From the third chapter of my book...
Copyright © 2023 Fred Duckworth
Numerical Price Prediction's making was carried out by applying five biblical principles to the art of buying low and selling high; and as already stated, the first principle led me to reject the use of almost all common indicators, such as MACD, RSI, CCI, stochastic oscillators and the like, along with any approaches involving harmonic patterns, Elliot waves, pivot points, Fibonacci ratios and whatnot.
Instead, I attempted to rightly interpret "the signs of the times" by devising a system based as much as possible on statistical analysis and mathematical probability via the adoption of a methodology similar to that used by meteorologist (numerical weather prediction) when making their forecasts.
The idea was to gather and evaluate precise, up-to-date, quantitative data and use it to calculate the odds of price reaching designated values within a given time period by patterning my system's elements after the equations, wave functions, and computer models used in weather forecasting.
But, instead of monitoring wind velocity/direction, cloud formation, humidity, temperature, and barometric pressure; I focused on the synergy between such factors as typical price ranges, reoccurring chart patterns, horizontal support and resistance levels, baselines, and market structures.
To put it in biblical terms, I consulted "a multitude of counselors" that proved to be valid and reliable over time, with the result being graphical depictions (or computer models) of current conditions I could then use to help me make precise, well-timed trades.
The system incorporates the idea of cycle theory, which holds that cyclical forces, both long and short, drive price movements, and can be used to anticipate turning points. It is also compatible with Edgar Peters’ fractal market hypothesis, which views financial markets as fractal in the sense that they follow cyclical and replicable patterns—ones consisting of fragmented shapes that break down into parts which then replicate the shape of the whole.
I used such cycles to generate the baselines mentioned above by conducting a thorough analysis to first uncover the cyclical waves formed in the wake of price action, followed by the defining of their general frequencies and magnitudes; and then finally plotting centered moving averages that came as close as possible to approximating the zero amplitude of the corresponding waves/cycles.
So as you can imagine, the notion that there are no "best" moving averages to use when trading is not one to which I subscribe. Again, at the heart of my system is the use of carefully selected baselines which I calculated in the manner just explained.
And yet, it is my understanding that individuals such as Norm Fosback, the former head of the Institute for Econometric Research, believe there are no magic numbers in trend following, and that it should be a basic requirement of any moving average trend following system that practically all moving average lengths predict successfully to a greater or lesser degree.
I have also read that the moving average one chooses is not as important as getting familiar with the way in which price interacts with it. But with all due respect, I regard both of these contentions as pure rubbish!
The way I see it, even Fosback’s own statement suggests the possibility that there might indeed be "magic" numbers in trend following. For if practically all moving average lengths predict successfully to a greater or lesser degree, it follows that those which predict successfully to a greater degree are the better moving averages—which would in turn infer that the moving average which predicts successfully to the greatest degree is the best moving average of all.
So then, one simply needs to wade through all that data to arrive at the one moving average which is superior. This is why the idea of applying the exact same standard settings to different time frames (i.e., the 10-, 20-, 50-, 100- and 200-period moving averages to five-minute charts, 60-minute charts, daily charts, etc.) has always struck me as counter-intuitive.
Of course, I have had many traders who possessed far more experience and knowledge than me eagerly explain how it is impossible to use moving averages in the manner I envisioned.
According to them, the core beliefs on which I based my suppositions conflicted with, and were therefore discredited by "the findings of practically every available objective, independent, systematic, statistically significant research trial ever conducted and published on the subject."
Nonetheless, I continued on my quest, regarding myself as sort of a modern day Aristarchus of Samos or Nicolaus Copernicus within the world of Forex trading.
As you may know, nearly everyone tried to convince these independent thinkers that it was impossible for the sun to be the center of our planet's system. Yet, just look at how wrong all the "experts" were! In all honestly, one can simply glance at my charts and see the logic behind my approach.
That doesn't seem to matter, however, to many individuals. And even when I showed the naysayers hard evidence attesting to the profitability of my method, very often, their typical response was to dismiss the data based on the theory that the system would fail…eventually, or that my evidence was invalid because it did not constitute ten, twenty, or a hundred years' worth of back testing and ought to therefore be discounted.
But in fact, the chances that the effectiveness of my approach will break down over time is virtually nonexistent given that the entire system is based strictly on mathematics—which does not fundamentally change.
Let me ask…if an Airbus A340 is able to lift off the ground today by angling upward at two to three degrees per second with a maximum angle of 10 to 15 degrees, are we to speculate that doing so 20 years ago would have ended in disaster, or that this same maneuver performed 100 years from now will cause the plane to crash? Of course not!
Likewise, as long as up is up and down is down, there is no reason to assume Numerical Price Prediction will not work as well tomorrow as it does today.
Among stock investors, there is a theoretical concept known as a Holy Grail trading system—one that always produces profitable trades regardless of the market environment or asset class traded. Though it doesn't exist in practice, it can help an individual design a more profitable trading strategy to call their own, and this is exactly what I did.
But, unlike so many hopeful treasure hunters searching for the one magical technical indicator able to unlock the secrets of the markets, I've adopted the view that there are any number of factors, or "data points" impacting foreign currency exchange rates, with the "Holy Grail" being the ability to unravel the hidden correlations between them.
It's all about interpreting what's happening in the moment based on market generated information, which is to say, technical analysis. (I choose not to put my trust in non-market generated information—meaning fundamental analysis.)
It comes down to what Peter Reznicek refers to as "ruling reason," which for me, is just another way of saying the numbers…the math…the summation of all those correlating data points that are a part of the market generated information. It's a matter of crunching the numbers and doing so in the correct manner—plain and simple.
And speaking of "correct manner," I think I should probably end this chapter by mentioning that, though one often hears traders stating "the trend is your friend," from my perspective, it would almost surely be more accurate to say that the trend is merely one of several friends.
For it seems to me that what would have to be considered at least equally as important as trend is the location of rates within the entirety of a given asset's price distribution. So then, though investors often speak of trend lines, I've ceased to think of trends as being represented solely by lines, and have come to conceptualize them as belts as well, with the location of price within the expanse of values constituting the width of these oscillating bands being just as important (when deciding exactly where to enter and exit positions) as the general direction that each "breadth of values" is headed.
Accordingly, my final decisions on when to buy and when to sell are always made based on the consensus of various input data, sampled in multiple time frames—data which includes baselines, market structure, temporal support/resistance, horizontal support/resistance, price ranges, and reoccurring chart patterns.
It is the consensus opinion of all these various factors that determines what I will decide to do in the final analysis. The moves I make depend on what each of these determinants means in light of all the others and how they all will affect and impact on one another. It is the interpretation of each moving part individually—and of all these assorted components as a whole—that constitutes Numerical Price Prediction (NPP), with the "belts" I just mentioned playing a key role in how the interpretation of the system's corresponding forecast models is carried out.
