Elliott Wave Principle Re-visited With Computer Trading

I write software. I'm pretty good at it. My strength lies not in streamlined code, but in algorithms. Any code monkey can write code. Most coders today could NOT write an object sorter using recursion and recursion is kiddie scripting in some of the functions that I code.

The real aim of the game is to not to get paid for writing software, but to write software that makes money. A quant is exactly that:

quant

   [kwahnt]

noun

Business Slang . an expert in quantitative analysis.

So, one of the ways to write software to make money, is to develop trading software for stocks, bonds, derivatives and Forex. Everyone has their own proprietary technical analysis trading software but they all start with Weighted Moving Averages and all sorts of statistical charting and apparent correlations that you give signals when to buy and sell.

The great granddaddy of them all is the Elliott Wave Principle. If you don't know what the Elliott Wave Principle is, you can read about it HERE.

A typical Elliott Wave pattern stock price looks like this:

Elliott developed his ideas over 60 some years ago, and I idly wondered if Elliott Wave Patterns were still valid in this day and age of computer trading. Would computer trades at split seconds skew an Elliott Wave pattern if and when they occur? (The reason why I say "if" is that determining the milestones of the Elliott Wave pattern is a very subjective thing. Many technical analysts try to debunk the principle and its adherents swear by it.)

So the burning question is and was: Is there something to the Elliott Wave, and how has computerized trading changed the Elliott Wave, if at all?

To do that, I needed some data, and not just large time domain general data. I wanted data points demarcated by seconds, not days or hours. After all computers trade by the second. So I captured the real live second by second trading of Facebook on its opening IPO where volumes were shattered but the price remained flat.

Here is a sample of that data:

To prevent subjective interpretation, I wrote a computer object -- a model of the wave that was magnitude agnostic (meaning that I was just searching for the pattern and didn't care about the price). One of the biggest problems with the Elliott Wave is interpretation and where does one begin to count for the wave pattern. I let the computer do that for me. If the signal (serial stock price changes) didn't fit the pattern, I advanced to the next data point, and tried again. I have to say that the results were pretty dismal.

Then it struck me -- I needed an "ish" engine on this. I have previously discussed "ish" on this blog. It is a form of fuzzy logic that can ignore the odd outlier whilst still identifying the pattern. I used the ish engine to categorize wildly divergent answer schemes of health surveys in Nigeria. Once I incorporated the ish engine into my model, I started to get many more hits where I did identify the Elliott Wave pattern.

To answer the question of how computerized trading was affecting the analysis principle, I had to collect models of the deviation of the Elliot Wave. The first thing that the ish engine picked up, was that computerized trading injected many more outliers that were in fact intermediate steps in the pattern. From a macro perspective, the Elliott Wave still sort-of resembled the pattern, but on a micro level, the fractal pattern was different, and like fractals, this was carried over onto the larger pattern.

Here is a graphic illustration of the outliers where intermediate steps are introduced into the wave pattern:

Instead of going from 1 to 2, now there is a 1A step inserted into the pattern. This was when I tested for 1 deviation per step.

Then I allowed the computer to test for two deviations per step. Now one can see two outliers as the wave progresses from 2 to 2A to 2B to 3. This is so simple to do when you have a computer object that models the wave and allows for ish or deviation. One can run many many epochs (data sessions) over and over again and change the parameters each time.

If one thinks of the wave as a series of vectors, then one begins to see how a direction vector can be incorporated into the ish engine or fuzzy logic. Let's suppose that Talib is right (and I am sure that he is) and there is a lot more randomness than one suspects. My posit was that computerized trading is responsible for generating the randomness.

When I altered the wave model to accommodate a deviation in the direction of the vector component in the wave, the computer came up with a model that was topless:

So, I now had models that the computer had saved. The next step was to assign Bayesian Probabilities to each model. The first injection of Bayesian probability was for predictive effect. Based on where I was at the moment, what magnitude and direction of the price vector would happen next? Then I determine the probability of which overall model that it will fit. From there one can make larger price determinations. Incidentally, no-fit is also an outcome in this model, where there simply isn't a pattern.

What's the next step? The next step is to introduce artificial intelligence multi-layer perceptrons as a fall-through model to analyze the price signal in real time. Then the perceptrons keep correcting themselves based on real time outcomes.

Can this updated algorithm score alpha and make money on stocks, futures, derivatives and Forex? I don't know yet, but I am too busy earning a living to take this to the next step. Are there any fund managers out there willing to fund a research project with the updated Elliott Wave coupled to fuzzy logic, artificial intelligence and Bayesian Inference?

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The End of the Line for the Business Intelligence Cube?

I was deep in conversation with a tech-savvy epidemiologist at a dinner party. He is a physician who is the head of an NGO (non-government organization) with offices in various countries on a few continents. He happened to mention that he had over a million record sets that needed data-mining in a very specific way.

His organization had ascertained that the easiest way to convey epidemic data to policy makers was via a 'weather map' where the geographic areas that were in the greatest danger would progress from green to yellow to red when a full blown epidemic developed. To that end they created a data-mining tool for reports. However there was one major flaw with the tool. It could only show results after the fact and didn't perform predictions. Predictions are important for epidemiologists.

I suggested that what his data mining gizmo needed was a Bayesian Inference Engine. Bayesian Inference principles are used for logical inference and prediction on imperfect data sets. A Bayesian operation takes historical data, and calculates the probabilities of a number of events of happening when their predecessor events have taken place. Bayesian inference is a tool in the arsenal of artificial intelligence. It is the perfect tool for running predictions on evolving data. In an epidemic situation, data evolves rapidly. One cannot wait until it is all said and done to run the analysis.

I described to my medical friend how one would make a real time inference engine. Before any row of data is inserted into a database, an inference factory instantiates an inference object. The inference object is used to either look up the probabilistic meta-data for the permutations and combinations of the columns in the row of data (it examines each data dimension) and recalculates the inferential probability with the input of the new data. The output is filtered and deposited into a results table.

Then the thought struck me, that if this function was built into the database engine, there wouldn't be a lot of need for business intelligence cubes that require vast amounts of ETL (Extract Transfer and Load) data dimensioning, data marts and obscure SQL statements the size of a novel.

All of the data would be digested in real time, and mined and refined in one shot. The inferential factory in the database engine would calculate in real time on every data insert, and various filters would be defined for reporting.

With the exabytes and exabytes of data that we are generating, this could be one way of handling the tsunami of data without being overwhelmed by it. And IBM would be awfully sorry that they bought Cognos Business Intelligence Cube software.

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Who Was At The Computer -- Solving a Whodunnit

I was idly watching some of the Casey Anthony murder trial being streamed on the Internet. She is charged with brutally disposing of her bothersome two-year-old child who was impinging on her party life.

One of the expert witnesses was an ex-police officer turned geek who wrote the program called "Cache Back". What the program does, is recover the browser cache of the web history after it has been deleted. He discovered that the browsing history contained terms like "chloroform" and how to kill people.

The defense lawyer stands up and tells the computer expert that there is no way that he could tell who was at the keyboard when the queries were made. The computer expert had to agree. Well, if they had geekazoids like me, there is away to state the probability of who was sitting at the computer.

Consider the following equation:

This equation is the basis of Bayesian inference. It is one of the keystones of data analysis and artificial intelligence. A quick explanation of the terms is as follows:

• H represents a specific hypothesis, which may or may not be some null hypothesis.
• E represents the evidence that has been observed.
• P(H) is called the prior probability of H that was inferred before new evidence became available.
• P(E | H) is called the conditional probability of seeing the evidence E if the hypothesis H happens to be true. It is also called a likelihood function when it is considered as a function of H for fixed E.
• P(E) is called the marginal probability of E: the a priori probability of witnessing the new evidence E under all possible hypotheses.

The theory behind this concept is the idea of querencia. When people log onto a computer, they usually follow a core of usual, habitual persistent URLs. They check their email, Twitter and Facebook page, and then perhaps check the weather or news or such.

So in this methodology to determine who was sitting behind the computer for a particular history, one examines the whole history. One finds the sequences where there is no doubt of the supposed user in question. This could be determined by the URL of a Facebook page or email.

Then one assembles a statistical model of the URL web pages visited, and calculate the variance from the Venn set of URLs as well as the deviation from the usual pattern.

By calculating probabilities from the browsing model, one can then take an unidentified set and using Bayesian inference, determine whether that user had the probability of being the unidentified user.

This is by no means a smoking gun of proof, but it can add one more piece to a circumstantial change of evidence. It can answer the question of "Who was using the computer" with a degree of probability.

This would also be a useful system in a corporate environment to determine what users had breached company policy in visiting banned websites.

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