I have a so-called entrepreneur who scheduled a call with me next Monday regarding his supposed interest in helping me scale up my "company."
I don't expect anything to come of it. Nonetheless, the gentleman
has launched an early-stage investing program in the past, as well as an investment desk; and he's directed business development for a closely held communications company; not to mention advising a financial services firm on business opportunities; so I'm willing to hear what he has to say.
I am therefore revamping my description of NPP to refresh my own memory as to what, in my view, NPP is all about...
What is Numerical Price Prediction?
Copyright © 2023 Will (Fred) Duckworth
"Buy low and sell high."
This well-known axiom is touted as the key to success in the financial markets. But until price reaches zero, what's low is completely relative, and since price can theoretically climb to infinity, high is an almost meaningless construct.
The answer to this dilemma is to give each, high and low, a valid context, which Numerical Price Prediction (NPP) does by accurately assessing the direction in which price is headed, and then calculating the maximum distance it is willing to deviate from that course within a given time frame. Once one establishes these two pieces of information, figuring out the relative position of high and low becomes a fairly straightforward process.
Unfortunately, for most traders, it is not always clear in which direction price is moving. Of course, many experienced professionals will tell you to look for successively higher peaks and valleys to identify up trends, and successively lower peaks and valleys to identify down trends
But, a glance at almost any financial chart will illustrates why this is often easier said than done. When it comes to the markets, price action is often characterized by endless fits and starts. The behavior of price is hardly clear-cut, typically executing erratic, haphazard, seemingly random maneuvers that evidence little rhyme or reason.
Numerical Price Prediction clears away the fog using a unique and innovative methodology that relies on something akin to what meteorologist use to predict the weather—an approach based as much as possible on statistical analysis and mathematical probability.
The idea is 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 the system's elements after the equations, wave functions, and computer models used in weather forecasting.
But, instead of monitoring wind velocity and direction, cloud formations, humidity, temperature, and barometric pressure; it evaluates the synergy between such factors as typical price ranges, reoccurring chart patterns, horizontal support and resistance levels, trend lines, and market structure, all in multiple time frames—with the result being a graphical depiction of current conditions that traders can then use to help 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's 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.
When the developing the system, I used these cycles to generate what some traders (such as Patrick Victor) refer to as "baselines," 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.
Even so, to trade with the clarity and precision I desired required me to carry out an additional step in which I assigned a specific temporal value to each individual baseline and its corresponding or associated price-range envelope(s)—to answer the question: What moving average best conveys in which direction and by how much price moves every five minutes, or every four hours, or even every day?
My goal was to arrive at something reflecting flight dynamics, where the laws of physics explain how forces act on vessels to govern their performance, stability and control to ultimately determine their velocity and attitude with respect to time.
Hence, in the same way pilots are aware that a Boeing 747 will lift off the ground by angling upward at two to three degrees per second with a maximum angle of 10 to 15 degrees; I as a retail trader now know the parameters dictating whether an asset is rising or falling from the perspective of a day, swing, or position trader.
And yet, even after this "final" step, their emerged still another aspect to interpreting price action that proved deserving of my consideration which I had not envisioned at all—the concept of "temporal" support and resistance.
In other words, not only do I believe there is a certain amount of
distance beyond which exchange rates will typically resist separating themselves from the central tendencies of key price distributions; it seems to me I have also observed that there is generally a limit to the amount of
time exchange rates will advance in one particular direction without deviation.
I refer to these limitations as
temporal support and resistance (AJ Monte uses the terms "stale green" and "stale red" candlesticks) and they have proven to be a welcome enhancement to my system.
Numerical Price Prediction was developed based on the following five biblical principles:
- Test everything and hold fast only to that which proves 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.
To my surprise, applying the principle of "testing everything and holding fast to that which is good" led me to reject many strategies wholeheartedly endorsed by any number of trading gurus, such as Elliott waves, Fibonacci ratios, harmonic patterns, pivot points and the like.
In effect, I replaced the advice to "keep your eyes on the road" with a mandate to "focus on your destination," a subtle, yet profound, distinction. Obsessing on the former tends to be constraining—dictating one's movements and limiting the parameters within which one is free to operate, often locking people into notions that are not truly worthy of the reverence bestowed upon them.
But, emphasizing the latter allows folks to be creative and take any route desired, so long as it carries them toward that on which they have resolutely set their gaze.
So, when strategies involving moving average convergence/divergence (MACD), stochastic oscillators, the relative strength index (RSI), the commodity channel index (CCI), the average directional movement index (ADX) and other indicators failed to live up to their reputations, I had no qualms about discarding them entirely and searching elsewhere for the "signs of the times" which, if interpreted correctly, would result in market forecasts of unusual accuracy.
As it turned out, I found that the absolute best "atmospheric barometer" for predicting the direction in which an exchange rate might ultimately be headed was nothing more than a simple moving average, with a handful of key moving averages evidencing superior accuracy in this role (which I already referenced when referring to baselines).
But though baselines constitute the backbone of my system, there are a number of other 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 simply 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 mention 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 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.
So then, Numerical Price Prediction is 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 "ruling reason," which for me, is just another way of saying the numbers, or "the math" if you will—the summation of all those correlating data points that are a part of the market generated information.
