Auction Market Value Theory & Analytics

Market Models

Value and Economic Fundamentalism
Possibly the earliest analytical approach to stock valuation is the work of Graham and Dodd, which seeks to find value from the discounted return on dividends. This fundamentalist approach can include other variables as well, such as the U.S. government economic reports, other government reports, etc.. As time passed it became understood that emotions played a role in investment decisions and should, in some way, be included. Also, discounting the dividends required a forecast of interest rates. In the modern world, that turns out to be difficult indeed (10, p18). More generally economic fundamentalists study market/economic variables and try to fit the pieces together, in an implicitly linear way; to find value. We would call this "true value" since it is what the asset is "really worth", something akin to the Graham & Dodd discounted dividends value. With true value in hand, successful fund growth would be assured by seeking out undervalued assets. Again, success is elusive.

Mathematical Fundamentalism
A mathematical fundamentalist goes a step past economic fundamentalism in that an attempt is made to not only identify the variables, but to mathematically catalog their inter-relationships. In most cases there are so many variables it is virtually impossible to know the inter-dependences. So the analysis is confined to a few 'major' variables. Even with less than a dozen variables, the mathematical description of the market would be a daunting, non linear, non homogeneous differential equation with non-constant coefficients. Interestingly, such an equation has the possibility of solution in the limit of a very quiet market. In that case, graphs of market variables will give straight lines (e.g. price versus weeks without rain for soybeans in their growing period). Capital Market Theory has had some success in the 'quiet markets' by assuming a market distribution function. Unfortunately, it is not the quiet markets in which the risk and potential return is significant. A general differential equation of the market is impossible to solve in closed form because the many variables are impossible to catalog along with their various interactions.

Market Models

Technical Analysis
Technical analysis is the graphing of market data and reading the charts for recurring patterns (e.g. a head and shoulders formation). It also includes mathematical manipulation of market data via moving averages, oscillators, etc. A more arcane part of the field reads significance into fibonacci series, elliott waves (cycle analysis) and even astrological data. Typically, one calculates or reads the graph to get "indicators" of market direction or intent. Since most of the charts can be computer drawn and the indicators can be listed, the user receives a trading path to follow. Unfortunately, the path is not well defined since the indicators give varied and often conflicting results.

These methods are available at reasonable cost from a number of sources. There are hundreds of books on technical analysis. Most new traders begin with access to technical analysis. Does it work? Probably not if the standard is trading success: the vast majority of new practitioners fail and the vast majority of current users (e.g. fund managers) do not break even relative to the applicable market index.

Current Market Theory, CAPM
Capital Market Theory (CAPM) has been in place at least since 1970 . The primary assumptions are that the market is stochastic and participants (investors) behave rationally. CAPM has been under attack almost since inception by behaviorial economists for some of it's assumptions. More recently, CAPM's reliance on the assumed gaussian distribution has come under possibly fatal attack by econophysicists , who show conclusively that the assumed gaussian distribution can wildly underestimate risk. Further, they find that no known distribution function that can fit the market data.

The assumption of stochasticity, i.e. the bell shaped curve, is a huge convenience for (CAPM) market analysis and can lead to elegant mathematical solutions, such as the Black Scholes option valuation formula. Unfortunately, it appears the the market is stochastic only so long as the risk is limited; those periods when the market is moving along on an even keel. When a market enters the non-gaussian phase (end of a bubble, dire economic news, etc.) risk may shoot up exponentially. In October 1987 the Dow Jones Index dropped over 30 percent in four days (Oct 14 - 19), a virtual impossibility in a stochastic market (one chance in 10 power 23 years, or longer than the world has existed ). More recently, the failure of Long Term Capital Markets to the tune of $1.3 trillion is laid, at least partially, to using a modified Black Scholes formulation to trade options.

Putting it all Together
A competent market analyst would be expected to be familiar with at least all four methodologies (economic and mathematical fundamentalism, technical analysis and CAPM). In the case of an equities fund manager, for example, it is assumed that the absolute best effort is exerted, since the survival of the enterprise is based on performance. Yet, it is a fact that actively traded funds do not do as well as index funds. Or, fund managers, on average, cannot even do as well as index funds. The statistics for futures traders seems to be reliable, with 90 to 95 percent of new traders failing, and is just as dismal. Options trading (Black Scholes) falls into line with the others, with the added onus of generating larger losses than any other method.

Methods and Results
Why are markets so hard to trade profitably? One would assume that as in any business there are capable practitioners and those who are not so good. Except for expenses, a trade should have a 50 - 50 chance of winning. Considering all the poorly qualified equities traders, one would expect the professionals, e.g. the fund managers, to win consistently against such under-qualified competetion. They do not appear to.

Possibly the wrong question is being asked. It may be the methodology. If a bridge fails, it asked "what was left out of the design". In the famous Tacoma Narrows bridge disaster it turned out that torsional shear (twisting) was omitted from the analysis and the bridge failed. It may be that fund managers and other traders are using the best information they have, but something is missing from their formulation. Could it be that their model is defective? Are they acting on theory rather than observation?

Probably the most used model in portfolio management is CAPM. But CAPM expects a high level of professionalism from the average investor, a level not found by empirical economists. Too, the assumed stochastic, gaussian formulation is proved wrong by the econophysicists. Possibly, CAPM offers too much and that on a shaky foundation. Why is CAPM still pre-eminent in MBA studies:?

In spite of the substantial and known drawbacks with CAPM, the theory continues to be taught in financial MBA programs. This is probably a result of there being no logical successor theory and CAPM is teachable. By "teachable" we mean:

1. It is coherent and complete
2. Mathematics are sophisticated enough to make MBA level students
work on the mechanics.
3. Tests can be made exact and thus easily gradeable.
4. The general ideas can give students a grasp of capital markets.
5. MBA students who are not planning to be traders (the majority).
thus have enough information to get by.
6. Portfolio balancing/efficient frontier and other portfolio
concepts are impressive and useful.
7. Mathematically oriented students can manipulate CAPM to gain
new uses, e.g. Black Scholes option pricing

A more prudent approach could be to start from first principles and understand the market itself before projecting solutions. In such a scientific, empirical approach, market understanding is placed on a firm, testable foundation and inferences can then be drawn from the behavior of the market variables. Whatever is found can then be traced back to first principles. That is the track of Auction Market Value Theory (AMVT). It is observational. Nothing is expected beyond the market's data. The data is complex since it is reporting a complex market. AMVT proposes to unravel, or at least live with, the complexity.

Section 2

Mathematical Auction Market Description: Econophysics

Econophysics groups are attempting to understand risk, to explain extreme events like crashes and basically to explain why markets behave as they do. Like CAPM, econophysics takes as observables, price and return (price change).

Modern Market Analysis, Complex Systems and Econophysics
Recent work by Johnson, Jeffries and Hui in the emerging field of Econophysics treats the financial markets as complex systems. They consider the problems of:

1) Markets as complicated dynamical systems that are continually
generating high-frequency data series.
2) How the stochastic assumption gives misleading answers to practical
problems such as minimizing risk, explaining extreme events such as
drawdowns and crashes and pricing derivatives.
3) Why financial markets behave as they do.
4) What can be done to minimize risk.

JJH (Johnson, Jeffries and Hui) on page 2, lists as goals, these practical questions:
1) When to buy.
2) When to sell.
3) Risk.
4) Predictability.
5) Crowd behavior.
6) Forecasting on basis of crowd behavior.
7) Forecasting time evolution of markets.

Although numbers 1 - 7 do scope the needs of traders, there is little in the book resolving the questions. This is not surprising. A trader with straight- forward answers to all seven could have untold riches. In all probability the very good traders of the world do have a good feel for the desired seven. In Section 3 on permitted practices, prediction is not-permitted. Still, a trader who masters items 1, 2, 3 and 5 is on the road to success.

Complex systems concepts have only recently begun to be applied to financial market analysis. Complex systems are generally nonlinear with feedback acting to continually adjust the system. There appears to be no hard and fast definition of a complex system.

Peters in 1999 listed these characteristics of a complex financial system:
1) The system has a purpose (e.g. to facilitate trading of say, soybean futures, stock market indexes, etc.)
2) The system is decentralized (many independent agents/traders)
3) Feedback occurs within the system (all agents observe the system and make changes in their behavior)
4) The system adapts to information from feedback (losers get out, winners increase holdings)
5) Adaptations are decentralized leading to innovation (each agent makes its own decisions)
6) Rules govern the system, rules can change or be changed (adapt) e.g. the movement from exchange floor to computer trading

Observations 1 - 6 describe auction markets such as stocks, interest rates, futures, derivatives and actually even markets as diverse as food and department stores. The financial markets are "double" sided auctions where a buyer at one moment may become a seller the next moment.

Items 2 - 5 show that feedback is diverse, affecting each agent or trader in a unique, personal way. The net effect of the feedback manifests itself in macrosocpic market parameters such as price movement and volume. But it is impossible and not even desirable to isolate a particular feedback element from any one of the agents; the useful observable is the net market change related to that element.

JJH (Johnson, Jeffries and Hui) defines complexity in financial markets somewhat differently from Peters.

1) Feedback: change contains an element of remembering.
2) Non-stationarity: the statistical distribution changes.
3) Many agents: traders, institutions interact in time-dependent ways.
4) Adaptation: agents adapt their behavior to improve their chances.
5) Evolution: agents behavior evolves thru feedback and adaptation and the system may not be in equilibrium. It can exhibit extreme behavior such as crashes.
6) Single realization: i.e. averages over time are not equal to averages over ensembles.
7) The market is an open system coupled to the environment: one cannot discriminate between exogenous (outside) and endogenous (inside) influences.

JJH analysis turns on measurement of price and return (p16).
Briefly, they assume:

1) Price as a function of time is the primary observable.
2) The assumptions of CAPM that price changes are independent and identically distributed are not borne out by observation.
3) CAPM volatility is not sufficient to classify risk.
4) A market price series' expected value depends in part on previous movement, the market has some memory. This helps explain drawdowns and crashes.
5) Crowd action plays a role in volatility.
6) The 'zero risk' in writing options under CAPM assumptions is not true in real markets.
7) Real price series differ from the random-walk model.
8) Trader's beliefs/actions can create patterns sometimes leading to crashes.
9) Markets are non-linear.
10) Internally and at all times, price moves much more and faster than rational expectations (of return) would predict.

The point is that both econophysists and Peters see the markets as complex systems with feedback causing change dynamically. The econophysicists are more mathematically oriented and propose a stricter analysis. They also propose a mode of attack, one using distribution functions. At this point it is not clear how a general market distribution function might be developed. Looking at the complex market from a differential equation standpoint, a descriptive equation would likely be nonlinear (feedback probably does not behave linearly), non-homogenous (variables most likely could not be separated, even if they could be defined) and the coefficients are non-constant (an increase of ten percent in the soybean crop would rarely translate exactly into a change in price of ten percent). It would be most unlikely that a complicated differential equation could be translated into a distribution function. One might think of a complex market in terms of the parable of the blind men and the elephant. An analyst explores all the various parts, ending up with enough pieces to make a slow moving elephant. However, with a complex market, the feedback can drive a change so that the next time the various parts end up making a fast moving tiger.

Section 3

Practices

A Practice is a proposed analysis method. If permitted by market theory, it is an approved or permitted practice, and may safely be used in market analysis and trading. A non-permitted practice is outside the bounds of theory and is not recommended for use in market analysis.

Practice 1: An example of a Non-permitted Practice, Efficient Markets:
It has been widely proposed that markets are efficient (any new information is immediately reflected in the price). This practice is invalidated by theory. Here's why: Markets are driven by feedback, therefore new information comes in at the feedback rate. Imagine that a given trader has new information and acts on it. That activity shows up on all trader's screens, say as an increase in price. Some buy on the news, some sell and some do nothing. It takes a different amount of time for each trader to decide. After some time (typically five to thirty minutes) the new information is assimilated and the market stops reacting to it. This shows market self organization at work, adjusting to the new situation. Feedback cannot take place instantly, so theory shows that efficient market concepts are invalid in auction markets. In plain english, even if a valid change in the value takes place instantly, the trader population cannot get the information, evaluate it and act on it instantly.

Practice 2: Congestion or Balance
The concept of congestion areas is often used by analysts as staging points for further movement. Is congestion a valid concept within market theory? The market communicates via feedback. The feedback can and does report markets with stable high - low ranges. That stability has been observed to extend from minutes to multiple days. Congestion, or balance, is observed from feedback, and thus balance is a permitted practice.

Practice 3: Value in a Balance
Define value as price over time (ref 23, p15), so long as price is stable. Since balance is a permitted practice, value in balances follows.

cont......

Practice 4: Trend
A trend is a period of generally rising or falling prices. Market feedback reflects such periods and so, trend is accepted, a permitted practice.

Practice 5: Value in a Trend
In Practice3 value is defined in a balance. In a trend, price is moving at an uncertain rate and direction in the very short time frame. Although changing price and varying timeframes are observables from the market feedback, that variation makes the definition of value uncertain in it's practice. Value is a macroscopic variable while market behavior parameters in a non-stable market are microscopic variables. Value in a trend cannot be read from feedback using macroscopic methods.

Practice 6: The Market Unit
A market unit is defined as the combination of a period of balance, followed by a period of imbalance and terminated by the start of a new balance. The imbalance includes the transition (to trend), the trend and the transition from trend back to balance. Is a market unit a permitted practice of market theory? Each part is permitted separately (balance and trend). The two parts are combined linearly. The compound practice is permitted because each of the parts are permitted and no manipulation is required.

Practice 6: The Market Unit

Discussion of DJ Market Unit Table
The fifteen units from January through November 15, 2005 show a wide variation in number of days in the units and the days in balance versus days of imbalance. Days in balance vary from 2 to 21, days of imbalance vary from 2 to 10. Days in the market units vary from 6 to 24. The market unit counts do not cluster about a central value as would be expected if the distribution were quasi-stochastic.

These wide variations infer that there is little commonality between units. The period to period variations suggest little market memory; it appears the feedback is relatively pure response of traders reacting to the market as they currently interpret it. There appears to be no trader bias toward a perceived market structure. That is, there is no bias toward an extended period of balance, followed by a short trend. While usually the imbalance is longer than the balance period, even that is not always the case. The conclusion is that balances will exist so long as the market deems (traders reacting to feedback) and likewise for the imbalances.

The lack of consistency in unit lengths calls into question the validity of moving averages as filters. Looking at the 'Net Unit Days' column, one would be hard pressed to select a characteristic length, as e.g. a 'Wilder' fourteen day period as espoused by so much of technical analysis. Smoothing is often a reason for averaging. A characteristic cycle length would be implicit in the process. The arbitrary lengths of the units and their component balances and imbalances appear to rule out cyclicality in the market above. There is a wave motion of sorts, but a cycle would be hard to find.

Practice 7: Predicted Prices
Predicting the future behavior of a market is most attractive to a trader. Such services are regularly advertised in market related publications. A prediction of the coming market track in a complex market suggests that market information in the feedback supports, is the base of, the prediction. However, the feedback cannot generally provide predictive information since the feedback depends on trader behavior. Trader behavior at a future time is not known in the current timeframe. That is, all traders see a market change at the same time. The trader response to that change and subsequent changes is conditioned on the path of each change in sequence. Since the later changes can only be effected on the basis of the next earlier change, the market behavior is uncertain from feedback to feedback. Prediction from feedback is not a permitted practice.

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 (not tick volume) 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, to the less restrictive Meta-Profile value and then to the Overlay Demand Curve (composite) 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) 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 . 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(don’t confuse with tick volume), 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. 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.

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.

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.

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......". The "sophisticated models" are no doubt the Black - Scholes methodology modified at least for volatility and probably other parameters as well.

Elements of Table 1

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.

Elements of Table 1

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** (VBPT p14). Much earlier, Jones identified a situation where the market acted in a predictable manner, wholly out of character for an efficient, stochastic distribution.

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 .

*The Misbehavior of Commodity Futures Prices, D.L. Jones, Commodities Magazine (now Futures Magazine), August 1974.

Summary: Teweles, Harlow and Stone, authors of The Commodity Futures Game, McGraw Hill, 1974, voiced the efficient market hypothesis in an article. We pointed out that 'noise' or random fluctuation makes measurement difficult. Furthermore, a single counter example can disprove a hypothesis. The disproof we cited is the article on persistence of trends in our 1973 publication.

*Persistence of Trends at Contract Highs and Lows, D.L. Jones, Commodities Magazine, (now Futures Magazine) Feb 1973.

Summary: This research on market continuation shows that a new life-of- contract high in a mature contract (60 days of trading) will be followed by a yet higher high within the next ten days 82% of the time. The probablility of two higher highs is 62% and three higher highs within the next ten days has a 46% probability. The same is true of new lows. This study refutes the efficient market hypothesis. If the future is (statistically) predictable as shown at new highs/lows, it is clear that the information driving the market is not instantly assimulated. This study is the first experimental refutation of the efficient market hypothesis known to the author.

**Steidlmayer on Markets, J.P. Steidlmayer, Wiley, 1989
**New Market Discoveries, J.P. Steidlmayer & H. Steidlmayer, KIRBMARN, 1990
**Steidlmayer on Markets, 2nd Ed. Steidlmayer & Hawkins, Wiley, 2003

Elements of Table 1

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'. 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.

Has anyone here ever used Steidlmeye's Capital Flow software? I'd be very interested to chat. I tried it for a very short time and I must be stupid or lazy but just couldn't grasp it. I am very VERY familiar with old school market profile so was keen to try and understand his latest evolution but failed. Anyone else have any experience with it?

P.S. The failing was totally on my part, I am not blaming the method or JPS. He is the most generous living legend IMHO.

I appreciate you taking time to post. I thought I did a trial through “aspen” years ago. I could be wrong, but I believe it was the only place at the time that had that charting. I don’t think they offered FX at the time either. I too found it difficult, and not applicable to the market I chose to trade. (at the time I was very new to trading and had no concept of trading outside of FX).

I resulted to working it out manually until a few years ago, where the “methodology” could be applied to an indicator for tick based markets (FX). But not having software I had to do an enormous amount of research to understand the math & formulas behind it, which naturally leads to the “theory” behind the MP indicator.

By his “latest evolution” are you referring to the “Time Variable Profiles” or the elusive “unicorn” called “Volume Strips”?.

Since you have a firm understanding of old school MP, you wouldn’t have a hard time at all grasping his latest concepts. I personally don’t use either, but I have a firm grasp on both methodologies. In the previous thread we had discussions about both. There are a few resources available. What resources on the topics have you looked at so far? If you don't mind, may I ask if you trade Futures or Spot FX?

Elements of Table 1

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. Later work used market activity, and added the caveat that the measured market had to be in balance.

As defined by Jones (VBPT, 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.