Market Microstructure · Signal Surveillance

Real-time algorithmic signal surveillance for microstructure research

Stockurai continuously monitors the U.S. equities market in real time, evaluating modular multi-condition algorithms against live multi-timeframe data and generating structured, labeled signal events — each capturing the full market state, every condition evaluation, and multi-interval outcomes. The result is a live laboratory and growing dataset for market microstructure researchers, ML practitioners, and analysis-driven traders and investors who work from evidence rather than narrative.

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Four Perspectives

One platform, four audiences

Stockurai serves academic researchers, institutional quant teams, decision science practitioners, and retail traders. Each sees different value in the same core architecture.

Perspective I
The Academic Laboratory
A live laboratory for observing microstructure phenomena as they happen. Students dissect real signal events, experiment with parameters in the Optimization Lab, and watch multi-timeframe dynamics unfold during market hours.
Market Microstructure Classroom Tools Live Observation
Perspective II
The AI Training Data Pipeline
Every signal event is a labeled training example — structured market state, condition evaluations, and multi-interval outcomes. Feed directly into ML pipelines for alpha research, execution optimization, or risk modeling.
Labeled Signals ML Training Data Feature Engineering
Perspective III
The Decision Science Template
A live augmented decision system and replicable architectural template. The instance is equities trading. The pattern — real-time monitoring, structured decision logic, correlative AI training data capture — is universal.
Augmented Decisions Bounded Rationality Domain-Agnostic
Perspective IV
The Retail Signal Engine
Systematic signal generation at institutional scale for individual traders. Multi-condition algorithmic plays, real-time surveillance across the equities market, and transparent audit trails for every signal.
Signal Generation Trade Snapshots Optimization Lab
Continuous Data Generation

What the platform captures

Stockurai is not a static dataset. It is a live system continuously generating structured, labeled data across every layer of its operation.

Market Statistics
real-time · multi-timeframe
A vast quantity of market statistics captured across multiple timeframes simultaneously — price, volume, volatility, momentum, structural levels, and derived indicators
Algorithmic Evaluation
unlimited plays · modular logic
An expandable library of algorithms that evaluate this data in real time, each applying structured multi-condition decision logic to the incoming market state
Signal Events
labeled · timestamped
The resulting signals — structured, labeled events capturing the full environmental state, every condition evaluation, and the raw values that produced each Boolean outcome
Machine-Based Decisions
systematic · rule-driven
Hypothetical decisions generated automatically from signal logic — what a purely systematic approach would do at every signal event, with no human intervention
Human Intervention Decisions
discretionary · behavioral
The human layer — where traders override, filter, or act on signals, creating a parallel decision record that captures the gap between systematic and discretionary judgment
End-to-End Audit Trail
complete cycle · outcome-labeled
The full lifecycle of each signal from market state through algorithmic evaluation, signal generation, decision (machine or human), execution, and final outcome — a complete, auditable record of every cycle
Research Applications

Questions this dataset supports

Structured signal data with labeled outcomes enables research that raw market data cannot support.

Market Microstructure

Under what conditions do multi-condition signal convergences predict short-term price movement?
How does signal information content vary across trending, mean-reverting, and volatile market regimes?
What is the relationship between volume-price dislocation signals and subsequent price discovery?

Machine Learning in Finance

Can models trained on structured signal features outperform those trained on raw OHLCV features?
What is the marginal value of multi-timeframe context in ML feature sets?
How do gradient boosting, LSTM, and transformer architectures compare on structured signal data?

Decision Science

Does adding conditions to a decision rule improve outcomes, or introduce overfitting and decision paralysis?
How does structured algorithmic decision support reduce measurable cognitive bias in trading?
What is the cost of overriding systematic logic with discretionary human judgment?
Architecture

Five domain-agnostic layers

The architecture that powers Stockurai is a replicable template for any augmented decision system. The instance is financial. The pattern is universal.

LAYER 01
Real-Time Ingestion
Continuous WebSocket feed normalized, timestamped, and routed for processing
LAYER 02
Multi-Resolution Aggregation
Raw data aggregated across multiple temporal resolutions simultaneously
LAYER 03
Structured Decision Logic
Modular, testable multi-condition rules evaluated in real time
LAYER 04
Signal Generation
Transparent, auditable signal events with full environmental state
LAYER 05
Training Data Pipeline
Every decision event is simultaneously a labeled training example

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Stockurai is currently available by request. Tell us briefly about your research focus or trading approach and we’ll set up your account.

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