LAYER A: BLUEPRINT EXTENSION (System Mechanics & Metric Pairing)

.
The Genesis & The Package: The formulation of the dataset QUERY begins with the primary task of capturing a static historical event snapshot bounded within a fixed one-year calendar envelope. To format the MnM Package, this requires selecting two distinct, symbiotic daily metrics that form a natural pairing to represent that specific 366-day historical instance.

The Natural Metric Pairing (M1 & M2): To create useful geometry, the QAE Engine requires useful data possessing real-world meaning. The selection of metrics is not strictly constrained to specific data types; rather, the objective is capturing a good, repeatable pairing that establishes a known relationship. It does not matter if the pairing is a traditionally learned market behavior or a fundamentally robust physical law. Provided the pairing makes sense to the Baseline Lens and Reaction Lens, capturing this natural relationship creates the geometric value within the QAE Report.
M1 (Baseline Lens) — Absolute Environmental State: Metric 1 defines the absolute state, physical volume, or absolute environmental participation over the bounded one-year calendar envelope. It acts as the structural landscape, capturing the absolute cardinal magnitude of the event to anchor the environment of the dataset.
M2 (Reaction Lens) — Daily Velocity of Change: Metric 2 must include a daily measurement strictly associated with the calendar. It defines the daily reaction as the response or velocity of change relative to the prior state (t-1). *(During Layer B Computation, M2 is deterministically processed through the Metric Reshaper formula—[Current State - Prior State] / Prior State—which extracts the pure proportional intensity of the daily reaction from the immediately preceding active day)*.

Handshake 1 (MnM Gate): The submission of the MnM Package at the gate. Validated ingress officially activates the SIGNAL PIPELINE, moving the data from the External Domain into the Machine Domain.
.

LAYER A: USER INSTRUCTIONS (The Dataset Query)

.
The Full System: In the full system architecture, a dataset QUERY begins with a User experiencing an 'aha moment' that generates a query to solve a problem. With QAE, that Query Dataset contains deterministic cardinal data bounded within a fixed one-year calendar envelope, using two symbiotic metrics to capture a historical event snapshot. The User invests time, energy, and intent to select the timeframe and a natural metric pairing—M1 Baseline and M2 Reaction—driven by precise knowledge of the relationship between those two metrics. To create geometric value, this pairing must establish an interaction between the absolute environment as a whole (M1 Baseline Lens: the absolute state over the year) and the intensity of the resulting daily movement (M2 Reaction Lens: the daily velocity of change relative to the prior state).

The M1 Baseline Lens simply applies ordinal ranking to the metric data and bins it into five symmetric 20% Q-Bands named Weak | Moderate | Median | Strong | Peak.

The M2 Reaction Lens is the Daily Velocity of Change, which you will see in Layer B. M2 is deterministically processed through the Metric Reshaper. This M2 metric should contain patterns at the daily time interval regarding the velocity of change, response intensity, or proportional violence relative to the prior state (t-1).

The Current Beta Status: In this early launch phase, the datasets for the demos have already been curated and processed for you. Even so, you should absorb what you can from the QAE Dataset Summary provided with each report. The more you understand the raw numbers going into the machine, the better you will understand the visual geometry coming out of the machine.

The QAE Engine only provides deterministic structure; it does not assign meaning. To extract true insight, it is highly beneficial to conceptually own the prepared SIGNAL before it enters the pipeline at Layer A. When you understand the event snapshot timeframe, both metrics, the metric pairing, Layer B math, and the Cheat-Sheet, you naturally own the interpretation of the geometric patterns in the QAE Report.

LAYER B: QAE COMPUTATION (The Machine)

.
Metric: A non-negative, measurable value derived from physical reality or industry participation (e.g., Volume or Price).
Cardinal Value: An absolute, independent measurement that answers the question "How much?".
Bipolar Value: A signed numerical result (+ or -) generated by the Metric Reshaper formula before it is normalized into a non-negative magnitude for ranking.
Directional Polarity: The independent visual binary (▲ Green / ▼ Red) extracted from a Bipolar Value that anchors the direction of a move without contaminating its ranked intensity.
Ordinal Value: A relative structural rank (e.g., Weak to Peak) that answers "Compared to what?" within a closed 365/366-day envelope.
Ordinal Geometry: The stabilized visual arrangement of ordinal values into fixed architectural coordinates and time-indexed containers.

The Ingress - Lens Classifications M1-BL & M2-RL: The inbound dataset package must be a 3 column metrics data-table | Date | Metric 1 BL | Metric 2 RL | . The package only needs to include Active Days, and metadata required is a Newest Date and a UTC Timezone offset. The title and logo are useful and optional metadata. Datasets can be encrypted by multiplying the metrics with a complex number. The Ordinal Geometry holds pattern, although the metrics overlays would have distorted values.

Computation transforms measurable real world metrics into quantile structure using time-indexed ordinal geometry with overlays. The metrics one-year daily interval dataset contains Metric 1 Baseline Lens and Metric 2 Reaction Lens. This metrics pairing enters the computation pipeline as a Deterministic Cardinal Historical Snapshot, not a data-stream. QAE Engine processes both - Metric 1 is processed directly through the Baseline Lens to establish the absolute environmental state - While Metric 2 is routed through the Metric Reshaper creating Bipolar Values as reaction intensity relative to the prior state (t-1), additionally also preserve directional polarity binaries. As illustrated in the Reaction Lens Classification map, this allows QAE Engine to process the dataset from two distinct angles of reality.

The Physics of Reverse Chronology: The QAE Engine operates using Reverse Chronology as a design choice which can be changed later on if required. The Newest Date serves as the primary temporal anchor for the dataset envelope and may be either an Active or Inactive Day. The computation anchors to the Newest Date from the metadata and reaches backward through formula exactly 1 year to establish the Oldest Date. This temporal anchor ensures that every Q-Band Count and seasonal distribution is derived from a closed historical envelope, preventing the engine from "hallucinating" trends, blending or looking into the future.

Handshake 2 (Ordinal Geometry Data-tables): Upon the completion of the cardinal-to-ordinal transposition, the machine generates the Ordinal Geometry Data-tables. These flat data-tables contain the finalized Q-Band Counts, Metric Overlays, and Directional Polarity Bias Overlays. This marks the internal handover (Handshake 2) where the Computation is exported to Layer C for visual rendering.
.

LAYER B: USER ORIENTATION

.
Day Qualification Logic: The QAE Engine recognizes three distinct day types. Active Days are daily observations where both metrics are present and are processed by the engine. Inactive Days (weekends or holidays) are skipped without breaking the mathematical sequence; the "Prior State" (t-1) is always the immediately preceding Active Day. Disqualified Days, where a single metric is present and another is missing, are rejected as invalid at the submission gate, and the MnM Package will be declined via an error report.

The Core Math (Cardinal to Ordinal): QAE Engine focuses on structural reality. It deterministically transposes cardinal measurements into Ordinal Geometry using five symmetric 20% Q-Bands: Weak | Moderate | Median | Strong | Peak. The QAE Engine creates time-indexed geometry where each of the Q-Bands is tested against various fixed timeframes to create the Q-Band Counts rendered in the final charts and tables.

The M2 Metric Reshaper Pipeline: To measure the proportional violence of transitions, the M2 raw metrics are transformed using the formula: (Current State - Prior State) / Prior State. As shown in the Reaction Lens Classification map, this formula creates a bipolar value encoding both magnitude and direction. The engine then normalizes these values to all be positive values, ensuring the system ranks pure Proportional Violence—treating a massive crash and a massive spike as identical "force" for quantile placement.

Directional Polarity Decoupling: Before ranking the intensity, the engine extracts the mathematical sign (+ or -) as an independent Directional Polarity Overlay (▲ Green for up, ▼ Red for down), this facilitates Directional Polarity Counts for vaious timeframes. This separation ensures that directional bias never contaminates or masks the ranked intensity of the reaction.

Layer C: Visual Rendering & QAE Report Handover

.
Layer C documentation is not yet completed, so for the time being here are important bullet points. This section will be completed soon. It will be extended with a comprehensive Container Map, detailing the exact layout of all pages, containers, and elements.
.

• Layer C performs visual panel construction picking up from Layer B computation. It is strictly an assembly phase, translating the computed ordinal data-tables from Layer B into a fixed layout digital display panel.
  This representational process organizes the deterministic outputs using structured combinations of data, symbols, containers, badges, aesthetic color systems, and spatial arrangement. See QAE Cheat-Sheet.
• The fixed layout relies on placement discipline, assigning coordinates to elements so the structure makes sense and becomes easier to cognitively engage with after some sessions.
• Container Architecture utilizes specific borders and badges to securely isolate data scope and prevent unrelated structures from blending together.
• Invariant visual grammar relies on strict aesthetic color systems: Purple for the Baseline Lens ◯_BL_M1, Blue for the Reaction Lens △_RL_M2, and dedicated colors for fixed timeframe badges and borders.
  The 5-tier ranked Q-Bands are aesthetically displayed using a consistent light-to-dark color gradient ranging from Weak to Peak.
• Directional Polarity Indicators (up/down or higher/lower) are placed as ▲ ▼ Arrows in M2 Q-Bands, while charts display Directional Polarity Counts utilizing Green for a positive move and Red for a negative move. This physical placement ensures Directional Polarity never visually or mathematically contaminates the quantile output in the ordinal geometry.
• Template Skins allow domain-specific semantic labels (such as "Volume" or "Mean Wind") to be applied to the surface without altering the underlying mechanics of the engine.
• Layer C officially terminates at Handshake 3 (QAE Report Handover), exporting the static QAE Report Artefact PDF and transferring full operational responsibility outward to the User for interpretation Layer D.

Layer D: User Interpretation

.
Layer D documentation is not yet completed, so for the time being here are important bullet points.

Part 1: Subconscious Parsing

Subconscious parsing is built on reliable, repeatable patterns. When someone learns to drive, they spend time normalizing to the controls and displays. After a few sessions, drivers begin to operate automatically — able to daydream or multitask occasionally, then later on with more practice multitask frequently. This happens because the ergonomic design of car controls aligns with the human body, allowing the subconscious to take over routine tasks.

A similar thing happens when learning one new language, after a certain amount of practice learners start thinking in that new language without the mental load of translation to the default language because their subconscious became structured enough to bypass using the previous dominant default language.

A similar principle applies to QAE. It projects a consistent, repeatable layout geometry that the subconscious can quickly internalize. By enforcing an invariant visual grammar through strict placement discipline, the architecture establishes a stable perceptual field. After some sessions and usage, people start instinctively knowing what they are looking at and where everything is. This biological division of labor assists rapid re-engagement, allowing the brain to significantly reduce searching for where information lives thereby freeing the mind to concentrate on data content and the underlying query.
.

Part 2: Interpretation & System Support

.

• The Boundary of Interpretation: The machine's mechanical responsibility strictly terminates at QAE Report Handover, marking the point where full operational responsibility returns to the User. The engine builds geometry and delivers a report. It does not offer advice, generate predictive models, or assign value. Meaning is entirely the User's domain.
• System Support & Education: Because the engine only provides deterministic structure and never interprets the data, the QAE ecosystem currently provides early version support and educational articles alongside the report. These resources will grow and improve.to assist Users in understanding the dataset details and the underlying mechanics needed to decode the messages and meanings within QAE Reports..
• Conscious Deduction: Interpretation is the conscious act of translating geometric patterns into actionable, real-world consequences based on specific domain knowledge. The system provides the visual invariant, while the User provides the deduction.
• Connecting Dual Realities: The true power of interpretation happens when the User connects relative Ordinal Position (the Q-Bands) back to absolute Cardinal Magnitude (the raw metric text overlays). Because the QAE Report displays both realities together for total contextual awareness, inspecting a Peak Q-Band rank paired with a historically tiny absolute raw metric reveals that the entire underlying dataset is tightly compressed.
• Pattern Recognition & Systemic Conviction: With the noise of absolute scale removed, the conscious mind is completely free to focus on structural anomalies and behavioral clustering. For example, observing a day where a Peak Baseline rank perfectly aligns with a Peak Reaction rank reveals a moment of massive systemic conviction.
• Seasonal Recurrence & Decay: By engaging with the historical viewports, the User can observe specific clusters of bands occurring in the exact same calendar month across Back-Stitched years. This allows the User to interpret reliable seasonal decay, persistence patterns, and macro regime shifts across an entire decade.

QAE Cheat-Sheet

Click the links to open in a new tab or right click to 'save file as'

Download PDF | Download Image 2560px Tall | Download Image 4000px Tall