PLATFORM

Architecture

Technical infrastructure powering Decision Lab. Purpose-built for quantitative finance computation with institutional-grade security.

System Layers

Presentation Layer
React SPA Interactive Charts Jupyter Interface PDF Export
API Layer
FastAPI Gateway GraphQL Federation WebSocket Streams Rate Limiting
Compute Layer
NumPy / SciPy cvxpy Optimizer QuantLib Ray Distributed
Data Layer
PostgreSQL TimescaleDB Redis Cache S3 Storage
Security Layer
OAuth 2.0 / OIDC RBAC Engine Audit Logging Encryption at Rest

Technology Stack

COMPUTATION

Quantitative Engine

High-performance numerical computing for valuation, optimization, and risk analytics.

Python 3.11 NumPy SciPy cvxpy QuantLib
INFRASTRUCTURE

Cloud Platform

Scalable infrastructure with auto-scaling compute and managed services.

AWS Kubernetes Docker Terraform
DATA

Storage & Caching

Time-series optimized storage with in-memory caching for real-time analytics.

PostgreSQL TimescaleDB Redis Parquet
INTERFACE

User Experience

Modern web application with interactive visualizations and notebook integration.

React TypeScript D3.js JupyterHub

Design Principles

01

Computation Correctness

Every calculation is validated against textbook formulas and reference implementations. Unit tests cover edge cases including singular covariance matrices and extreme parameter values.

02

Audit Trail

Complete logging of all inputs, parameters, and outputs. Every decision can be reconstructed and explained to regulators or stakeholders.

03

Fail Explicit

No silent failures or default values that mask errors. When something goes wrong, the system stops and tells you exactly what happened.

04

Reproducibility

Identical inputs produce identical outputs. Random seeds are logged, environments are containerized, and dependencies are pinned.

Explore the Platform

See the architecture in action. Build production-grade quantitative systems.

Request Access