» Cycles - Part 2    » Cycles - Part 3    » Cycles - Part 4    » HOME

financial planning by a professional is necessary

trading commodities financial products

Cycles – Part Four - By Profit Max Trading

The subject of Cycles falls into two camps. You have those that base their theories on the Random Walk (or the “drunkard’s walk”). One approach to de-trending involves the use of Fourier Transforms. Here is a excerpt about the use of Fourier Transforms found at the website below the quote:

“The Fourier transform, in essence, decomposes or separates a waveform or function into sinusoids of different frequency which sum to the original waveform. It identifies or distinguishes the different frequency sinusoids and their respective amplitudes.”

John Ehlers devised an approach to Cycle Analysis he dubs MESA. Claimed to be more effective for isolating short-term cycles over the Fourier Transform. Both these approaches are based on the assumption that the cycles found in market patterns are formed by random actions, a theory I have long discovered is not correct. However, regardless of the base theory to how cycles have come to exist within market patterns, fact remains they do exist. And so these varied approaches to exposing these cycles have proven to be useful to a degree.

Why I am able to say with conviction that market cycles are not the result of random behavior is based on my own experimentation in the field of cycles. I became interested in cycles after having read about them in W. D. Gann’s books. These books did not provide me with anything I could use as far as cycles analysis goes, except that it started me looking in the right direction. Having already discovered a geometric approach to timing market tops and bottoms that was incorporated into a software program; this was used for comparison purposes when I started to incorporate my theories about cycles into its own computer program. Today I use both programs to confirm their respective results. It is absolutely amazing to see the relationship between market cycles and market geometry. They are most definitely related!

My approach to cycle analysis is unique in comparison to those widely known and advertised. Rather than trying to de-trend historical market patterns into their individual components and then re-combined them for forecasting purposes, my approach is based on locating anchor points in time within the pattern itself and then moving it forward in time (and in sync with the recent past) for the purpose of forecasting.

For instance, at some point in time market patterns will REPEAT themselves. In its basic form, this is what many ‘technicians’ use to trade the markets. For example, you may have heard of the ‘M’ pattern or the ‘heads-and-shoulders’ pattern. You've no doubt heard of the ‘triangle’, ‘flag’ and others. Technicians have long discovered that the market will follow a certain behavior more times than not after these common patterns.

But imagine if you can locate COMPLETE patterns that span beyond a simple ‘flag’ or ‘heads-and-shoulder’, etc. Then imagine if you could find this repetitive pattern in more than one time period in the past. What you will discover as I have is that the time distance between the patterns are EQUAL in length. So then, once the pattern has been found, you KNOW where to plot it into the future; the exact distance from the end of the last one you found.

It takes a very sophisticated program to do all this comparing, but that is exactly what I had my program do. And the results continue to amaze me.

The point here is that what you see on your price charts is but a small sample of a much larger picture (going all the way back to when the market started trading). So it is easy to not be able to see the repeating pattern threads.

To help you further understand the depth of this cycle analysis and what will make it hard to easily see the patterns is due to the MAGNITUDE of some of the component cycles.

For example, consider the two cycle threads below (these are dynamic cycles as they the result of several fixed-interval cycles combined). The spacing between the numbers depicts the varied distances between the cycle turns (tops and bottoms). Note these distances vary because we are dealing with patterns as seen on your chart that are dynamic cycles and not just a single fixed cycle. The numbers we will call the MAGNITUDE of the turn. The positive numbers are tops, the negative numbers are bottoms. The magnitude will range from 1 to 10 for tops, -1 to –10 for bottoms. Obviously then, the higher the number positive the higher the top, and the lower the number negative the lower the bottom.


The above are TWO EXACT cycle threads based purely on PATTERN. Yet, the magnitude of these two threads is different. So if you rely on your ‘eyes’ to spot the patterns, you will not likely be able to do so even if you had ALL the data stretching back years before you on the wall. Thus, it takes a computer to help here.

Now if we had data covering centuries it may be possible to find the start of the exact pattern, magnitude and all, that has occurred since. Well, there just isn't enough data to do this. Anyway, it isn't important for our purposes. We simply want to determine when a turn is most likely. We don't need to have the sight of God.

This concludes Part Four.