@expiated what makes your system better?
Copyright © 2021 Fred Duckworth
The development of Numerical Price Prediction (how I refer to my system) was carried out by applying five biblical principles to the science/art of trading, as listed below…
The Biblical Foundation of Numerical Price Prediction:
- Test everything and hold fast only to that which proves to be valid and reliable.
- Systems generally operate at peak performance when the interactions between their component parts evidence strong, healthy relationships.
- The best plans are usually established in the presence of a multitude of counselors.
- Rightly interpreting the signs of the times is an absolute necessity.
- Positive outcomes are typically the result of having made good choices.
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 methodology based as much as possible on
statistical analysis and
mathematical probability. 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 (or the strong, healthy relationships) between such factors as typical price range, reoccurring chart patterns, horizontal support and resistance levels, baselines, and market structure.
In other words, 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; which is to say, good choices based on rightly interpreting the signs of the times.
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
baselines, which I define as painstakingly selected moving averages that serve as valid, reliable road maps showing traders where price is likely to go in the not-too-distant future. This was accomplished 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 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. This is why the idea of applying the exact same standard settings to different time frames (i.e., the 10-, 20-, 50-, and 200-period moving averages to five-minute charts, 60-minute charts, daily charts, etc.) has always struck me as counter-intuitive.
Naysayers typically dismiss the success I've achieved by stating that the system will fail
eventually, or that my results are invalid because they do not constitute ten, twenty, or a hundred years' worth of data and ought to therefore be discounted.
Yet, I maintain 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.
For example, if a Boeing 747 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, am I 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. It's all about interpreting what's happening in the moment based on market generated information, which is to say, technical analysis.
I should mention however that even though my system relies heavily on baselines, its effectiveness is also due in large part to
ceasing to think of trends as being represented by lines, per se, and conceptualizing them instead as
"belts,
" with the location of price within the expanse of values constituting the width of these oscillating strips 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, typical price ranges, temporal support/resistance levels, horizontal support/resistance levels, market structure, 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 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, in a nutshell.
