The Anatomy of a Trading System

The Anatomy of a Trading System

by | published April 10th, 2019

About a month ago, I introduced a new trading algorithm designed to provide a “cherry picking” tool to identify movers in the energy sector before they spike.

This past weekend I discussed the structure of this mechanism at greater length with the sold-out audience at Money Map’s Black Diamond Conference in Delray Beach, Florida, along with providing the initial picks made for my Energy Inner Circle and Micro Energy Trader services.

The first results have been very positive, and I am currently running the regression analysis – actually a series of correlated multiple linear regressions – to select the next recommendations.

Now, in last month’s Oil & Energy Investor column, I discussed the general nature of algorithmic approaches and why they usually get the result wrong.

As currently used, failures are covered up by massive plays in split-second computer applications. They involve heavy reliance on placing multiple moves on the entire trading curve, “supported” by weighty options and derivatives to offset bets.

In short, these are well beyond anything an average retail investor can use. They do, however, artificially affect the market.

And here’s how…

The Consequences of the Big Red Button

Upon occasion, somebody pushes the wrong button.

The result is what has been labeled a “flash crash,” in which the value of an entire section of the market either collapses wildly or disappears altogether.

The current utilization is at best a “band aid” approach, patching over shortcomings by using highly speculative assumptions and unwarranted premises. In other words, a square-pegs-being-shoved-into-round-holes approach.

Risk is addressed only via (hopefully) hedged large volume trading moves offset by progressively larger computer trading models.

This entire process often disfigures what the market is genuinely telling us, in turn requiring yet another layer of derivative paper to level off the trading environment.

But there is something else of interest resulting from my research of these large algorithm plays over the past 18 months:

They are wrong over 55% of the time!

Of course, this doesn’t stop them from being used. The size of most of these plays merely shrouds what is being done.

Now, I noted here last month what a real algorithmic approach is designed to do…

An Algorithmic Roadmap

An algorithm is a procedural road map, a set of rules or ways of proceeding that streamlines calculating exercises intent on solving problems.

But this is not magic, and the way in which the approach is used currently in large-block energy investment strategies has become suspect.

Allow me to state a categorical principle I have learned from inserting such approaches into a wide range of energy sector, academic, policy, and intelligence usages over the last half century:

No approach is a “one size fits all;” there are plenty of ways in which the environment effects the calculations.

It is crucial that a trading approach recognize where the algorithm applies and where it does not.

That brings us to what I have determined after over a year of designing my approach.

There are three overriding requirements – it must be employed as one tool among others; it must overcome volatility spikes; and it must compensate for random uncertainty.

Phase One

As for the first, what I have called the “Predictive Σ [i.e., Sigma] Algorithm” is designed for use as part of an integrated approach. The following is the slide I used in my Black Diamond presentation last weekend to describe my coordinated investment strategy.

I have used Phase One in one form or another for some time.

It involves traditional investment picks arising from market dynamics, upon occasion augmented by ways to compensate for volatility, selected exchange-traded funds (ETFs) and exchange-traded notes (ETNs) to lessen sector risk, highly selective options plays, and an attention to the difference between invention and innovation.

This last consideration is the most important single lesson I have learned in life.

Phase Two

Invention is coming up with something new. It is very rare.

Innovation is applying something that already exists in a new and different way. Innovation has resulted in most human advancement.

Innovation literally saved my life in the field during my intelligence days (think of it as MacGyver without commercial breaks or a sound track).

It allowed me to identify new ways of authenticating highly complex interconnections in scholarly endeavors, and has brought about usage of approaches found in one discipline to problems encountered in another.

The Predictive Σ Algorithm is the latest example.

It involves calculations through five series (called “universes”) of multiple equations. The first two are “innovated” from approaches to establish ranges in options trading, the third from finance modeling, and the last two are revised versions of equations I used in quantum equilibrium theory during my early days in theoretical physics.


Because including volatility spikes and random uncertainty (the two crucial elements I mentioned earlier) are not part of what the current algorithmic domain is good at doing. And, among other considerations, quantum mechanics is a view of the physical universe that is entirely predicated on random occurrence.

Anyway, it is important to recognize that no approach can succeed in the abstract.

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Phase Two (as referenced in the slide above) involves networking my global energy connections for early signaling of “raw material” developments having an impact on the sector and the identification of six “Key Sequencing Triggers (KSTs)” that comprise the base variables for the algorithm.

The algorithm does not apply in all cases, however.

It remains a part of the larger investment approach noted in the slide above. It is designed to provide selections, but not to drive the overall process. The math should never control the outcome, something current computer trading applications do not understand.

There is a great deal of detail I will avoid at this point. But keep in mind two important matters that I will occasionally come back to in further Oil & Energy Investor editions: fat tails and kurtosis.

The Results Speak for Themselves

The above slide is how I introduced the discussion of these two matters (and others) at the Black Diamond Conference.

There is a basic rule that 99.7% of all data points will end up within three standard deviations (or sigma) for the mean. When more of these than normal extend into a sigma 4 or beyond (that is, moving further right or left on the bell-shaped curve), these are fat tails.

These have been happening more frequently in the energy sector. In fact, while a sigma 7 is considered by many as statistically impossible, parts of the energy sector in the past several years have provided us with two 9s!

Early identification here makes for huge profit potential. For example, my Energy Inner Circle members banked an 1100% profit on an options play because of it.

This results because of intense volatility in the underlying equities.

And that brings us to the second point.

Among certain energy plays, volatility is increasing in shorter cycles. This period of relative calm interrupted by significant volatility spikes is what statisticians call kurtosis. Harnessing these spikes guarantees some very nice returns.

My readers in both Energy Inner Circle and Micro Energy Trader have collected multiple triple-digit wins over the past three months because of these swings.

(If you’d like to join them, and learn how you could profit from this strategy, just click here or here.)

As we move forward, my entire integrated trading strategy will benefit from the Predictive Σ Algorithm and the overall approach will be subject to continuous refinement.

Remember, this is an extension of the trading I’ve already been recommending – and enhancement, if you will.

Yet the equations themselves do not apply in all cases.

Since January 14, 216 applications have been run, all six of the KSTs provided workable correlations in 45, broader sector applications emerged in 8, resulting in four initial test investment applications beginning on March 4. All moved up at least 20% in value during the first week of trading and are now up by at least 30%.

The latest formal “Σ Algorithm” selection was released to my readers in Energy Inner Circle this morning. That is the fifth (three to Energy Inner Circle, two to Micro Energy Trader). They are all performing nicely.

But this is only the start of the complexity of the algorithm.

There is much more on the horizon.

So stay tuned for that.



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