Hire Quantitative Developers for Real-Time Trading & Exchange Systems

Cortance is a hiring platform that matches companies with vetted remote developers from its own network. Its quantitative developers build the system a trading strategy runs on: market data, backtests, order execution and risk limits. As of October 2026, 11 engineers in our network have built the backend of a trading, exchange or trade surveillance system.

When you hire quantitative developers through us, we match on that system rather than on a quant title alone. A strategy that looks profitable in a research notebook can still lose money in production. The cause is usually the plumbing around the model, so that plumbing is what we check in an engineer's project history.

Where does a trading strategy break: in the model or in the system around it?

A trading strategy usually breaks in the system around it. A backtest that ignores fees and slippage reports returns no live account will see. A paper trading run that skips the full order path hides how the exchange API rejects or partially fills orders. A live strategy without exposure limits keeps trading through a drawdown that nobody set a floor for.

Pavlo H. built each of those layers on an algorithmic trading platform for cryptocurrency strategies across several exchanges. His backtesting engine accounts for transaction fees, slippage, position sizing and stop-loss rules. The same platform runs paper and live trading through exchange APIs, with exposure limits, drawdown monitoring and volatility checks in front of every order. Python, pandas, NumPy, Kafka, Celery and PostgreSQL carry the market data and the order flow.

Trading systems our engineers built beyond the strategy itself

Trade surveillance is the same discipline, just viewed from the compliance side. Razmik P. built alerts for layering, spoofing, quote stuffing, wash trading and marking the close on the OneTick tick database, so compliance officers at executing brokers can investigate suspicious order flow.

Ilya B. built an arbitrage engine for decentralized exchanges that simulates thousands of transaction paths inside a single block window. Every path is priced with gas, slippage, swap fees and flash loan costs before anything is sent. Oleh St. spent about five years on the trading platform of a large European online bank, covering shares, bonds, ETFs, crypto and securities lending for retail and professional clients.

AI can read the market, but it should not place the order on its own

Language models now sit inside trading products as analysts and assistants. Mykola B. built a .NET system that detects new Ethereum tokens, filters out scams with custom heuristics and uses a custom-trained LLM to read price patterns and social signals. Artem B. built an AI assistant that helps users write their own algorithmic trading bots in the browser.

In both products the model produces an input, not a trade. A signal from an LLM or a bot written with AI help still has to pass the same backtest with fees and the same exposure limits as any other strategy. We look for quantitative developers who keep that gate in place even when the model is the newest part of the stack.

Tell us the market and the venues

The quickest route to a good match is a short description of your market: the asset class, the exchanges or brokers you trade through and where your market data comes from. Add what already exists, whether that is research notebooks or a production engine, and where your risk limits live today. Cortance uses that to pick engineers who have built a similar system, so you can start within 2 days if the specification is ready.