Quantitative Engineering

Quant Work & Algorithmic Trading

End-to-end development of proprietary trading infrastructure — from signal research through live execution.

What We Build

Custom execution bots (TWAP, VWAP, iceberg) that minimize market impact across multiple venues. Slippage analysis and fill optimization.

Event-driven simulation engines replaying L2/L3 order book data with realistic fee models, partial fills, and latency modeling. No look-ahead bias.

Gateway-level checks: position limits, drawdown circuit breakers, max order size, and emergency kill switches that halt trading in milliseconds.

Cointegration models, pairs/basket strategies, and automated market-making engines for crypto and equities with inventory risk management.

Feature engineering from microstructure data (order flow imbalance, VPIN, Kyle's lambda), cross-sectional momentum, and alternative data integration.

Low-latency WebSocket feeds, order book reconstruction, VWAP/TWAP calculators, and tick database ingestion (TimescaleDB, ClickHouse).

Python C++ Rust CUDA FIX Protocol WebSocket TimescaleDB ClickHouse Redis Docker Kubernetes

• Trading firms needing proprietary execution infrastructure built from scratch or replacing vendor solutions
• Funds with existing strategies that need production-grade backtesting and risk management
• Teams that want a senior quant engineer embedded for 2-12 weeks, not a junior contractor

• Retail traders looking for a trading bot they can buy off the shelf
• Teams that need regulatory compliance consulting (we build the tech, not the compliance framework)
• Firms without existing market data feeds or exchange connectivity to build on

Week 1

Review existing codebase, data feeds, execution stack, and risk controls. Identify gaps and define the build scope.

Week 2

System design document covering data flow, latency targets, failover strategy, and deployment topology. You approve before code is written.

Weeks 3-8

Production development with weekly demos and test results. Every component ships with unit tests and integration tests.

Weeks 9-10

Deploy to paper trading environment. Validate execution quality, fill rates, and risk controls against live market data.

Weeks 11-12

Go-live with monitoring dashboards, alerting, runbooks, and full documentation for your team to maintain.

We build proprietary quant systems for technology-focused funds. Tell us about your project.