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Bill Duryea founded the IOAMT with the goal of teaching traders how to read data, identify important information and act on that knowledge. The IOAMT maintains a historical database of market generated data to generate its proprietary statistical calculations from which its daily trading levels are derived.
The Broad Benchmark U.S. Index, the S&P 500 resumed its13-month rally by closing the week at a new 52 week high.
Friday, April 19th, witnessed the equity markets declined after the SEC charged Goldman Sachs with fraud.
However, this week market participants were focused on the 85 components of the S&P 500 that reported first quarter earnings.
Most exceeded expectations. However, the number that beat revenue expectations was impressive. The number of companies that missed on their top line were few and only modestly below expectations.
Broad base positive bias carried over to the small-cap stocks, as evidenced by the Russell 2000 outperforming the S&P 500 with gains 3.8% versus 2.1%.
Cyclical sectors advanced this week, led by energy (+4.2%) and consumer discretionary (+4.1%), while noncyclical sectors underperformed, most notably health care (-0.9%).
First quarter earnings will continue to dominate the headlines next week. There will also be two major economic events on calendar next week. The major event will be the release of the FOMC rate decision and policy directive on Wednesday: followed by the first quarter GDP report released on Friday.
The U.S. Treasury auctions will resume following a two-week break. There will be an $11 billion 5-year TIPS auction on Monday, followed by $118 billion of 2-, 5- and 7-year Notes during the middle of the week.
Technical Perceptive
The April 16th decline found support on Monday, as demand enters the market at 1180. The 1180 reference points goes back to April 8th rally off 1170, where price pull-back during J-period. On late April 9th the S&P June futures found during B-period support at 1183.
On Monday, buying interested established a new base of support for this current phase of market development, as responsive buying was consistently present during the mid-day session probe to 1180. J-period witnessed a short covering rally through Monday’s range with price trading up to 1196 at the close.
On Tuesday; the S&P traded above the previous day’s high, re-tested the April 16th high at 1206 and closed the session at the high of the day.
Wednesday, the S&P traded up to 1207 at the open, and traded down to 1195, before closing back within the previous day range.
The secondary reaction to the ret-test of resistance at 1207, which was observed Wednesday, continued during Thursday session. The S&P opened below Wednesday’s settlement and sold down to 1186, modestly above Monday’s low.
The selling pressure pause at the low during remained of the opening range. There was no re-test of the of the A-period low. During H-period, the failure to make a lower low gave way to a short covering rally, which continued through M-period at which point the S&P traded up to the prior day’s high.
Friday, the S&P found minor support at 1201, the low of prior day’s settlement range. The price action consolidated between opening range: 1201 A-period low, 1210 B-period high. Late-in-the day, price broke-out above 1210 and traded up to 1213. The S&P June Futures close the 1212, a new 52 week high.
Technical Reference Points for June S&P Futures Contract
Up-side target for Q1 earnings season is 1221, the September 2008
Resistance
1213, April 25th High, June Futures, new 52 Week High [1217 CASH]
Support
1186, April 22nd Low
1180, April 19th Low
1171, April 8th Low
1161, March 31st Close
1156, near term support March 26 Daily Low
1148, Major Support March 22nd low, the FOMC Low
1136, March 15th Daily Low
If the ideas and concepts of auction market theory appeal to you and you would like more information, you are invited to visit our website at www.IOAMT.com
Consideration is given to theoretical bases and aspects of the practical application of a method of pattern recognition, which relies on the polynomial regression.
Pattern Recognition Methods
Characteristics are defined by the structure of the price action.
The categories are initially determined by observation and inspection of the data set, i.e. the time and sales record.
Labeling the specific properties of the market data make possible the classification of the self-similar characteristics within the pattern categories
Periods of horizontal l price movement, i.e. minimum of 3 to 5 Thirty minute sample sharing a common Mean are defined as micro intraday consolidation.
Periods of directional price movement, i.e. minimum of 3 to 5 Thirty minute sample where the slope of the localized mean is advancing or declining are denied as micro intraday trend.
Sharing a common mean
Neter of the program implementation of the method, which are defined on the bases of graphic patterns of symbols with known bounds. The correlation is made with characteristics of the known symbol recognition algorithms, such as neural networks and an algorithm of the comparison with reference patterns.
Polynomials Based on Pattern Recognition
Algorithms for polynomial regressions can be written using algebraic expression pattern recognition.
Pattern recognition of geometric patterns, i.e. point and figure charting, can be done in a straight forward way by comparing the corresponding vertices and edges.
Horizontal Price Action = Balance / Equilibrium
Directional Price Action
Retracement
Pattern Recognition Methods
Characteristics are defined by the structure of the price action.
Pattern Recognition Research and practical experience in observing market activity provide an objective bases upon which the two dominate phases of market activity can be defined.
Each pattern is a representation of the specific properties within the data set.
Pattern Classification is the first step in the Process
Graphical illustrations of examples of market activity provide the bases for classifying the general properties of the patterns and help make the different classification intuitively understandable.
The categories represent the identity of the corresponding patterns
Interpretation defines the pattern
The Representation Information is expressed using numbers and letters
Learning is the process of
The interpretation the data is the process by which the pattern is learned.
The Bayesian Methods of approximate inference algorithms can be developed around the major pattern and their minor variation and probabilistic techniques can be employed to define the degrees of certainly associated with anticipated market developments.
Market Generated data consists of a series of many numbers: The many numbers are price and volume. Volume equals trade activity. There are two stream of trade activity: buying interest and/or selling pressure.
The relationship between of a series of numbers, i.e. the trade activity between the buyers and sellers are the dependent and independent variables influencing price direction. Thus, market activity can be expressed mathematically as a polynomial regression.
A variable is value capable being changed. When applying polynomial regression analysis to market generated data the variables are: price at the bid and the offer.
The current location of price is the result of the last trade. If price changes from its current location, move higher or lower, the cause is attributed to influence of supply and demand entering the order flow: i.e. buying interest and/or selling pressure.
Price probes, pulls-back and retraces to previous areas of support and resistance. The process is referred to as price discovery.
The interpretation defines the pattern
The Bayesian Methods
A graphical model of the general framework of the data set
Describe and apply probabilistic techniques
The development of a range of approximates inferences algorithms
New models based on Kernels
Polynomial regression is a form of linear regression in which the relationship between the independent variable x and the dependent variable y is modeled as an nth order polynomial.
Polynomial regression fits a nonlinear relationship between the value of x and the corresponding conditional mean of y, denoted E(y|x), is used to describe nonlinear phenomena of price fluctuation.
Although polynomial regression fits a nonlinear model to the data, as a statistical estimation problem it is linear, in the sense that the regression function E(y|x) is linear in the unknown parameters that are estimated from the data. For this reason, polynomial regression is considered to be a special case of multiple linear regression.
Polynomial regression models are usually fit using the method of least squares. The least-squares method minimizes the variance of the unbiased estimators of the coefficients, under the conditions of the Gauss-Markov theorem.
The least-squares method was published in 1805 by Legendre and in 1809 by Gauss. The first design of an experiment for polynomial regression appeared in an 1815 paper of Gergonne[4][5].
Polynomial regression played an important role in the development of regression analysis, with a greater emphasis on issues of structure and inference.
More recently, the use of polynomial models has been complemented by other methods, with non-polynomial models having advantages for some classes of problems.
Gauss did not publish the method until 1809, when it appeared in volume two of his work on celestial mechanics, Theoria Motus Corporum Coelestium in sectionibus conicis solem ambientium. In 1829, Gauss was able to state that
The least-squares approach to regression analysis is optimal in the sense that in a linear model where the errors have a mean of zero, are uncorrelated, and have equal variances, the best linear unbiased estimator of the coefficients is the least-squares estimator.
This result is known as the Gauss-Markov theorem.
In statistics, regression analysis includes any techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables. More specifically, regression analysis helps us understand how the typical value of the dependent variable changes when any one of the independent variables is varied, while the other independent variables are held fixed.
Most commonly, regression analysis estimates the conditional expectation of the dependent variable given the independent variables - that is, the average value of the dependent variable when the independent variables are held fixed.
Less commonly, the focus is on a quartile, or other location parameter of the conditional distribution of the dependent variable given the independent variables. In all cases, the estimation target is a function of the independent variables called the regression function.
In regression analysis, it is also of interest to characterize the variation of the dependent variable around the regression function, which can be described by a probability distribution.
Regression analysis is widely used for prediction and forecasting, where its use has substantial overlap with the field of machine learning. Regression analysis is also used to understand which among the independent variables are related to the dependent variable, and to explore the forms of these relationships.
In restricted [defined] circumstances, regression analysis can be used to infer causal relationships between the independent and dependent variables.
A large body of techniques for carrying out regression analysis has been developed. Familiar methods such as linear regression and ordinary least squares regression are parametric, in that the regression function is defined in terms of a finite number of unknown parameters that are estimated from the data. Nonparametric regression refers to techniques that allow the regression function to lie in a specified set of functions, which may be infinite-dimensional.
The performance of regression analysis methods in practice depends on the form of the data-generating process, and how it relates to the regression approach being used. Since the true form of the data-generating process is not known, regression analysis depends to some extent on making assumptions about this process.
These assumptions are sometimes (but not always) testable if a large amount of data is available. Regression models for prediction are often useful even when the assumptions are moderately violated, although they may not perform optimally. However, in many applications, especially with small effects or questions of causality based on observational data, regression methods give misleading results.[1][2]
Regression analysis is a statistical tool for the investigation of relationships between variables. The purpose of investigation is to seek the causal effect of one variable upon another.
For example: The price of security declines, the assumption is that supply i.e. the number of sellers has exceeded the number of buyers.
The price of security advances, the assumption is that demand, i.e. the number of buyers has exceeded the number of sellers.
In both cases can the cause of the price change be determined in the underlying data?
To explore such issues, the investigator assembles data on the underlying variables of interest and employs regression to estimate the quantitative effect of the causal variables upon the variable that they influence.
The investigator also typically assesses the “statistical significance” of the estimated relationships, that is, the degree of confidence that the true relationship is close to the estimated relationship.
Regression techniques have long been central to the field of statistics.
In this lecture, I will provide an overview of the most basic
The natural dynamic of the dual auction process results in price moving in a non-linear progression.
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