Quantitative Trader

QNT Partners; Greater London; On-site; GBP 120,000 - 190,000; Full time

Job summary

QNT Partners in London seeks a Quant Trader who can take a strategy from idea to live risk. You’ll own the edge from hypothesis to execution, balancing research sophistication with robust production engineering.

You’ll write production-grade Python (and selective C++ for latency-sensitive components), backtest with realism, shadow-trade, and help evolve data pipelines and risk controls across the trading stack.

Qualifications

• Proven ability to turn research ideas into live, risk-managed strategies.
• Experience writing production Python, with C++ for latency-critical paths.
• Strong backtesting discipline with walk-forward testing.
• Ability to extract signals from tick or order-book data.

Responsibilities

• Own strategy from idea to live risk.
• Code production systems and run live experiments to measure edge quality.
• Backtest properly with realistic costs and no look-ahead.
• Review attribution and update dashboards daily.

Skills

Python production

C++ latency optimization

Quant research

Backtesting

Tools

Python

C++

Job description

Quant Trader – Build, Ship and Run Strategies

We’re working with a high-performance quant trading firm that trades liquid markets with their own capital. No client money, no layers of approval and no “research that never goes live.”

What you will actually do every day will involve owning a strategy from idea to live risk.

• Morning (pre-open to open): Pull the latest overnight data, check feature drift, re-fit or re-weight models if needed, and decide size and risk limits for the day. You watch the book go live and catch any execution anomalies in the first 30 minutes.
• Core of the day: Code. You write production Python (and some C++ when latency matters) that turns research into signals, risk checks, and execution logic. You run controlled experiments on live data, measure slippage and adverse selection in real time, and kill or scale ideas based on evidence—not opinions.
• Afternoons: Deep work on the next edge. Feature engineering on tick or order‑book data, alternative data pipelines, regime detection, or portfolio construction across related strategies. You backtest properly (no look‑ahead, realistic costs, walk‑forward), then shadow‑trade before full capital.
• End of day / post‑close: Review the day’s attribution, update dashboards, write a short note on what worked and what didn’t, and decide tomorrow’s research priorities. Once a week the whole team reviews open research and live performance together—no PowerPoints, just code and numbers.

Who this is for?

You should be strong at both the research and the engineering, which would look like moving from a messy data idea to a clean, monitored production system without hand‑holding. You care more about the quality of the edge and the robustness of the system than about fancy titles or conference talks. You’ve probably already built and run strategies (personal, academic, or previous firm) and know the difference between a backtest that looks good and one that survives contact with the market.

Why people stay?

• Direct line from your code to P&L. When a strategy works, you see it in the numbers the same day.
• Autonomy; you propose the idea, build it, size it, and own the risk parameters within clear firm limits.
• Small team of people who are better than you at something.
• Capital is available when the research is ready; they don’t sit on good ideas waiting for budget cycles.
• Compensation is high and tightly linked to contribution. Base is competitive; the real upside is meaningful participation in the strategies you help create and run.
• Tools and infrastructure are first‑class. Clean data, fast research environments, proper monitoring. They invest in removing friction so you can focus on the hard problems.