Case File: fintech-risk-engine#FinTech#High-Performance

High-Frequency Financial Risk Engine

Client

QuantLeaf Capital

FinTech Startup

Primary Objective

Legacy risk assessment models were too slow for real-time trading, leading to missed opportunities and exposure.

Impact Protocol

Reduced latency from seconds to milliseconds. Enabled real-time risk checks for over 1M+ daily transactions.

The Challenge

QuantLeaf Capital needed to execute trades in microseconds, but their legacy risk engine (written in pure Python) added a latency overhead that made high-frequency strategies impossible. They needed a system that could compute Value at Risk (VaR) and Greeks in real-time without stalling the order execution pipeline.

Our Approach

We rewrote the core mathematical models in highly optimized C++17, utilizing SIMD instructions for parallel number crunching. We exposed these core kernels to the Python application layer using pybind11, allowing their data scientists to keep working in Python while enjoying C++ speeds. On the frontend, we built a WebSocket-driven React dashboard to visualize risk exposure in real-time.

The Results

Achieved sub-millisecond latency (p99 < 800us) for risk checks.

Scaled to handle 1M+ daily transactions without jitter.

Zero downtime deployment architecture.

Empowered traders with real-time visibility into portfolio Greeks.

Tech Stack

C++17

Python

React

WebSockets

Redis

PostgreSQL

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