(Disclaimer:  This post is strictly for educational purposes only.  Any trades presented by the narrator are not recommendations nor will be held liable for any trades one makes in regards to the strategy presented.  The trading platform used is “thinkorswim” and the excel spreadsheet is from Apache Open Source.)

In this post I present three video segments of the VXX JAN 15 option chain that I traded for a scalp profit.

Second is an insight to my “black box” equity spreadsheet model (alpha) where I bought a Long Call in the JAN 15 option chain at the $110 Strike.  The entry premium was $1.45, and I closed my position at $3.03 before the last hour (Witching Hour) going into the close.


The first video introduces the first morning move of the VXX and how heavy volume trading can “skew” the spreadsheet calibrations for the Reduced Cost Basis Covered Call formulary.   Also you’ll see the TLT chart correlative play.

To watch the video on YouTube click here.

My second segment shows the VXX intraday pull back after making a clean +6% move north.

To watch the video on YouTube click here.

In the final and third segment, I present the typical “pull back” – or what is called a “mean reversion”.

To watch the video on YouTube click here.

If you took note of the Front and Back month “Put” signal, then you’d have been busy with a Long Put setup going into the last two days.  That’s the beauty of this model.  I’ve calibrated the equation bins to the randomness of price formation, both with the underlying and option chain, so you get a robust signal.

AAPL – “Idem” + “Potence” (same+power) + Fuzzified Probability

Steve Jobs’ key function built into the Apple’s operating system software (or is it Macintosh?) iOS is a fixed point first-order function, unlike Bill Gate’s Windows, floating point.  This illustrates my focus on the development of advanced mathematical finance instead of financial theoretical models.  Derived from applied mathematics, I extend the mathematical or numerical models built upon price formation of the day trading session – Hilbert’s proof of theory coupled with Doyne Farmer’s theory.   The goal with derivatives in the “Q” World (not James Bond’s deadly endgame gadgetry scientific mastermind) is determining “fair price”, of a given underlying security so that the option premium price is not skewed.  The equation p = f(p) applies to higher-order functions: f takes another function p.  This is a fixed-point combination that is idempotent in relationship to calibrating the various “bins” that contain (solution set equations) to the inputs and parameters of the model.

For example: g(f) = p, where p = f(p) – which I pointed out in the previous paragraph.

All of this boils down to one thing – the problem of points (nodes or bins) needed to find the expected (future) value of the instrument.

Probability theory is taking center stage over financial indicators, is an analysis of random phenomena, kind of a Ripley’s Believe or Not fad.  One must argue this point to expect their model to evolve over time in a random fashion.  Living in a “random” world is assigning probability to each measurable subset of possible outcomes of a randomness procedure or statistical inference that is purely a “sample” of the mean.

Given “continuous time” as complex system when delving into market analysis, probability distribution assigns a probability, thus it is a closed structure that operates solely on being “randomized” at every point or node.  I have worked on this methodological for quite sometime, finding it frustrating to be cobbled to probability of density functions.

This is a Darwinian process of growth and learning where upon I arrived at “hard science” with “IF-THEN” rules or Fuzzy Logic.  The magic is alarming when it comes to “expected value, expected return, expected robustness” with every investment instrument, when this approach is applied.

Thus the next step is developing “fuzzy associative matrices”; utilizing Zaden operators to build an awesome – game changing – commission free trading app for the iPhone.

Probability Theory and Fuzzy Logic deal with is “uncertainty”; both address the issue of minimum and maximum truth.  But time and again the outcome of Probability Theory or statistical probabilities not as idempotent!

If you want to be exposed to the world of Fuzzified applied mathematics, watch my very brief “in play” AAPL options trade explanation on YouTube – click here.





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