Systems/Warehouse

The structured market intelligence layer.

The Warehouse is not a dashboard or a database overview. It is the system that turns raw market data into normalized, reproducible intelligence — and then reasons over that structure to describe how the market is actually behaving.

Open the platform Trade Engine

Warehouse pipeline · deterministic stagesreproducible

Ingestion

raw feeds in

Standardization

canonical schema

Interpretation

structured reasoning

Outputs

derived datasets

I/Purpose

A foundation for every downstream analysis.

The Warehouse is the platform's structured market intelligence layer. It transforms raw financial data into usable, normalized information and serves as the single foundation that every downstream analysis, classification, and interpretation is built on.

Single source

One governed record that downstream systems read from, instead of many divergent copies.

Normalized by default

Raw observations are converted into consistent internal representations before use.

Reproducible

A dataset defined today resolves to the same record when queried later.

Warehouse pipeline · deterministic stagesreproducible

Ingestion

raw feeds in

Standardization

canonical schema

Interpretation

structured reasoning

Outputs

derived datasets

II/Ingestion layer

Real-time ingestion, cleaned at the door.

External market data feeds are ingested in real time, standardized on arrival, and put through cleaning, deduplication, and normalization before anything else reads them. The result is a consistent internal format regardless of vendor-side differences.

Ingestion · stream processing● live

equities.taqreceived1,284,002 msg/s

options.oprareceived742,118 msg/s

flow.consolidatednormalizing— dedup 0.02%

reference.symbologyreconciled— canonical map

Inbound feeds are validated, deduplicated, and converted to internal formats on arrival.

Real-time feeds

Continuous capture of external market data as it is published.

Cleaning & dedup

Malformed and duplicate records are removed before they can propagate.

Format conversion

Every stream is mapped to consistent internal representations on arrival.

III/Standardization layer

A canonical schema that holds over time.

Standardized data is mapped to a canonical schema for financial instruments, with explicit time-series consistency rules and symbol/asset normalization. The objective is reproducibility: the same dataset definition resolves to the same record, independent of vendor format drift.

Canonical instruments

A stable instrument identity that survives vendor and symbol changes.

Time-series consistency

Ordering and timestamp rules that keep history aligned across sources.

Reproducible datasets

Point-in-time pinning so historical queries never silently change.

Canonical schema · instrument recordv · pinned

Field	Type	Rule
instrument_id	canonical	stable across vendors
event_time	utc_nanos	monotonic per stream
symbol	normalized	corporate-action aware
source_rev	lineage	point-in-time pinned

IV/Interpretation layer

Structured reasoning over market behavior.

This is the core of the Warehouse. Over the normalized record, the system models liquidity behavior, order-flow imbalance, and participant response, then detects regime — trend, volatility expansion, mean reversion — as a probabilistic read of market structure. It is a structured reasoning system, not storage.

Interpretation · market structure stateINFERRED

Flow interpretation

Order-flow imbalance+0.34

Liquidity depththinning

Participant biasaccumulating

Sweep pressurebuilding

Regime detection

Trend

64%

Volatility expansion

41%

Mean reversion

28%

Probabilistic state, not a directional call.

Liquidity & flow

How depth forms and thins, and how order-flow imbalance shifts around levels.

Participant behavior

Inference of accumulation, distribution, and forced versus chosen activity.

Regime detection

Probabilistic classification of trend, volatility expansion, and mean reversion.

V/Analytical outputs

Derived signals, ready for downstream reasoning.

The interpretation layer emits structured outputs: derived signals, computed features, and market-condition classifications. These feed trade-decision evaluation — explicitly non-execution — and the structured datasets that downstream reasoning depends on.

Analytical output · structured record● non-execution

{
  "symbol": "SPY",
  "as_of": "2026-06-03T13:45:11Z",
  "regime": "compressed_vol",
  "flow_imbalance": 0.34,
  "classification": "accumulation",
  "source_rev": "a3f9c1"
}

Derived signals and condition classifications for downstream evaluation — not order routing.

Derived signals & features

Computed measures produced from the normalized record.

Condition classifications

Market states labeled for consistent downstream consumption.

Decision evaluation

Inputs for evaluating trade decisions — without executing them.

VI/System philosophy

Deterministic processing, separated cleanly from interpretation.

The Warehouse is built on deterministic processing and reproducibility. Raw data is preserved as raw data; interpreted layers are versioned separately; and every conclusion traces back along a defined transformation path. Structured reasoning is kept distinct from raw observation.

Deterministic

The same inputs and definitions yield the same outputs, every time.

Raw vs interpreted

Observation and inference are stored and versioned as distinct layers.

Traceable

Every interpreted value resolves back to the source revision behind it.

Raw observation and structured interpretation are different things, and the Warehouse never lets them blur. Observation is preserved exactly as received; interpretation is a separate, versioned layer built on top of it. That separation is what makes a conclusion defensible — you can always walk it back to the data that produced it.

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