“Vector Strength” quantifies support and resistance

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To make our explanation of how Vector Strength quantifies support and resistance” accessible and acceptable to the broadest possible audience we first explain it using visual / natural world analogies, then we follow with an explanation of how it is analogous to certain established techniques and commonly employed assumptions of quantitative finance.  We encourage readers interested in the latter to read the former as well.  

Visual / Natural World Analogies:

Vector Set Strength decreases with distance and time

Like the wake behind a speedboat, Vector Set Strength generally dissipates with distance and time from the model date price. Therefore, Vector Sets anchored by recent tops and bottoms have greater strength than those formed from tops and bottoms that occurred in the distant past. Likewise, Vector Sets whose vectors are tightly bunched around the current price have greater strength than those that are more disperse or don’t envelop the current price at all.

Top and bottom “touches” also indicate Vector Strength

Another indication of Vector Set Strength is the number of tops and bottoms that touch the vectors of the Vector Set, including those occurring between and beyond the tops and bottoms to which the Vector Set is anchored. A high level of Top and Bottom touches demonstrates that a Vector Set is well attuned to the net Vector Set balance trajectories driving the price.  Just as the pelicans skimming the wave crest in the pic below are going where the fish are being pushed, a vector set skimming the crest of the net vector set balance is going where price is likely headed.

The cumulative Vector Strength between the prior close and any given forward price is the quantification of cumulative support and resistance between the prior close and that forward price.

Similarly, the more breakers you encounter, the harder it is to swim out from shore.

How proximity and touch count factor into Vector Strength

Touch count tends to increase with the age of a vector set. Older Vector sets definitely have less proximity with respect to “time”, though they may or may not have high proximity with regard to price. VecViz balances these considerations in its formulation of Vector Strength. The series of four vector sets depicted below, in descending order of Vector Strength (i.e., Vector Set 1 is strongest), provides some indication of how these tradeoffs work.  

For example, Vector Set 1 has sufficiently more “touches” than Vector Set 2 to more than offset the fact that it has less proximity with regard to time and price. In contrast, Vector Set 3 is further in proximity than Vector Set 4 in terms of price, but closer with respect to time, and Vector Set3 likely has more “touches”.  

Some additional key takeaways:  A Vector Set’s “strength” ranking changes over time as the prices of the ticker and the Vector Set (if sloped) change and time passes.  This is especially true to the extent the passage of time brings forth new tops and bottoms.  New tops and bottoms may or may not add to an existing Vector Set’s touch count, but they always generate new Vector Sets.  

Analogies to established techniques and concepts of quantitative finance:
Cumulative Vector Strength traversed is how the Vector Model scales price movement.  In other words, it is how the Vector Model scales price volatility.  The more Vector Strength between the current price and some other price, the further away it is in terms of likelihood of reaching it, all else (especially chart shape metrics) equal.  There are several parallels between Vector Strength and its use to scale volatility and the techniques and commonly employed assumptions of quantitative finance.

1) Emphasis on more recent data: Ascribing more vector strength to recently formed vector sets is akin to the well established principal in quantitative finance that “sigma” volatility metrics are improved upon by applying exponential decay to the return lookback window. 

2) Scaling of deviations, and in like quantities Vector Strength is ascribed to Vector Sets, which are anchored by at least one top and at least one bottom.  Take the simple case of a flat vector set anchored by one top and one bottom.  Assume that the distance from top to bottom, the core channel of the Vector Set, spans one “standard” deviation (conceptually, not as defined in your stats textbook) to the upside from the center and one to the downside from the center.  By comprising each Vector Set of a “core” and an approximately equally sized “levelled up” and “levelled down” area, each Vector Set encompasses ~ 6 sigma, an essentially full range of variability as would be contemplated under the Black Scholes model (at least for the one year forward period from the dates of the Top and Bottom).    

3) Central Limit Theorem: The Vector Strength of a single Vector Set in isolation is meaningless, but VecViz generates hundreds of Vector Sets for each ticker it covers.  Consider each as an estimate of the range of deviation, based on a sampled range of price history (encompassed by the tops and bottom dates).   In aggregating them, on a Vector Strength weighted basis VecViz is attempting to estimate the true forward one year range of deviation.  The intuition behind doing so is that from a large enough collection of samples that truth about the population can be known, which is the core of the Central Limit Theorem.  

4) Factor Based Risk Models: The number of tops and bottoms a Vector Set has touched is an influential component of its Vector Strength calculation.  A Vector Set line that has a lot of such touches likely is not dissimilar to a regression line for the period spanned by the Tops and Bottoms.  The techniques of factor risk models are essentially centered upon identifying such lines, but rather than use the lines that connect tops or bottoms as VecViz does in its construction of Vector Sets, they use time series data for factor indices (or more rarely, principal components).  Such models map a ticker’s historic variability to factor index exposures on the basis, essentially, of the fit of a regression of their returns on the returns of the ticker.  Then they use the most recent measures for the (typically sigma) volatility of the factor indices to which the ticker has been mapped, and the correlation between them, to estimate the ticker’s forward variability.  

Subjectively identified trend lines thought to represent support and resistance are the technician’s approach to scaling volatility.  Sigma, factor models, and the like are some of the tools used by quantitative finance.  Vector Strength quantifies the former, in part, by incorporating the intuition behind some of the techniques of the latter.  

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