Intuition Amplifiers 2

Interesting, so you are using splines on the price as well as indicator and testing the effects of time variables. Are you using a dominant cycle on the indicator... or multiple cycles and using the movement from conflict to harmony in several cycles to ascertain the probability of the strength of the next move? I have found this multiple conflict - harmony cycle to be the best indicator of true strength.

 

Many of the market parameters that carry very valuable information about market’s Sentiment and Perception including the signals that one can extract from the Order Book are highly erratic, non-periodic processes. They may contain very abrupt moves as well as slow and smooth transitions between different time frames. In order to dissect those types of processes a technique called “Spectrum Analysis” was invented in the beginning of the19th century. The Idea behind Spectrum Analysis is a separation of variables, which reduces problems expressed in two or more dimensions to a family of one-dimensional problems. This technique, for example, is the basis of tomography, a widely used tool of contemporary medicine. The best known implementation of this method is The Fourier Transform - a mathematical operation with many applications in physics and engineering that expresses a mathematical function of time as a function of frequency, known as its Frequency Spectrum. For example, the transform of day light could be presented as a mathematical representation of the magnitudes and phases of the individual colors that make it up. When we pass the day light through a prism we essentially utilize the Fourier transform to extract the rainbow of colors that form it. The most significant advantage of using algorithms and devices that are based on the Fourier Transform is their ability to visually represent relatively complex observations in a very compact and easy to comprehend format.
Consider the following. If we are listening to a choir that consists of many male and female singers it is virtually impossible to detect the singers that have high pitch voice and who sings in a much lower octave. Even if we recorded the sound of this choir and looked at it using an oscilloscope we wouldn’t be able to discern the voices of individual singers from one another. However, if we passed this sound through a series of filters each tuned to a single specific frequency the result would be drastically different. By measuring the signal strength of each filter we could easily assess the relative impact of the voices with different pitches on to the overall sound of the choir. Knowing the average “loudness” of a singer we then can accurately estimate how many singers sing in any particular octave. More so, if we plot the filters’ output signals using a bar graph where each bar height corresponds to the signal strength of a particular frequency filter we could create one of the most powerful visual signal analysis tools – Fourier Transform Spectrums. Although these spectrums are widely used in hundreds of engineering applications they all have the same weakness. As it turns out, Fourier Transform Spectrums could only be efficiently used if the processes they analyze have a highly periodic character. In other words, they are only efficient if applied to signals like sound, mechanical vibrations, heart beat etc. that consist of periodical (sine wave like) components. Unfortunately, most of the market information including price fluctuations, Order Book bid/ask ratios, Volatility etc. is not necessarily highly periodical. It of course limits the use of Fourier Transform Spectrums for efficient visualization of market signals.
Realizing this problem back in late 90s I came up with an idea to use natural cubic splines instead of Fourier filters in order to form new types of analytical tools that did not exist before – Spline Spectrums. At their heart is a set of splines that have different elasticity. Some of them are very easy to “bend” and some are very difficult. The easily bendable, flexible splines could “follow” the points in a data set very closely and “catch” even the slightest changes in the data flow. At the same time the stiff, rigid splines only bend slightly at each data point thus “ignoring” the most abrupt changes in the data set. The difference between end points of each spline could be plotted as a bar graph that represents a Spline Spectrum.
It turns out the Spline Spectrums are as powerful for market data analysis as Fourier filters for sounds and electromagnetic waves analysis. The perception they create significantly enhances our intuition about possible future changes in the market conditions. We have tested the Spline Spectrums with hundreds of traders in the past 12 years.
Those spectrums are one of the major parts of any of my intuition amplifiers.

 

a) Can be controlled doesn't mean markets are usually controlled by one person. I doubt any one person can control ES or furthermore EURUSD these days also. And persons with power can also act as part of a crowd. Very much so.

b) If something resembles each other, it's not necessarily the same thing. Many charts look similarly, but show absolutely different events driven by absolutely different forces. Market is not random, by the way Mr. Gallacher who's book you recommended me a while ago confirms that clearly.

 

Ehlers (of MESA) found the same problem with Fourier and also adapted it, however Ehlers either seeks to determine the dominant cycle or when the market is out of phase. I found the out-of-phase cycle conflict to be of as much value as the appearance of a dominant cycle as the evolution confirms each other. I like your daylight illustration for this morphing effect.

I look forward to finding out more

 

What errant foolishness! A simple gedanken experiment suffices to prove it. I give you a data stream consisting of five variables. I tell you that you will be rich beyond your wildest imaginings if you can predict the value of the fourth variable based on the behavior of all five. You quickly deduce that the first variable is an intermittent measure of elapsed time because it is monotonically increasing and has date-like and clock-like qualities. You observe that the second, third and fourth variables are quantized and tightly coupled. The second and third variables are usually separated by one to a few quantum levels. The fourth variable almost always falls at or in between them. The fifth variable is always an integer, has a minimum value of 1, a large but finite maximum value, and tends to be correlated in amplitude with increased data rates and bursts of volatility in variables two, three and four.

Easy-peasy. You perform statistical and cross correlation and auto correlation analyses. You observe early in your analysis that the all important variable four might be adequately characterized by four or five states. It's clearly a deterministic time series. Its statistics seem to be stationary. You develop a Kalman filter, or a differential equation, or whatever your favorite modelling tool is. You start making predictions. You're going to be rich. Then a shitstorm of volatility appears in the data which takes the number of states to infinity. You have just gotten a hard lesson in the mechanistic fallacy of applying classical analysis to complex chaotic systems. You might just as well try to figure out why your wife is pissed off at you.

 

Ehlers...not Elders

 

Time and timing plays a central role in the relationships between all living things. People’s activities are governed by cycles of time, which when taken together determine individual and social behavior. These cycles, otherwise known as actions or behaviors, require a level of skill that can only be acquired after a long period of training before they can become useful, while others seem to develop spontaneously. Why? The answer is spontaneous synchronization.
Here is an example. Suppose you are in a hockey arena with thousands of fans. Those fans react to the game’s flow and shout at will. When a small group of the most active cheering fans start to rhythmically chant something like “Go, Canada, Go!” the whole arena begins to cheer in unison, causing the otherwise unrecognizable noise of cheering to become clearly recognizable to anybody within a mile of the arena. The conversion of the crowd’s noise to a loud and recognizable cheer depends on the timing of the chanting or cheering. While participating in this chanting people within the arena do not even realize that their heart begins beating faster due to their surroundings. The cells in our body are quite literally synchronizing to the external stimuli. The emotional character of the cheer can accelerate or decelerate our heartbeat. We are not aware of the process, but the cells themselves manage to change coherently, almost in unison.
Just a few milliseconds seconds after a person’s favorite team scores the crowd starts to cheer loudly. Initially the cheer may be incoherent, but the wish to cheer and deliver a message of appreciation to the home team transforms the otherwise incoherent scream into a perfectly synchronized chant “Go, Canada, Go!” despite the different location of individuals inside the arena. This example illustrates Spontaneous Synchronization, one of the most captivating cooperative phenomena in nature. Spontaneous Synchronization is observed in biological, chemical, physical, and relevant to this discussion, social systems such as stock markets.
The relevance of synchronization in the Stock Market has been studied for decades if not centuries, but until now it has not been fully understood.
To further illustrate the concept consider the behavior of fireflies. To facilitate courtship, fireflies flash their hind end while other fireflies seem to respond and ultimately synchronize flashing. Similarly, in the stock market Spontaneous Synchronization occurs resulting in dramatic price fluctuations that cannot be explained by other rational market models.
Spontaneous Synchronization observed in complex systems can suddenly change the system’s behavior from a disordered state to an ordered one. These sudden changes are known as phase transitions and occur in a whole range of systems — think, for example, of a group of chaotically moving birds suddenly coming together to form a "V" shape, or locusts simultaneously alighting on a field of valuable crops. Fish spontaneously assemble large schools and small birds form swarms to protect themselves from predators. The behavior of these kinds of systems is remarkably predictive of the behavior of stock market participants.

Understanding the mathematics of how, and under what circumstances, entities can synchronize provided us with a starting point for designing our way of looking at markets.

 

Thanks. As friends can attest, only one typo is outstanding for me.

 

no because they have different goals so they act in conflict with each other rather than in concert. surf

PS-- unless you believe its all a cabal-- in which case, I cant argue with you.

 

While you are certainly correct---the exceptions make the rule in the financial market.

I would counter you by saying, George Soros ( just an example) wakes up angry and randomly decides to "punish" the yen by short selling it--- the yen plunges, this is unpredictable and random-- AND it happens all the time