Core Concepts
What is VecViz, and what problem does it solve?
Most investors know about support and resistance—zones where buying or selling pressure tends to concentrate. But while these concepts are widely discussed, they’re rarely quantified. VecViz is perhaps the only charting framework that systematically measures and scores support and resistance across a ticker’s entire price history.
VecViz provides three main ticker level offerings are: (1) a charting framework that visualizes quantified support and resistance, (2) the Vector Model, which uses machine learning to generate probability-based price forecasts, and (3) the V-Score, an indicator of expected relative performance. All three are built on the same foundation: quantified support and resistance.
VecViz also recently introduced portfolio optimization oriented analytics, including two measures of forward correlation: VecEvent based correlation and VecViz Analytic “Fingerprint” based correlation, and a VaR and OaR breakage based regime framework for scoring expected forward ticker performance.
What is Vector Strength?
Vector Strength is VecViz’s proprietary score for quantifying the support or resistance a price channel is likely to exert. It’s calculated using three factors, each supported by academic research on support and resistance:
Time proximity: More recent levels have greater influence. Price proximity: Levels closer to the current price have greater influence. Touch count: Levels that price has bounced off more frequently have greater influence.
The cumulative Vector Strength between the current price and any forward price represents the total support or resistance standing in the way. The more Vector Strength between two prices, the harder it is to get from one to the other—and the lower the probability of reaching that price, as depicted in the image below:
For more about Vector Strength from a conceptual perspective read here and here. For more from a practical perspective read here, here, and here.
What is a Vector Set, and what are all the grey lines on the chart?
A Vector Set is a price channel anchored by at least one major top and one major bottom. Think of it as a triple-decker Fibonacci channel comprising 15 lines (called Vectors):
The core section (middle deck) spans from the anchoring bottom to the anchoring top, with three Fibonacci-spaced lines in the center. The leveled up section (upper deck) extends above to capture resistance at higher prices. The leveled down section (lower deck) extends below to capture support at lower prices.
The grey lines you see on the Vector Strength Histogram are these individual Vectors. VecViz identifies up to 325 Vector Sets per ticker—far more than any analyst could track manually. Each Vector Set is scored for Vector Strength, indicated by shading intensity (darker = stronger) and histogram bar length (longer = more cumulative support/resistance at that price).
What is the Vector Model?
The Vector Model is VecViz’s machine-learning-based system for forecasting forward price probabilities. Its key innovation: rather than measuring price movement in dollars or percentages, the Vector Model scales price movement by the amount of support and resistance (Vector Strength) that must be traversed. A 5% move through heavy resistance is fundamentally different from a 5% move through open air.
The model is trained to predict how much Vector Strength is likely to be traversed based on a ticker’s “chart shape” profile, then translates that back into price probability percentiles. These are displayed in blue on VecViz dashboards for six forward time horizons (from 1 day to 1 year).
What is Sigma, and how does it differ from the Vector Model?
Sigma is the standard deviation of returns—the foundational volatility concept used in the Black-Scholes option pricing formula and introductory finance courses. VecViz calculates Sigma using daily log returns over a two-year lookback period with a 6-month half-life decay.
The key difference: Sigma treats all price movements of equal magnitude as equivalent—it doesn’t account for support and resistance. VecViz displays Sigma-based probability percentiles in red alongside the Vector Model’s blue percentiles, so you can see how much the support/resistance structure affects probability estimates for a given ticker.
Key Metrics
What is the V-Score?
The V-Score is an indicator of expected relative performance, designed to answer: “Are these charts bullish or bearish?” It’s calculated using an ensemble of machine learning techniques applied to Vector Model inputs and outputs.
A preliminary score (-2 to +2) is calculated for each of six forecast horizons. The sum produces the final V-Score, ranging from -12 (most bearish) to +12 (most bullish). Out-of-sample testing shows modest predictive power for relative performance, particularly for horizons between 10 and 252 trading days.
The V-Score also enables the V-Score Criteria Closest Comparables chart, which shows historical ticker-dates with similar chart shapes from both top-quintile and bottom-quintile performers—helping you see what happened in analogous situations.
What are VaR and OaR?
VaR (Value at Risk) is the maximum loss you could experience at a specified future date at a given probability level. If 95% VaR is accurate, losses would exceed it only 5% of the time. VecViz shows 95D and 99D levels for both Vector Model (blue) and Sigma (red).
OaR (Opportunity at Risk) is the maximum gain you could forgo by being uninvested. It’s VaR’s upside counterpart. VecViz shows 95U and 99U levels for both models.
Important: VaR and OaR estimates apply to the specified future date only—not the minimum or maximum price over the intervening period. VecViz monitors “breakage rates” (how often actual prices exceed these levels) in dedicated dashboards.
What are EUB and EDB?
EUB (Expected Up Body) and EDB (Expected Down Body) are probability-weighted average prices within the “body” of the distribution (between the 95th percentiles up and down).
EUB tells you what price level to expect if the ticker rises but stays below the 95U tail. EDB tells you what to expect if it falls but stays above the 95D tail. Together they comprise the Vector_BodyFrcst displayed on dashboards.
Narrative & Context
What are VecEvents and VecDates?
VecDates are the dates of the tops and bottoms that anchor each Vector Set—the turning points that define support and resistance channels.
VecEvents are news events, themes, or phenomena linked to Vector Sets based on timing. They provide narrative context: why did that top or bottom form? To our knowledge, VecViz is the only charting framework that embeds narrative elements directly into charts. Currently, VecEvents are sourced internally by VecViz.
Theoretical Foundation
How does VecViz relate to established quantitative finance?
VecViz’s approach has several parallels to traditional quantitative finance:
Recency weighting: Ascribing greater Vector Strength to recently formed Vector Sets parallels the well-established principle that “sigma” (standard deviation) based volatility metrics are improved by applying exponential decay to the lookback window of returns. Both approaches give more weight to recent data.
“Standard” deviation coverage: Vector Sets are defined by at least one price top and at least one price bottom. Consider a simple case: a flat Vector Set anchored by one top and one bottom. The distance from the center of this channel to the anchoring top—and similarly, the distance from the center to the anchoring bottom—can each be thought of as representing one conceptual “standard deviation.” In other words, the core channel of a Vector Set spans approximately two standard deviations (±1 sigma from the center).
By then adding the “leveled up” section above and the “leveled down” section below—each approximately equal in width to the core—the full Vector Set extends to roughly ±3.5 sigma from the center. This means each Vector Set aims to encompass a range equivalent to approximately seven standard deviations total, essentially capturing the full spectrum of variability often considered in models like Black-Scholes for a one-year forward period.
Fibonacci ratios (left) map to approximate standard deviations (right). The core channel ≈ ±1z; the full Vector Set ≈ ±3.5z.
Central Limit Theorem: The Vector Strength of a single, isolated Vector Set may have limited meaning. However, VecViz generates hundreds of Vector Sets for each ticker. Each Vector Set can be considered an estimate of the range of price deviation, based on a sampled period of price history (defined by the dates of its tops and bottoms). By aggregating these Vector Sets on a Vector Strength-weighted basis, VecViz attempts to estimate the true forward one-year range of deviation. This reflects the Central Limit Theorem’s core concept: a sufficiently large collection of samples can reveal characteristics of the entire population.
Factor model parallels: A Vector Set boundary line with many touches may function similarly to a regression line for the period spanned by its anchoring tops and bottoms. Factor-based risk models in quantitative finance are fundamentally centered on identifying such relationships—they map a ticker’s historical variability to its exposures to factor indices based on the goodness-of-fit of regressions. While VecViz uses lines connecting price tops or bottoms rather than factor indices, both approaches seek to identify meaningful structural relationships in price behavior.
Additionally,
- there is academic and industry research that supports the concept of support and resistance – we place VecViz in its context here.
- academic research related to the use of neural networks in conjunction with chart imaging supports several of the “chart shape” features in our Vector Model and V-Score methodology. See our blog comparing the V-Score to chart image recognition models for more detail.
- Industry increasingly relies upon machine learning for volatility estimates, particularly in options market making. Read about the evolution toward “deep learning of rough volatlity” and how VecViz relates to it here.
Where can I review VecViz’s analytic performance?
VecViz is committed to transparency. We publish a report on the performance for each individual VecViz metric, comparing them to Sigma based metrics wherever applicable. We also report on the performance of portfolios generated using VecViz and Sigma metric based optimization. All such reports are available in the Reports section at vecviz.com, covering more than three years of out-of-sample data.
How can I get access to VecViz analytics?
We recently developed an API to deliver VecViz analytics into the OpenBB Workspace. Let us know if you are interested by sending an email to admin@vecviz.com.