then you are supposed to integrate the changes that are constantly occurring. This can be a tall order. At end of day, of course, there are no dynamic changes. But there will be as you trade on this information tomorrow.

In preparing for tomorrow with the Value Analytics the average trader can divine a market's behavior in minutes. With that information under one's belt, setting up a trading strategy is much, much simplified. It therefore becomes possible to analyze several markets for the next day's trading. Diversified trading can be a reality, with all the benefits that come with the risk reduction.

Tomorrow pre-open: for markets with overnight (electronic) trading
Currently, some markets begin actively trading after midnight. Within an hour or two a comparison of current market behavior with the 3 day Overlay(merged or composite profile) gives a graphic picture of the market's follow-through of the previous day's movement. Adjustment of one's trading strategy generated last night may be needed. If so, it will be clear what has changed from the previous day.

Post close data used for analysis: Value-Analytics Report

1. Market Condition Location of Value
10 Day Overlay (TPO merged or composite profile)
5 Day Overlay (TPO merged or composite profile)
3 Day Overlay (TPO merged or composite profile)
2 Day Overlay (TPO merged or composite profile)

2. Meta-Profile: Value, Reference Points (3 consecutive days)

General
Price range
Number price ticks (Here tick means 'change in price', the actual definition of ticks, not the transaction count (time and sales))
TPO count
Tick count (total) (Here tick means 'change in price', the actual definition of ticks, not the transaction count (time and sales))
Trade Facilitation Factor
Volatility
Close as percent of high

Initial Balance
Price range
Number price ticks (Here tick means 'change in price', the actual definition of ticks, not the transaction count (time and sales))
TPO count
TPOs above/below

Value Area behavior
Price range
Price location
Point of Control
Point of Control half-hour symbols
TPOs above/below
Number price ticks (Here tick means 'change in price', the actual definition of ticks, not the transaction count (time and sales))

Tails
Location upper
Number price ticks upper (Here tick means 'change in price', the actual definition of ticks, not the transaction count (time and sales))
Completion or not upper
Location lower
Number price ticks lower (Here tick means 'change in price', the actual definition of ticks, not the transaction count (time and sales))
Completion or not lower

Range Extension
Price range upper
Price range lower

Attempted direction
Basis POC
Basis Rotation Factor

Multiple Distribution Days
Number distributions
For Each Distribution
Value area
POC
POC TPOs

Hi,

Looking forward to your posts. Please carry on.

Prem

Earlier it was noted that one effect of disequilibrium is that Auction Market Theory has an empirical base. The tenets of AMT leads one directly to the analysis. Unraveling the details inside a particular market situation draws on both market knowledge and experience. There are many possible Overlay/Merged Profile timeframes, for all different markets. The shortest one would use would have at least the most recent market condition in its entirety. This one covers five days of data. The market may be in one of four conditions (balance, transition to trend, trend and transition to balance). Further, trader activity and interest in the market may be of various kinds. Market Profile structures are universal, bringing the market forces together in a single display. The analyst's task is to read the market from AMT principles and set the strategy for the next day. The basics will all be there, but the reasons for the behavior will often come from experience and market understanding.

Observables are the raw data of Auction Market Theory. They range from prices and their behavior to constructs such as Market Profile and Overlay/Merged Profile. The constructs bring value into the picture. With value in hand, theory development can proceed.

Auction Market Theory Development. The Elements.
Now that the observables and their consequences have been considered, it is time to collect and sort out the descriptors of auction markets. General information brings auction markets into focus; what they are, how they are constituted. This information is the framework for understanding the details of the theory.

Elements of Auction Markets
General
Since financial auction markets are of diverse sorts, some of these general elements are prone to change. The viewpoint here is of exchange based trading. Movement to off-exchange trading will certainly alter the effects of exchanges and members. However, nothing in the forseeable future will change the fundamental way auction markets behave, the way prices are negotiated and the way demand affects value.

1) Future price levels are not predictable.
2) Markets display little day to day serial correlation.
3) Auction market: marketplace in which price is arrived at by negotiation.
4) Double-sided auction markets see activity by both buyers and sellers.
5) Auction markets are not generally equilibrium systems.
6) An auction market's structure is continuously evolving, being revalued.
7) An auction market is in one of two conditions: balancing or not balancing.
8) Demand fluctuates over the day timeframe.
9) Demand change drives price change.
10) Markets generate price data: ticks, open, high, low, close
11) Price ranges are developed by negotiation.
12) Some prices are accepted, some are rejected.
13) Market volume varies day to day.
14) Market range varies day to day.
15) Markets accumulate (balance) and distribute (trend).
16) Markets cycle (phases are: balance, test, trend, test, balance, etc.)
17) A market phase can exist for a short time (minutes) or long (days).
18) Market hours control aspects of trading: openings, closes, initial balance.
19) Heavier trading is seen on exchange opens and closes.
20) Auction markets are traded for short term appreciation.
21) Traders seek value.
22) Accepted prices define value.
23) Value is price over time. P x T = V x constant
24) Value based (price-time) data: Market Profile, Overlay Demand Curve.
25) Short timeframe trader holding period is from minutes to hours.
26) Longer timeframe trader holding period is days.
27) Longer timeframe traders move markets by accumulating positions.
28) Public day traders and public long timeframe traders seek trends.
29) Long term trending markets are controlled by long timeframe traders.
30) Four types of exchange members (floor, commercials, off-floor, public).
31) Most members seek balanced markets.
32) Most members are short-time frame traders.
33) Electronic platforms clear faster

Market Profile (MP)
Market profiles graphically display generally understood market characteristics. A market day displays price change from demand, rumors, aborted moves and trading strategies. Market Profile displays the structure of the day.

1) Price - Demand structure: a day's trading is quasi-bell shaped.
2) Accepted and rejected prices are displayed. Usually they are highs and lows.
3) The TPO is the minimum time for information to be decoded by the market.
4) TPOs developed because of market inefficiency in mirroring demand.
5) The Value Area is easily identified by it's bell shaped bulge.
6) The most heavily traded price is near the middle of the distribution (POC).
7) Volatility is measured by the average range of the half-hourly bars.
8) Market Profile displays market congestion.
9) Balancing markets are congesting.
10) Pausing trends are congesting.
11) Market Profile displays Initial Balance (first hour).
12) Demand change is often delineated in 'double (running)' Market Profile.

The Overlay/Merged Profile identifies the phase, the market condition. The Overlay is a multi-day construct. Market profile is a day structure. It's interpretation requires a larger framework, the phase of the market. A balanced profile in a balanced market phase would be interpreted differently than the same profile within a trending market.

Overlay Demand Curve/ Merged profile (ODC)
1) ODC collects day markets into longer term structures.
2) Demand Curve structure of ODC shows market condition, its phase.
3) ODC development shows continuation or change in phase.
4) Breakout from balance is an alert for trend.
5) ODC shows time evolution of a market with varying length Overlays/Merged Profiles.
6) Balanced markets define the distribution limits and risk.
7) ODC is Quasi-bell shaped, locates value region and shows price rotation.
8) In trending markets, ODC shows trend components including pauses.
9) The ODC distribution delineates expected and unexpected market behavior.
10) Expected/unexpected behavior leads to "if this, then that" analysis.
11) ODC may contain internal trends.
12) ODC balance may be skewed, indicating non-balance influences.

The Structure is Complete
Auction markets can be analyzed and understood with the elements posted above. These elements are the base of Auction Market Theory. Since the theory is empirical it is not falsifiable, i.e. it's validity rests upon it's utility. But markets change and new definitions may be required or old ones altered. Certainly there will be change in the exchange structure if electronic trading dominates. Or, the TPO, which is defined on a 30 minute basis, may move to another timeframe as new methods of information dissimination develop. Such change will not alter the fundamental ways auction markets work.

The elements of the theory are both descriptive and numeric. Facts of auction market behavior are useful for qualitative decisions, alerts the trader to other possible changes. Numeric elements are the stuff of hard trading decisions. The numerics can lead to trading rules.

Volatility

Volatility is a natural part of all auction markets. In CAPM it is the risk, arrived at statistically. AMT is slightly different. Volatility is related to the trading range; small in quiescent periods, larger in more active markets. It changes from day to day. Fluctuation grows with volume in the day timeframe. Daily trading range does give a gross estimate of market fluctuation.

A better working estimate of volatility describes activity within the day. Market Profiles are based on half-hour periods. Half-hour timeframes break down the day into manageable parts. More importantly, a half-hour appears to be the minimum average time for changes in demand to be reflected in the value (Steidlmayer Observations).

The average of the half-hour bars approximates the risk of a trade stop-out from either the long or short side. It is the 'noise' risk. If one sets a risk (stop-loss) smaller than the noise, then the probability is high that simple market fluctuation will cause trade exit. The volatility, then, becomes the minimum risk one should take on a trade.

Practically, volatility has another important use. It is a sensitive measure of market congestion. Balanced markets (congestion) have low volatility. Trending markets have larger volatilities. The 90 day average volatility for T-bonds (as of March 13, 2002) is 8.3. Minimum is 3.9 and maximum is 15.5. Large volatility increases rarely precede the start of a trend, although sometimes the general market tenor, as measured by volatilty, rises prior to directional movement. Volatility is more of a coincident indicator, which helps to uncover trend end. Volatility offers a tip-off to market intentions.


We define the (AMT) volatility as the average range of the half-hour time periods of a Market Profile. these are:

Volatility is another valid way to check markets for congestion. As reference point for market condition, volatility adds to the visual measures.

Volatility calculations are tied to the timeframe. If a different timeframe is selected (say 15 minute bars) the volatility will be unique to that timeframe. However, the only valid volatility is the one associated with the appropriate timeframe, the timeframe that best reflects the time delays inherent in the market. That timeframe is thirty minutes in the data in this report.

Market demand interpretation aided by the volatility. BAL is 5 day balance as per the Overlay Demand Curve. ULIM and LLIM are the Overlay balance limits. VTY is the half-hour bar average volatility for the day.

Start with the Balance as of close Jan 28.
Jan 29, breakout on down side alerts for start of trend.
Close of Jan 29: Price lower, volatility at 546 is up 80 percent.
Interpretation: volatility implies demand is still present.
Close of Jan 30: Trend bottomed out at 108075. Closed higher.
Interpretation: short timeframe trend is over. Higher volatility
is not directional and is disregarded.

Start with the Balance as of close Feb 4.
Feb 5, breakout on down side, volatility up 30 percent, price lower.
Interpretation: short timeframe trend is probably still in place.
Feb 6, price moves down slightly, volatility is only 27 percent above entry.
Interpretation: demand or trader interest is not growing.
Feb 7, price continues down, volatility is down to 22 precent above entry.
Interpretation: demand continues to decay.
Feb 8, local bottom reached at 107550, close is higher, market in balance.
Interpretation: trend is over.

Start with the Balance as of close Feb 8.
Feb 11, breakout on the upside, close at breakout price, volatility lower.
Interpretation: breakout not supported by demand increase.
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