Real-Time Engine

Quant Engine

7-layer depth architecture for real-time quantitative analysis. Data ingestion through execution — every layer visible, testable, replaceable.

Depth Architecture

Seven layers from raw data to executed orders. Each layer is independently deployable, testable, and monitorable.

Data Ingestion

Multi-source data feeds — market data, alternative data, internal signals — normalized to a common schema.

streaming

Normalization

Time-series alignment, outlier detection, missing data interpolation, and cross-source reconciliation.

pipeline

Feature Engineering

Derived features, rolling statistics, cross-asset correlations, and regime detection indicators.

compute

Signal Generation

Quantitative models produce directional signals with confidence intervals and decay schedules.

models

Risk Filtering

Position sizing, exposure limits, correlation checks, and drawdown constraints applied pre-execution.

guardrails

Execution

Order routing with smart splitting, venue selection, and real-time slippage monitoring.

live

Reporting

P&L attribution, factor decomposition, risk reporting, and audit trail generation.

output

Every layer replaceable

Swap any layer without touching the others. Interfaces are contracts, not suggestions.

Every layer testable

Each layer has its own test harness. Backtest any configuration against historical data.

Every layer visible

Real-time dashboards per layer. If something breaks, you know which layer and why.