Company

We build systematic trading strategies through rigorous engineering thinking, viewing markets as complex systems, and empowering strong autonomous teams

Engineering mindset

We define problems clearly, write code to explore them, run simulations, and look at results. Every part of the process is measurable - from model behavior to system latency to logs in production.

Market as a structured system

Markets operate under constraints, dynamics, and feedback. We build models and infrastructure that account for latency, liquidity, volume shifts, and cost. These aren’t external factors — they’re part of the system we’re solving.

End-​to-​end responsibility

We take full responsibility for what we build. From idea to production, we test, deploy, monitor, and improve things ourselves. We enjoy working this way - it’s clear, honest, and rewarding.

Technology Stack

State-​of-​the-​art but pragmatic. We prioritize low dependencies and in-​house systems built for reliability and performance.

C++ logo

C++

Core language for latency-critical components

Python logo

Python

Research, backtesting, and orchestration

PostgreSQL logo

PostgreSQL

Structured data storage and analysis

AWS

Cloud infrastructure and compute resources

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Docker

Containerization and deployment

Docker logo

Grafana

Real-time monitoring and visualization

Grafana logo

Our focus areas

01
Trading

We break down market behavior into measurable problems, build systems to test ideas, run simulations, and evaluate results under real constraints. Every stage is observable — from execution quality to latency, market impact, and production logs.

02
ML

Machine learning in trading isn’t about applying the latest models. It’s about understanding what data can reveal, designing reliable pipelines, and building systems that remain stable under changing market conditions.

03
Infrastructure

Infrastructure isn’t just about speed. It’s about reliability under pressure, observability when systems fail, and architectures that remain maintainable as complexity, scale, and throughput increase.

04
Quant

Quantitative research is driven by evidence, not intuition. We analyze market structure, test assumptions rigorously, and make decisions based on measurable statistical behavior and repeatable results.

While you're reading this, our algorithm has already made about a thousand trades

Contacts

Contacts