Applying "Squishy Problems" to Energy to Help You Profit

Applying “Squishy Problems” to Energy to Help You Profit

by | published June 7th, 2019

My new Σ Algorithm trading system has already produced a huge 1,100% profit during its initial application, and then five triple-digit winners and a 44.7% gain in the last five weeks for my trading services.

And today, I’d like to take you through one aspect of my new trading system designed to confront the most vexing element in energy investment decisions.

My Σ Algorithm currently provides a usable read in about 30% of cases to which I have applied calculations, although that may improve as the system is refined.

Still, this is never going to be a one-size-fits-all approach to trading. It has instead become a very nice cherry-picking tool. After all, this is what any selection process needs to be.

The system is deliberately eclectic in sourcing the mathematical basis for its approach, and has been in various stages of beta testing for almost two years. As I have noted on several occasions, it is innovation (applying something that exists in an entirely new way) rather than invention (coming up with something that itself is entirely new) that has resulted in most of human advances.

And it’s the same thing this time around…

Squishy Problems Require Squishy Solutions

Having had a bit of an eclectic life myself, the system borrows (and then revises) formulae from quantum equilibrium theory, implied volatility curves, random probability distribution, standard deviation calculations, the five major “Greek” variables applied in serialized options trading, and a few other eccentric places.

One of these variables considers what a famous mentor of mine called “squishy problems” – those roadblocks that are more human than statistical. They are difficult square pegs that don’t fit very well into round holes, and also are not susceptible to easy quantification and make prediction more difficult.

Central to my Σ Algorithm calculations are six distinct Key Sequencing Triggers (KSTs). The degree to which all of these point in the same direction constitutes the strength of an investment move recommendation.

One KST is what I call the political risk coefficient, or PRC.

When it comes to assessing those factors impacting on energy prices and investment selection, this may be the most vexing element of all… especially when considering how geopolitics impacts on the energy sector.

This element owes its origin to an approach I came up with during my almost thirty years in intelligence work.

You cannot always determine what the other side will do in any given situation. There is what they should do, given rationale alternatives, and there is what they actually end up doing.

Often those two are hardly the same thing.

Nonetheless, the range of what is likely is effectively constrained by the four “Ps”: Policy practices and preferences; Prior decision making; Personality; and Practical possibilities (choices that are doable). Each of these has several alternative scenarios.

All of this is hardly any longer a theoretical exercise. What is covered by PRC has become the single greatest wild card in assessing global energy trends.

Now, it used to be that analysts would factor out the major underlying market trends and be done with it.

This is no longer the case.

How Geopolitics Makes Algorithmic Waves

Geopolitics, and upon occasion even the dynamics of domestic politics, has taken over the driver’s seat in many energy determinations

Nonetheless, the four Ps still provide a read on possible outcomes, tempered by the likelihood of any given choice being made. Now here is something on which I can crunch numbers.

Without revealing the “family jewels,” there are three major ingredients contributing to how PRC is determined.

First, I determine the “standard situation.” This is the normal responses and actions usually applied in a given environment by a government, company, or decision maker when confronted with policy impediments foisted by “other parties.”

This is defined as a “ground zero.” Actually, it is given the value of 0.

Second, a series of deviations from the “norm” are determined, each given a standard probability weight of occurrence.

Third, the likelihood of each deviation from ground zero is estimated an applied against the findings of the other five KST results. This provides an estimation of the degree to which PRC is: (1) moving in tandem with other factors; and (2) is providing a readable indicator.

If the overall image of what results looks familiar, it is.

It comprises a revised version of a standard deviation curve. During my presentation at the recent Black Diamond Conference, I outlined it this way:


There is an empirical rule about such things. Over time, data falls into a standard normal pattern. Usually, 68% of results fall into the first standard deviation from the center, 95% within the initial two, and 99.7% within the first three.

It gets interesting when the distribution distorts, where more than expected is found further out from 0.

This is how I presented that phenomenon during my Black Diamond briefing:

Now, what the Σ Algorithm system has noticed is this…

Why My Degree in Theoretical Physics Comes in Handy

While occurrences beyond three standard deviations are rare in normal applications, when fat tails take place in calculating a PRC, they tend to signal a direction rather than simply provide a statistical result.

A major issue in applying this to making investment choices involves how to place what are “squishy” elements into a mathematical statement.

Here, I borrowed from my early time in theoretical physics.

There are necessary primary elements in determining the location, mass, or movement of a sub-atomic particle. These are essentially unknowable as a finite quantity but can nonetheless be estimated by injecting a series of arbitrary ranges to delimit random outliers.

The exercise ends up with particle qualities like “color,” “charm,” and “spin.” These limit how far the range of possible occurrences can be by limiting the state in which the particle can exist at any given time. But they don’t exist as such. They are artificial yardsticks applied from and residing completely outside what is being examined.

In short, random exercises are conducted effectively to limit random results.

Applying this approach has not been successful in most statistical cases. However, it has been of some use in determining PRC impact in the energy sector. That has allowed me to apply numbers to the “squishy” matter of political impact in energy.

At least upon occasion. And that may still be the most exciting part of the Σ Algorithm.

I will have more information on my process and calculations at a later date. In the meantime, if you’d like a chance to reap the benefits yourself, just click here to learn how to get access to my recommendations derived from my Σ Algorithm.

I’ll see you there.



Please Note: Kent cannot respond to your comments and questions directly. But he can address them in future alerts... so keep an eye on your inbox. If you have a question about your subscription, please email us directly at

  1. No comments yet.
  1. No trackbacks yet.