Auction Market Value Theory & Analytics

Thanks for the reply, yes, I clearly understand the concept as you explain it now that I know what a TPO and POC are. I appreciate you taking the time. I can imagine that the indi that was written the purpose everyone thought was "broken".

“Auction Market Theory & Analysis” is about analyzing the “frequency distributions” at

In AMVT, TPO’s (That price occurred) are used as a “frequency distribution”. Rather than the standard distribution function (Gaussian). “Auction Market Theory” is the analysis of the frequency of prices being “used.”

This comes from Don Jones at Cisco-futures from his work as scientist in Physics as aero space engineer studying Radio waves of Jupiter. He found that distribution function was a poor fit to the measured data. Producing many errors in the data.

He found that this is true of the “market” as well. Market analysis is much the same as mis-analyzing Jupiter's noise source, in his work.
“(60 or 70 percent of the time) the market data seen is a poor fit to a 'Normal' bell shaped distribution of market activity. In such case, value and value change measurements are unreliable information”.

By analyzing the “frequency” of prices “used”, you can eliminate the poor fit of the measured data, caused by the normal distribution. This is why AMVT has nothing to do with Normal distribution function. (Gaussian).

Donald L. Jones
February 11, 2014
http://www.cisco-futures.com/meetdonjones.html
MS Aerospace Engr (~1963)

First graduate course: Mathematical Statistics. A great help in data reduction; but more valuable was the knowledge that much of the time a posited distribution function was a poor fit to the measured data. In such case the conclusions are unreliable. The reason that there is so much use of standard distribution functions is that these functions often have some known limited/approximation solutions and maybe, with some stretching and pulling, one can apply those special solutions to a market at hand. The danger is that the application's data may not be a proper fit to the assumed distribution function and hence the answer is in error (for the purposes of an analysis). For instance, there are many similarities between an apple and a peach. Both grow on trees, have a round form, a rind, etc. As a snack either an apple or peach may fill the need perfectly. However, they are not equivalent for the person who does not like the taste of peaches.

An Actual Case of a Research Program Ending up With an Invalid Conclusion
A measure of intermittent radio noise from the planet Jupiter pointed to transmitting centers on the body of the planet that rotated with the planet. The cross-correlated measurements looked good, evidence that Jupiter although covered by clouds could be a solid just like earth, Mars, Venus, etc.

However subsequent studies showed the transmitting centers to be outside the planet body, in it's gaseous ionosphere. This was a case where a cross correlation fitting identified locations of transmission that were not correct. The driving force,is the physics generated data, the radio signals. We misunderstood the source. Our cross corellated results gave the wrong answer even though the mathematics were done correctly.

Market analysis using charts and/or moving averages, etc., is much the same as mis-analyzing Jupiter's noise source: the analysis must be done right. We have price and volume that generates the chart's support and resistance graphics, but what we project mostly does not occur because we do not thoroughly understand markets. Markets are complex and driven by feedback. You may observe a five tick move up and view that as strength. What you cannot know is that there is a massive seller whose sell stop (price) is a few ticks above your position. Your trade is a latent loser, just waiting for the market to move up a few ticks.

DJ first Independent Scientific Job: Cross_Correlation Methodology for Locating Jupiter Radio Sources
This was an out-of-hours effort (my time) using the NBS IBM 650 computer our group administered. The observations each occurred at specific times and we knew the the approximate rotational parameters of the planet from Jupiter's great red spot and assumed that was connected to the solid body. A rotation rate that correlated well with the observed radio signals was found. Only later did we learn that the signals were generated far from the surface of the planet. This was a case where the measured data and statistics used were valid but one basic assumption was incorrect. So the solution was invalid. Financial analytics is rife with such problems. People always want to know what the market is going to do (basic question on the financial shows: Where do you think the market is going?, right?).

Conclusion: The complexity of markets often requires a 'gaming' approach to trading. You take all the real data you can find and despite limitations, you make the best analysis possible. When dealing with science or markets you simply use 'best efforts' to uncover the facts. The more facts you learn, the deeper your understanding and the less likely you are to be a victim of misdirection.

If you rely on assumptions/beliefs such as "the market is going up" you may be right, but that is of little help if you do not know when or how much. To win you must 'be like the market' at the proper time(s). Markets change, sometimes very fast. You must attempt to validate your basic assumptions as often possible. You want to know the odds and how the odds are evolving. Successful trading is a contnual, evolution, exploration of market facts and behavior. Just as in a poker game: you do not control the cards you are dealt, or how your opponent plays his, but you can control how you play yours.

In market data analyses we often find statistical analysis being misapplied to price, volume and time data. For instance, the most used mathematical tool in market data analysis is the moving average we mentioned before. Overlooked by most is the simple fact (before noted)that performing a mathematical operation, like moving averages, may well change the distribution.

A glaring case is the analysis of futures data in terms of a bell shaped price-time curve, i.e. the Market Profile. To fit the normal distribution the price-time data must show a symmetrical bell curve. But most futures profiles are not symmetrical or have more than one peak, etc. Assuming a normal distribution and going ahead with trading analysis builds a decision tree on a shaky foundation.

Another ubiquitous case is the use of moving averages to develop 'indicators'. The problem arises immediately, as soon as you do the moving average. For example, a 6 point average of, say 30 minute bars for a day, implicitly assumes that there is an underlying 30 minute wave imbedded in the data. Probably no such wave exists, but now your data is smoothed/changed, probably destroying much of whatever information did exist before the smoothing.

If your price-time data shows a well-defined bell distribution it is likely a good measure of value (for the time period studied). All too often (60 or 70 percent of the time) the market data seen is a poor fit to a 'Normal' bell shaped distribution of market activity. In such case, value and value change measurements are unreliable information (potentially costly to a trader!).

PHD Aero Engr Sci earned (1966): Thesis published in a professional journal.

CISCO Futures

Steidlmayer Market Profiles, 1985, 1991

By Donald L. Jones, CISCO Futures
June 10, 2010
http://www.cisco-futures.com/steidlm...t_profile.html

J Peter Steidlmayer, a member of the Chicago Board of Trade, was a pit trader in the grain and interest rate markets for many years. An astute observer of market action, he saw in market activity a reflection of the Normal, or Gaussian, distribution he had learned about in college. This well known process has wide application (the distribution of men's and women's heights, shoe sizes, weights, and many other situations). The Normal distribution has a bell shape and is defined by two measures, the mean and the standard deviation. Both of these variables can be found from measured data. A discussion of the Normal distribution is available in any elementary statistics book.

Steidlmayer found that a plot of price as a function of time generated a normal looking Normal curve. He then drew a number of conclusions from the plot (reference points). This work was published as CBOT Market Profile 1985 and CBOT Market Profile 1991. These publications plus several books by Steidlmayer and co-authors were generlly understood to usher in a new value based analysis methodology for futures trading.

Simply stated, the measured peak volume price of a set of market data (occurance of market prices over time) was equated to the peak of a Normal distribution and considered the center of value. The central 70 percent of the volume around the peak (the standard deviation) is defined as Value. So, measuring the cleared volume and prices leads to Point of Control (peak value) and value area (+/- 1 standard deviation).

Reference Points from the Previous Day
A profile chart for the previous day can be read to give a variety market facts. There is an Initial Balance (high and low of the first hour of trading), Range Extension (price above and below the Initial Balance), high and low volume areas, the shape of the chart (Day Type), today's Open, High, Low and Close relative to yesterday, Point of Control and Value Area relative to yesterday, etc, etc. Link to VISUAL GRAPHIC Background

Of most importance to traders was Steidlmayer's ability to divine from yesterday's profiles who was driving a particular market (the long timeframe traders such as commercial grain dealers) and the short or day timeframe traders (such as the public). The chart patterns were presumably not easy to master. MP 1985 suggested that it would take six months to a year.

It was also suggested that the pattern of a currently developing day could be read to predict the direction and behavior of the market for the rest of the day. This was done by finding the 'day type' early on in the day.


Reservations on Holistic type Profile Analysis
The masterful reading of profile chart patterns in MP 1985 and MP 1991 likely displays much of the way Steidlmayer actually traded. He had a vast background of experience, personal contact in the pit with the market drivers (commercials) plus the charting that he had developed to support his analysis. It is a fair question as to whether a neophyte non-member could apply the charting part and become a successful trader.

A. The author, as an active swing trader in 1985, is one of those who became interested in profiles because of their day time-frame emphasis. Every swing trade starts as a day trade and ends as a day trade; so our idea was that profile could 'close the loop' so to speak, on our trading. We began our analysis of the methodology by investigating the basic concept: profiles are good examples of experimental normal distributions. That turns out to be only partly true. Sometimes the symmetry is good, more often, it is poor. We found that value from the profile varied widely day-to-day even in cases where the market was balancing. This led us to develop a 'multi-day profile', the Overlay Demand Curve. Overlays proved much better in recognizing stable markets. For a first cut, one needs a three day Overlay.

B. The structure of the market has changed. The pits of yesteryear are mostly gone. The personal contact Steidlmayer had with other traders and especially the commercials is no longer available. The commercials used to be about 15 percent of the market. Their power came from their ability to dominate a market when they wished. It appears that now the CTI 2 commercial category has about double the volume but much less effect on open interest. A big trade that results in no change of open interest is likely a day trade. Hence, the commercial category appears to trade more like the public.

C. The speed and range of the market is vastly increased over 1985 days. A simple test of the market range in the wheat market was maxed at 70 cents from 1985 to 2000 and 180 from 2000 to 20010. The Dow was similarly 70 and 144. Trading volume is up hugely. The markets of today are far from the leisurely ones of yesteryear. It affects the trader as higher risk compounded by having less time to recognize current conditions.

D. Much of Profiles 1985 and 1991 relied on the Liquidity Data Bank, especially the elements relying on the CTI2 commercial data. This was by design in part, because CBOT was the only exchange releasing LDB data (later, around 1989 the CME licensed the format from the CBOT). Since no other exchange, the peak cleared volume price (Point of Control) and the 70% volume range was not available for markets such as crude. At CISCO we developed the Meta-Profile with TPO's substituted for cleared volume. Steidlmayer's Market Logic School rejected the Meta-Profile concept, avowing that cleared volume was required. With the CME-CBOT merger the LDB fell into limbo--it is apparently available now at exessive cost.

E. The holistic, chart pattern reading of MP 1985 and 1991 relied on starting the day off right. Early in the day, as early as possible, a trader was to ascertain what type of day was in store. Was it neutral (a balance day), a trend day or what? Profile, 1985, listed three day types. Profile, 1991 had four. Steidlmayer on Markets, 1989, lists six. MOM, 1990, has nine. Steidlmayer on Markets, 2003, lists five. Markets in Profile, 2007, does not have an index item for Day Type!

In the words of Mind Over Markets (MOM), pg 19:
...Market Profile as a whole tends to fall into readable patterns in the day timeframe, determined by the degree of involvement of the other timeframe participant. These patterns, when properly identified, can increase the day trader's success, as well as provide information regarding what the market is trying to do in the longer term.

If the trader is expected to recognize the 'Day Type' early on in the day and the primary writers in the area do not agree with each other or even themselves on the number of basic day types that exist; how is the poor trader to make a decision. A fair question is how many day types are there really? Our guess is as many as you want to put on your list. This is a problem with pattern recognition, since only at end-of-day will you be sure.

G. The absence of Pits, the change in structure and speed of markets, the irrelevance of the Liquidity Data Bank and the difficulty with day types are all problems that make the original holistic pattern recognition paradigm of Market Profile problematic today.

A Better Way
Pattern recognition is a wonderful way to trade if only we could make it work! But we cannot because markets are complex and the same pattern may have different meanings on different days (read about Auction Market Value Theory in the CISCO References pages). There are a number of Reference Points, maybe 30, that measure market parameters (example: Point of Control, volatility, trade facilitation factor, relative location of Initial Balances from day to day, etc.). Go to the Value Analytics pages for a complete list. These reference points have feedback and cross talk because of their complex interactions. For instance, an increase in volatility might be thought to presage continuation (of a trend up, or a trend down). But what of an increase of volatility accompanied by a decrease in volume?

CISCO Futures

Market Waves are NOT Cycles
Donald L. Jones
Copyright CISCO Futures March 25, 2005

A hope of many technical analysts is that markets have a cyclic component that can be used for predictive purposes. Belief in an underlying periodicity is implicit in moving average smoothing, since the moving average is expected to act as a filter. The same is true of oscillators, where two moving averages of different time frames are subtracted to find differences. The fact that oscillator differences often move smoothly and change fast in highly directional markets is taken as evidence that the resulting curves provide information that can be traded. An underlying but unspoken assumption of virtually all technical analysis is that market data is well conditioned (small errors in the data produce small errors in the result). In fact, as will be shown below, the fundamental assumption of essentially equidistant peaks in a set of market data is incorrect, and thus market data is poorly conditioned for most of technical analysis.

It is theoretically true (Fourier analysis) that several waves of varying amplitudes and periods can be combined to provide a display that looks like market flow (with rises, falls and periods of low amplitude). Unfortunately, it is also true that market flow can and does come from a complex, self-regulating process (aggregate decisions of a large number of traders). This process cannot be described by any known mathematical distribution. Specifically ruled out is the fourier process, the market is not (so far as anyone can tell) a combination of sine waves.

Many elements of normal life are cyclic; the 24 hour day, seasons, weather, ice ages and other natural phemonena. Some economic phenomena such as booms and busts are thought to follow some sort of wave, such as the Kondratieff 30 to 60 year wave. In markets, much has been made of the Elliott five wave up and three wave down proposition. While phemonena such a Elliott waves become apparent after the fact, proponents seem unable to agree on the present. Even if you could tell which leg of an Elliott wave you are in, that knowledge would be hard to use in day-to-day trading where your outlook is rarely more than just a few minutes or days.

Markets always display wave-like motion. Prices move up and down with some regularity, describing local highs and lows. This movement reflects the changes in the way the composite of all traders, their feedback, value the market. While clearer on longer term charts, the local highs and lows describe periods that vary from wave to wave. Prediction of the next high or low cannot be made in any reliable way. Incidentally, prediction is not a requirement of successful trading

Because the source of market motion cannot be tied to any known distribution, a market surely cannot be described by a set of sine waves or helped in any real analytical sense by smoothing.

Some analysts describe market waves as cycles, imputing a regularity not supported by the data itself. Analyzing market waves to obtain any predictability is not simple and may well be impossible, due to wave irregularity. Some waves can be very short, comprising just a day or two from top to bottom, others may take twenty days to find a bottom. While market waves for predicting the future seem to be impossible to divine, it is possible to look to the past to study the obvious wave motion in past data, with the aim of better understanding market

Full Study
http://www.cisco-futures.com/market_...tech_anal.html

Market Waves are NOT Cycles

It took me several years to figure that out. I trade with a volatility adjusted grid now. Sometimes I regret how much time I wasted chasing ghosts. Trading is really quite simple. We make it hard.

from Value Based Power Trading...........

Hi,

Regarding the TPO count and imbalance in a balanced day by counting TPOs above and below POC, I have seen Dalton say that overnight inventory is short if there is more TPOs below settle and long if above settle. Why is this difference in logic when applied to MP during a normal session, where TPOs below POC are considered buy and TPOs above POC are considered sell. Please enlighten me.

Also, I see that TPOs constituting buying tails and selling tails are not counted in a balanced day MP when counting imbalance of buyers and sellers. Am I right?


Thanks,
Prem.

He speaks and assumes that everyone has read MOM. When you said “seen him say”, does that mean you saw a video clip?

Without reading the book, a lot of what is said is taken out of way out of context………… When you read the book he’s a bond trader. He trades markets that contain cleared volume, and a daily open & close. MP is exclusive & copy written to the CME. This includes its concepts & methodology, as well.

In a cleared volume market, the close is called a “settlement”. So from his perspective, his market has a daily open & close. Any trading after the “settlement” (close), from his perspective, is considered “Overnight Inventory”. TPO’s aren’t volume or considered any type of inventory. After the close, and when the market re-opens, if price trades higher than the close, the market is long. If prices are lower than the close, the market is short. It doesn’t have anything to do with tpo count.

An auction‘s not considered over until the last buyer has bought or the last seller has sold. TPO’s aren’t volume, its frequency of prices “used” over time. TPO tails (single prints), allow you to see where prices are no longer being used. These tell us “where” the Auction (distr.) to the upside or downside, begins & ends. We know where the last buyer or seller entered the market.

Unless you’re trading using the CME LDB volume report, you have no idea of the imbalance of buyers & sellers. The volume report used in CME MP analysis is just like the COT report, but on a daily basis.

Unless you buy this info, it is unavailable. “Cisco” was the first and only CME approved vendor of the LDB volume report for many years. The CME had excessively increased the price cost of the report and Cisco, rather than pass the cost on to its customers it decided to discontinue its use. Cisco researched and tested, and couldn’t find any statistical evidence that the data provided by the LDB volume report provided any edge, or insight to be found by its use in MP analysis.

That’s why the TPO’s methodology is used, for both cleared volume and tick only based markets (FX), which removes our dependence on the LDB volume report for MP & meta profile analysis. Cisco was only a handful of approved vendors by the CME that provided CME volume data & stopped providing the LDB Volume Report many years ago.

Last time I checked, you could only get MP LDB volume data directly from the CME for $1,800 a month.

Being that I trade FX, and there is no “cleared volume” data to tell me which class of traders are in control (commercials, public, etc.). The concept of “Volume” and how it is used in MP can’t be applied to FX. You actually need to “read” how MP volume is used, to realize the context in which it is used, before you realize it’s not applicable to FX.

Understanding how the different classes of traders behave (operate) is important when analyzing tpo’s (frequency of prices “used”) when you have no real buyer or seller info available. Tick volume is of no use as you can’t “assign” direction to it (buy vol or sell vol). Tick volume can’t tell you if there is more buyer or seller participation. Again, the “why” the use of tpo’s, because real buyer & seller data isn’t (cleared) available from the CME until the next day. By breaking the distribution into “units” (TPO) creates a measurable way to quantify, the data within the auction (distrib.)

LTF participants trade value. If more prices are being “used” below, this is where the LTF buyer operates (does business) and will be active. More prices being “used” below, tells you that you have more LTF buyer participation than LTF seller participation.

Mind over markets, doesn’t show you how MP “volume” is actually used until pg. 135 (hard copy) (pg.149 pdf.) It’s impossible to use MP volume analysis as described in any of the MP books and apply it to FX. (don't take my word, read the book yourself, and you will be able to form your own opinion) you'll find the same info in Steidlymayers book chapter7 pg. 97,as well as the CME MP handbook.

CME MP Volume analysis of the different Classes of traders (commercial, public, etc.) tells you who’s in control. If you’ve read MOM it shows you how to do this with the use of tpo analysis, before it shows you how to use the volume data……..

You are right, that the single prints are not used, we already know where the last buyer or seller entered they’ve taken sides, what’s in between will determine which side balance should tip…………………..

"He speaks and assumes that everyone has read MOM. When you said “seen him say”, does that mean you saw a video clip?"

Yes, from videos of Dalton from Vimeo.

Thanks for the detailed reply, very helpful and insightful too. Looking forward to your posts on using MP analysis in Forex.

Auction Market Theory
Donald L. Jones
CISCO Futures 2002

Auction Markets
Most of the world's markets are auction based, e.g. futures, options, debt and equities, derivatives, etc. The Auction market dynamic is created by the participants. Daily price range is determined through negotiation between the traders. As price is negotiated, some prices are accepted by the marketplace and create heavy volume. Rejected prices, the highs and lows, and sometimes opens and closes, are seldom traded and generate little volume. By trading many times throughout a day, accepted prices identify value.

Despite the ubiquity of auction markets, there is no general auction market theory. Capital Market Theory (CAPM) is a special case for long term, equilibrium markets. CAPM is at base an investment theory. It assumes a logical investor with a particular level of risk tolerance. A portfolio approach provides diversification. What is not provided is guidance for the trading upon entering, changing and exiting the portfolio. The trading part of the investment process is ignored.

Observation and experience shows that the trading part of portfolio management can be critical to overall performance. For convenience, assume a one-hundred percent rollover of a portfolio and a trading gain of two percent of the average value on the net rollover. With an average portfolio gain of eight percent, trading gain represents a twenty-five percent enhancement of return. A two percent trading range is not unusual in the S&P 500 index, while the nasdaq index range is much larger. CAPM makes the point that index funds are superior because of the low rollover and hence low costs. But even index funds have some rollover that might be traded rather than just rolled. Every trade has a risk, expenses and offers an opportunity. There are good and bad times to buy and sell. The trading function can conceivably be a profit center instead of merely a cost. Trading risk is not treated in the risk tolerance measure that initially sets the portfolio. Identifying and managing the trading timeframe is the substance of Auction Market Theory (AMT). Futures approach the ideal in auction markets. Auction market analysis developed on a futures exchange. Most explanations in the text will use futures because of convenience.

J. P. Steidlmayer , a member of the Chicago Board of Trade, first described and interpreted the day structure of auction markets. An extension of the day market concept was added by Jones to cover market condition, the environment in which day trading takes place.

Sharpe, in the foreword to the reprinted reference 1, makes the point that CAPM uses little empirical material (mercifully, he says, because of the short half-life of empirical studies). Most of science starts empirically; collecting and sorting data to derive basic principles. AMT is based on empirical studies, in part because the underlying mathematical description would be a non-linear, non-homogenous differential equation with non-constant coefficients, and basically insoluble. Many fields of science suffer the same defect, but airplanes fly even though their wings are not infinitely long. Likewise, auction markets can be understood phenomenologically through empirical evidence. An assumption is that the probability of making better trading decisions improves with market understanding. This is a general principle running through most human endeavors. Successful decisions in most arenas come from data coupled with a good understanding of the business at hand. Thus, auction market theory is normative in effect, providing information for decision making.

Very Long Timeframe/Very Short Timeframe Auctions
An equity market can be idealized as a long timeframe, equilibrium market, as in Capital Market Theory. Such an equilibrium market is an investment medium. At the same time, equities trade every day in the short timeframe non-equilibrium limit. Other markets such as commodity futures spend virtually their entire lives in a non-equilibrium situation. Non-equilibrium markets can thus be characterized as trading media. Auction markets as a whole are a continuum, stretching from the shortest, non-equilibrim limit to the long timeframe equilibrium limit. However, the non-equilibrium short timeframe limit theory developed here will be termed Auction Market Theory (AMT) as contrasted with the long limit CAPM. ………

For those who want to jump ahead
Article in its entirety http://www.cisco-futures.com/amt_theory.html

Auction Market Theory (AMT); 3 Parts, a Brief Review

Part 1. Development of AMT: The Theory
The primary aim of Auction Market Theory is to identify the characteristics of short timeframe non-eqilibrium auction markets and to present a coherent description of their behavior. Achieving this goal will place market analysis on a quasi-quantitative basis, removing the mystique and misinformation surrounding market activity. An allied aim is to develop a procedure to utilize current market knowledge as a guide to future (trading) action. The approach is empirical. Observables are identified, cataloged and interpreted to provide a structurally sound market description that applies to real world situations. AMT analyses will identify the salient elements of a market; including value, market condition, risk, volatility and other items in a market's structure. These market elements will, in turn, be used to generate a strategy for trading.

AMT (theory) develops the principles governing short timeframe market phenomena. Often, prediction of future events qualifies a theory. Financial markets are replete with market predictors and market predictions. It is an axiom of Auction Market Theory that future prices are not predictable. Current market condition is found from AMT principles. Market condition is the state of the market, e.g. local equilibrium or balance, local trend or transitions between the two. The state or condition is time dependent in that one timeframe may be in one state (say a market is balanced in a twelve day view, while trending by a four day measure). AMT analysis shows that changes to the current condition can be the basis for market decisions. A strategy developed subject to the event of change from one market condition to another is non-predictive (normative). While predictions of future price are not made, logical conclusions from market behavior are. Incidentally, CAPM operates much the same way, picking portfolio candidates on the basis of past behavior

Part 2. Calculation of Reward and Risk
The aim of trading is the reward, the gain from a trade. Reward (plus or minus) results from the trading process. Auction Market Theory does not specify trading procedures; rather trading strategies. It will be shown that maximum potential reward in a day trade depends only on the entry point. In certain situations (e.g. balanced markets) entry points are readily identifiable. Reward maxima for a trading event can then be found.

Reward alone, while significant, is better understood in terms of it's relation to the risk involved. The rarely known reward-to-risk ratio is important to appropriate money and risk management. A risk estimate can be found from a simplified standard trading model that uses entry prices and risk from an AMT analysis process. The maximum time frame of a trade is from trade entry to market close. The maximum possible reward is the largest price excursion from entry to close. This is defined as the Potential of the trade. Potential for a trade in such a case, is defined by one variable, the entry point. Potential in day trading plays somewhat the same role as the index in CAPM, i.e. as a marker for success. In AMT, Potential measures opportunity.

Part 3. Standards: Potential and Opportunity
Standards are desirable but rare as a basis for evaluating models and markets. If an investor's stock portfolio value declines while the market index is declining more, the investment is described in positive terms (it beat the market). In non-equilibrium trading there are no indexes. A loss is absolute, a gain is absolute. But, losses can carry different connotations. A loss in a market offering little opportunity is quite different from a loss in one with much opportunity. Potential is the arbiter, it sets the standard. If one trades a market that consistently offers low Potential, one should change markets. If one does poorly in markets that consistently offer substantial opportunity, one must examine one's trading methodology. Auction market principles use 'best trading' risk and reward to risk ratios to catalog markets.

CAPM is Not a Guide for AMT
Although the focus of AMT is on short timeframe trading markets, the long-term (CAPM) is important psychologically. The public tends to think of short timeframe trading in terms of the more familiar longer term investing. The two are quite different. Speculation, betting on risks and opportunities, in the day timeframe has a totally different risk and reward structure from investing. The long term upward trend of equities rewards a buy and hold strategy. The same strategy in speculation is fatal. A trader/investor who is not aware of the difference is doomed.

Auction Market Structure: Beginnings of AMT
Short timeframe auction markets, although not an an equilibrium system, do spend time in quasi-equilibrium, balanced trading. As a non-equilibrium system, AMT is difficult to describe in a closed mathematical formulation. Much of the analysis of near term markets is necessarily empirical. A benefit is that AMT, in practice, is not obscured by mathematical formalism. The average trader can readily understand and follow the principles. That is not true of CAPM with it's efficient frontier.

AMT is applicable to trading strategy in a wide variety of instruments; securities, options, interest rates, futures contracts and other double ended markets . Regardless of the market, the timeframe for a given trade is specific to that trade. It ranges from seconds to minutes to many days, determined by the market itself, in it's relationship to value and value change. A certain percentage of losses are inevitable in a trading environment. Leverage may exist, as in futures or margin trading in equities or more generally in hedge funds. Consequently, unlike a long term investor, a trader should risk much less than 100% of an account's capital on each trade. Performance is measured by appreciation alone, but it can be gauged against an ideal, the Potential.

Beginnings of Auction Market Analysis: Steidlmayer Original Decisions
J.P. Steidlmayer started as a floor trader at the Chicago Board of Trade in 1963. Over time he developed the concepts from which evolved the Market Profile volume charts and an auction market description as well . The seven market concepts below are a brief overview of his work.

1) All the publications he searched attempted to predict the market. They failed. He surmised that prediction of the future should not be a goal.

2) Graham and Dodd's book Security Analysis showed that value could be found in the present tense. He projected this result to his trading.

3) He posited a basic unit of market activity, now known as the TPO (price and time location of trading activity). TPO's are measured in half-hour segments of the trading day. Steidlmayer selected the half-hour time frame because that seemed to be the time it took for new information to get represented into price change.

4) He found that grouping a day's set of basic units forms a bell shaped distribution of price - volume over a day, which he named a Market Profile (tm). The bell shape called to mind the 'normal distribution' so widely applied elsewhere to bring "order out of chaos".

5) Price-over-time measures value. He saw that relative value at a price is equal to the sum of the basic units (TPO's) at that price.

6) Traders/investors seek a fair price. Markets auction 'too high' and 'too low' in the search for fair prices. Markets accept fair prices with enhanced volume and reject unfair prices by way of low volume. The price range is reached by negotiation among the traders.

7) His experience showed that there are two sorts of market, '(balanced) day trading' where value is static and the market moves around a static fair price; and 'longer term' where value is moving (sometimes called 'trend days').

Super post, MsVega!

Hi,

Thanks for your posts and your insight on rejected open and close.


Prem

@ jmn5611 & prem,

Thanxs again, for the ongoing support……. But I cant take any credit for any kind of "insight". There is only but a few resources of research available on the subject. My job is to make sure I'm familiar and well versed in those few resources. My knowledge base consists of the same resources that have been shared here and have been available to everyone by a small group of members here, who use AMVT as a way of understanding how auction markets work & that the indicator is just a tool. But it's encouraging to know there are a few actually interested in the "Theory", rather than just the indicator itself.

Market Observables: Exchanges
Organized financial auction markets have been present in the United States since 1792. As markets expanded, rules were implemented and the exchange structure, with memberships (seats), took form. Exchanges formed the arena for the growth of the well-defined auction markets that exist today. Steidlmayer's pioneering work occurred within the exchange structure. In developing Auction Market Theory, a somewhat different path is taken from the Steidlmayer work. Here the market observables, self evident facts clear to all, forms the starting point.

Significant Market Observables
1) Possibly the first rules in 1792 set the meeting place, time and trading hours. These rules are, in the case of electronic markets, now being stretched to include non-floor, non-exchange hours. Futures exchanges set margins based on volatility (risk).
2) Auction prices are arrived at by negotiation.
3) Some prices are accepted by the market and show considerable trading activity during the market day.
4) Other prices, often the highs and lows, are quickly rejected, trading little.
5) A market may be accumulating (balancing) or distributing (moving). That is, markets spend time in balance and sometimes trend.
6) Some participants seek quick gains, e.g. day traders, while others trade a longer term strategies (position/swing traders).
7) Some days many traders are active in a market, other days few trade. Some days a market will have high volume, some days not.
8) Some markets usually have wide daily trading ranges, others are noted for consistently small ranges.
9) The heaviest trading often occurs in the opening and closing periods.
10) Markets display little day to day serial correlation.
11) Markets cycle from balance to trend and back to balance.
12) An auction timeframe can be long or short (e.g. the market has been in balance for 15 days, or the market broke out of balance and has been trending for 3 hours).
13) Members on the floor have different functions (scalpers, commercials, specialists, trading for the public, etc.).
14) Electronic platform markets clear, or complete trades faster than the floor.