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 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).

Vector Strength is VecViz’s proprietary score for quantifying the support or resistance a Vector Set price channel is likely to exert. It is calculated using three factors, each supported by academic research on support and resistance:

• Time proximity: Vector Sets anchored by more recent tops and bottoms have greater influence.
• Price proximity: Vector Sets terminating closer to the current price have greater influence.
• Touch count: Vector Sets with lines that touch more tops and bottoms (including tops and bottoms that are not significant enough to be Vector Set channel anchors) 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 from a conceptual perspective read here and here. For practical applications read here, here, and here.

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 to the upside and downside based on a ticker’s “chart shape” profile. Chart shape includes concepts like timing of last tops and bottoms, proximity of highest tops and lowest bottoms, the angle of the Vector Set channels, and the distribution of Vector Strength above and below the current price.

The predicted Vector Strength quantities are then applied to the given ticker’s current distribution of support and resistance by price to arrive at price probability percentiles. Vector Model based price probability percentiles are displayed in blue on various VecViz dashboards (whereas Sigma is displayed in red, for comparison).

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 considers daily returns only. It is indifferent to their ordering and what prices they were associated, and totally unconcerned with the concept of 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.

VecViz’s V-Score ranks tickers on their expected forward price return performance. We believe it to be applicable for periods ranging from 1 day to 1 year forward. The V-Score is based entirely on 13 metrics comprising Vector Model inputs and outputs and ratios thereof.

All these inputs are disclosed in the V-Score “Closest Comparables” spider 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. The V-Score inputs relate to the characteristics of the Vector Set channels and the Tops and Bottoms that anchor them, such as the angle of the channels, and the timing of the last tops and bottoms. We sometimes refer to such metrics as being “Chart Shape” related. Vector Model probability distribution and Option Fair Value Estimate related metrics are also considered.

The V-Score utilizes a proprietary ensemble of widely used Python machine learning model libraries to generate 6 time horizon specific scores (each ranging from -2 to +2) that sum to the “overall” V-Score (which ranges from -12 to +12). This ensemble of models is trained upon the 13 Vector Model related metrics discussed above for 30,000+ ticker-model dates spanning 100-150 tickers and 250 randomly selected model dates between June 2005 and January 2021. The only input to VecViz’s chart shape and related metrics for each ticker – model date, is a window of up to 6,500 closing prices, to the extent available.

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 is 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.

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.

These metrics attempt to capture the impact on investor returns of using the Vector Model instead of Sigma VaR and OaR metrics to size positions, assuming the investor has a fixed risk budget per ticker. They are constrained by a cap of 300% and a floor of 33.33% to ensure realistic performance parameters.

• Return on VaR Based Capital (ROVBC): Assumes position sizing is dictated by downside risk. Sigma earns the return of the ticker, and the Vector Model earns a proportionate return based on the ratio of Sigma VaR / Vector VaR. Higher ROVBC indicates the Vector Model successfully identified elevated risk, protecting capital on downside moves.
• Return on OaR Based Capital (ROOBC): Assumes position sizing is dictated by upside opportunity. Sigma earns the negative of the return of the underlying, and the Vector Model earns a proportion based on Sigma OaR / Vector OaR.
• Return on Long OaR Based Capital (ROLOBC): A variation of ROOBC oriented toward long-only, return-seeking investors. It assumes Sigma earns the return of the ticker, and the Vector Model earns a proportion based on Vector OaR / Sigma OaR.

Similar to the VaR and OaR efficiency metrics above, these capture the impact of position sizing based on the Expected Body (EUB and EDB) forecasts, utilizing the same 300% cap and 33.33% floor constraints.

• Return on Expected Down Body (ROEDB): Reflects a risk-averse strategy. Sigma is ascribed the underlying return, and the Vector Model implementation is ascribed a multiple based on the ratio of Sigma EDB / Vector Model EDB.
• Return on Expected Up Body (ROEUB): Reflects a return-focused strategy. Sigma is ascribed the underlying return, and the Vector Model is ascribed a multiple based on the ratio of Vector Model EUB / Sigma EUB.

Beyond its ticker-level offerings, VecViz provides a suite of portfolio optimization analytics that apply its support/resistance and narrative frameworks to correlation estimation and regime-based performance scoring:

• VecEvent-based correlation: Measures forward correlation between tickers based on the similarity of their VecEvent narratives — an alternative to purely price-history-derived correlation that incorporates the fundamental stories driving each ticker.
• VecViz Fingerprint-based correlation: Derives forward correlation from the similarity of tickers’ VecViz analytic “fingerprints” — their pattern of Vector Model outputs and chart-shape features — providing a price- and return-aware correlation estimate.
• VaR/OaR breakage-based regime framework: Scores expected forward ticker performance based on the historical regime of VaR and OaR breakage rates — identifying periods when a ticker’s price distribution has been systematically over- or under-estimated, and using that regime context to inform forward return expectations.

Most traders look at a chart. Some look at the news. Few attempt to mathematically compare the two. Vector Narrative Alignment (VNA) does exactly that.

VNA is VecViz’s valuation framework for determining whether a ticker is cheap or rich relative to its prevailing narrative, which we source from a leading LLM, as a baseline. The implementation of VNA on the OpenBB workspace allows the user to adjust the characterization of a ticker’s narrative to reflect their own views. It compares two quantities:

• Actual Price VecLevel — where the ticker’s price currently sits within its channel, measured in channel core-widths from center. This is where we are.
• VecEvent Implied VecLevel — a score derived from LLM-characterized VecEvents, reflecting how bullish or bearish the narrative is, adjusted for whether each narrative emerged during or after the channel’s formation period. This is where the story says we should be.

VNA scores are aggregated across dozens of channels for each ticker on the basis of the scored support and resistance (Vector Strength) each channel represents. Then, we adjust for systematic LLM bias via regression across tickers. The residual — All Ticker Adj VNA Upside (VLs) — answers: how much better is this ticker’s story than the market is giving it credit for, relative to how all other tickers are priced against their stories?

Translating this into a VNA Target Price is done by multiplying the adjusted VNA Upside by the ticker’s average Price Per VecLevel and adding it to the model date price.

A VecLevel is the price’s location within a Vector Set channel, measured in channel core-widths from the channel center. The channel core-width is the price differential between the parallel lines linked to the bottom(s) and top(s) anchoring the channel.

• A VecLevel of 0.0 means the price is at the channel center.
• Values beyond ±0.5 indicate price is outside the core channel (extended or compressed).
• A VecLevel of +1.0 means the price is one core-width above center — at the center of the leveled-up channel.
• A VecLevel of −1.0 means the price is one core-width below center — at the center of the leveled-down channel.

VecLevels provide a normalized, channel-relative location metric that is comparable across tickers and time periods — the common unit of measurement that makes VNA’s cross-sectional comparison possible.

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 the news themes and catalysts linked to those turning points based on timing: why did that top or bottom form? VecViz sources VecEvents using a leading LLM and characterizes each on two dimensions:

• Bias: Bullish or Bearish
• Bias Trend: Intensifying, Steady, or Waning

To our knowledge, VecViz is the only charting framework that tags narrative context to price channels — and the only framework that uses that narrative context to generate a quantified valuation.

Not all narratives influence a channel equally — when a narrative emerged relative to the channel’s formation period matters significantly.

VecViz’s theory is that a channel’s trajectory reflects the net bullish/bearish balance of the narratives active during its formation period (the span of dates between its anchoring tops and bottoms). Extrapolating the channel forward presumes that balance has been maintained.

• A bullish narrative from the formation period that is now waning reduces the net vector balance — implying some downward drift within the channel going forward.
• A bullish narrative that has intensified since formation pushes the balance more bullish — implying upward drift.
• A bullish narrative emerging after formation always contributes positively to the net balance, even if it has recently begun to wane.

These dynamics are captured in the VL_Score assigned to each Bias / Bias Trend / Timing combination:

Given VNA’s heavy reliance on recently introduced, frequently updated LLMs — and the resulting challenge of point-in-time knowledge cutoffs — rigorous back-testing is not feasible. An LLM running today cannot reliably reconstruct what it would have said about a narrative in 2019.

However, VecViz is committed to transparency. We will track and report on VNA’s forward performance from the date of introduction and regular production (May 2026) onward, published in the Reports section alongside all other VecViz performance metrics.

VecViz’s approach has several parallels to traditional quantitative finance:

Emphasis on high trading volume: VecViz places considerable emphasis on price observations associated with high trading volume by virtue of anchoring channels directly on major tops and bottoms. See the following FAQ on Tops and Bottoms for more detailed empirical analysis of this phenomenon within the framework.

Balancing Memory and Stationarity Via Sloped Channels: The framework’s emphasis on sloped Vector Set channels (as opposed to flat, horizontal channels) naturally balances price and return considerations. This provides the associated benefits of capturing both market memory and stationarity. Finding a rigorous balance between memory and stationarity was a key motivation in Marcos López de Prado’s pioneering work on fractional differencing. While López de Prado’s approach is more carefully and mathematically calibrated to maximize memory while strictly preserving stationarity, VecViz’s structural use of sloped price channels functionally targets a similar conceptual middle ground between raw prices (which have memory but lack stationarity) and raw returns (which have stationarity but lack memory).

Vector Strength, VNA 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 — both seek to identify meaningful structural relationships in price behavior. Furthermore, VNA’s cross-sectional regression adjustment — regressing each ticker’s aggregate Raw VNA Upside upon its Actual Price VecLevel across ~140 tickers — is structurally analogous to a factor-model neutralization step. It removes systematic LLM bias from implied VecLevel scores, leaving the residual narrative-price gap as a clean signal. This approach is consistent with the academic tradition of Fama-French style alpha decomposition, applied here to narrative-derived valuation rather than fundamental factor exposures.

“Standard” deviation coverage: The core channel of a Vector Set — spanning from its anchoring bottom to its anchoring top — can be thought of as approximately ±1 sigma from center. Adding the leveled-up and leveled-down sections extends the full Vector Set to roughly ±3.5 sigma, capturing a range equivalent to approximately seven standard deviations — consistent with the full-spectrum range used in models like Black-Scholes for a one-year forward period.

Central Limit Theorem: VecViz generates hundreds of Vector Sets per ticker. Each can be considered an estimate of the range of price deviation based on a sampled period of price history. Aggregating on a Vector Strength-weighted basis reflects the CLT’s core concept: a sufficiently large collection of samples can reveal characteristics of the full population.

Recency weighting: Ascribing greater Vector Strength to recently formed Vector Sets parallels the well-established principle that sigma-based volatility metrics are improved by applying exponential decay to the lookback window of returns. Both approaches give more weight to recent data.

Additional academic context:

• Academic and industry research supporting the concept of support and resistance is reviewed here.
• Academic research on neural networks and chart imaging supporting the Vector Model and V-Score is discussed here.
• The evolution toward machine learning for volatility estimation and how VecViz relates to it is discussed here.

VecViz views major price Tops and Bottoms as turning points in the net bullish/bearish vector balance driving a ticker’s price. They are the anchors of every Vector Set, the dates from which VecEvents are timed, and key inputs to the Vector Model and V-Score — several of which are direct transformations of Top and Bottom prices, dates, and the angles of the channels constructed from them.

What VecViz did not initially set out to do was incorporate trading volume. Volume is not a direct input to any VecViz model. Yet empirical analysis reveals that Tops and Bottoms tend to occur amidst elevated trading volume: across 141 tickers studied from January 2015 through January 2026, the median ticker showed a Top/Bottom Volume Ratio of 1.41× non-Top/Bottom days. Mann-Whitney testing found this ratio was significantly greater than 1.0 for 63–78% of all tickers studied, depending on confidence threshold.

This matters because trading volume has been a well-regarded quant signal for decades. Clark (1973) and Epps & Epps (1976) established that volume-weighted returns improve volatility modeling. Easley, López de Prado & O’Hara (2012) introduced the “Volume Clock” for detecting informed order flow. Subsequent research has continued to confirm volume’s predictive virtues.

VecViz’s heavy reliance on Tops and Bottoms therefore implicitly channels these virtues — without trading volume appearing anywhere in the model specification. The practical evidence bears this out:

• Tickers with statistically significant Top/Bottom Volume Ratios showed an incremental ~250bps annualized improvement in V-Score long/short performance differential versus tickers without significant Volume Ratios.
• A nearly significant improvement in 95% VaR 1-day performance relative to Sigma was also observed for high Volume Ratio tickers.

The conclusion: by anchoring everything on Tops and Bottoms — the moments when narrative forces reach a tipping point and reverse — VecViz inadvertently tapped into one of the most robust phenomena in quantitative finance. Read the full analysis here.

Convolutional Neural Networks (CNNs) excel at extracting complex, non-linear features from structured data without manual feature engineering. Gramian Angular Fields (GAFs) are a technique for transforming a price (or trading volume) time series into a square matrix amenable to CNN processing. When visualized, GAFs produce plaid-like heatmap patterns and are increasingly employed by quantitative investors for pattern recognition.

Both VecViz and Gramian Fields transform a time series into a structured representation for machine processing, but do so from different starting points. Our research shows that they capture somewhat different features. However, please note that VecViz has not yet developed a commercial product offering based on this GAF/CNN research. It remains an active area of theoretical exploration and internal development. Read the full analysis here.

VecViz is committed to transparency. We publish a report on the performance of each individual VecViz metric, comparing them to Sigma-based metrics wherever applicable, as soon as we have a meaningful amount of performance history to report upon. 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 four years of out-of-sample data.

VecViz analytics are delivered via API into the OpenBB Workspace. Subscription options are available at vecviz.com/vecviz-openbb-api-subscription-page.

For enterprise access, API integrations, or institutional inquiries, please contact us at admin@vecviz.com.

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