01 · The VecViz framework

The VecViz framework

Two long-term inputs, one framework, three outputs. The sections that follow take you through each block in turn.

long term chart long term narrative Tops & Bottoms Analyst · Agent Vector Set channels VecEvents timing risk valuation V-Score Price probability VNA Target Price
One framework · dashboards, API, or MCP · human or agent
02 · Vector Set channel

Vector Set channel

Major tops and bottoms typically occur on high volume and carry more signal than other price points. The regression line is the channel center, with standard-error bounds.

top top bottom
  • bounds, ±1 standard error
  • least-squares center
  • price, lifted above the center by a bullish event

1 VecLevel (VL) = 1 standard error = 1 “channel width”

VecViz considers up to 2,028 Vector Set channels per ticker, aggregating them on an expected support and resistance weighted basis to a GMM distribution centered above the current price.

03 · What a VecEvent is

What a VecEvent is

A VecEvent is a dated storyline about the stock, with a direction and a trend that are judged separately. Each event is mapped to every Vector Set whose anchoring tops and bottoms occurred prior to or concurrently with the event.

VecEventDirectionTrend
Regulatory relief for the sectorBullishIntensifying
Central bank easing lowers funding costsBullishSteady
Input-cost inflation squeezes marginsBearishWaning

Bullish / Bearish / Neutral  ·  Intensifying / Steady / Waning

Each event pushes price above or below the center of the channels it maps to.

04 · VecEvents move the target

VecEvents move the target

A VecEvent’s bias, trend and timing determine its channel contribution. The model combines these contributions to update the VNA target.

Event emergence: during or after channel formation

BiasBias trendDuringPost
BullishIntensifyingUpUp
SteadyFlatUp
WaningDownUp
BearishIntensifyingDownDown
SteadyFlatDown
WaningUpDown

Neutral bias: Flat for either timing and any trend

“Flat” treats ±0.0001 VL as effectively zero in this guide.

05 · VNA target price

VNA target price

Vector-Narrative Alignment compares narrative with channel position to generate a target. Across ticker coverage, its implied return is calibrated on average to expected base-case returns over the next 6–12 months.

$100 + 0.5 × $20 = $110 model-date price  +  adjusted upside in VecLevels × price per VecLevel  =  VNA target

Base case = average expected body return. Individual ticker returns vary.

06 · Two LLMs: build and review

Two LLMs: build and review

One LLM generates the baseline VNA estimate. If an expert human cannot review it, VecViz recommends your agent check and supplement the VecEvents via web search and its MCP.

1  Baseline

An LLM catalogs and tags the events. The VNA model prices that baseline.

$108.00

2  Review

A second LLM checks the news, revises tags and adds missing events. The model re-runs.

$104.00

Every change is re-priced by the model

Illustrative review: neutralize an ended cycle, revise a trend, add a risk.

07 · Price probability estimates

Price probability estimates

Four tail bounds plus two body averages, at a chosen horizon. The body spans the prices between 95D and 95U.

Price $100 99D$80 95D$86 EDB$94 EUB$110 95U$125 99U$138
Illustrative prices at one horizon; unequal spacing shows asymmetry.
PointDefinitionHow to read it
99D99% downside bound1% modeled probability below this price.
95D95% downside bound5% modeled probability below this price.
EDBExpected Down BodyMean (base case) downside inside 95D, weighted by probability.
EUBExpected Up BodyMean (base case) upside inside 95U, weighted by probability.
95U95% upside bound5% modeled probability above this price.
99U99% upside bound1% modeled probability above this price.

EDB/EUB are base-case means inside the 95% bounds. Bounds apply at the horizon end, not to interim extremes. Actual outcomes can breach them.

08 · Sigma comparison

Sigma comparison

Sigma supplies a symmetric, normal-distribution benchmark from past returns. Compare it with the Vector Model at the same horizon and percentile to see how the bounds differ.

Illustrative 21D, 99% bounds

0% Sigma −15% +15% Vector Model −10% +25%

The Vector Model incorporates support, resistance and asymmetry by a machine learning model that predicts price movement on the basis of chart geometry in terms of support and resistance traversed.

09 · V-Score

V-Score

A timing signal that compares today’s setup with historical analogs using 13 chart geometry and price probability features. The headline score adds six horizon scores, each from −2 to +2.

Six horizons, one aggregate score

+1 +2 +1 0 -1 +2 1D 10D 21D 63D 126D 252D = +5 example

−12 bearish  ·  +12 bullish

Near zero can mean neutral, mixed, or little discernible signal.

Illustrative examples. Research outputs, not investment advice.
Dashboards and API at vecviz.com ·

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