Python Trading Research Analyst

We’re looking for a smart, curious, and analytical junior researcher to join our trading team. If you enjoy working with Python, exploring market data, and turning numbers into insights, this role will give you hands-on experience in real-world trading environments and market-making research.

About Freedx

Freedx is a newly established cryptocurrency exchange dedicated to delivering a seamless, secure, and innovative trading experience. Built by a team of seasoned professionals from the crypto, finance, and technology sectors, we’re united by a shared passion for shaping the future of digital finance. Our vision is to become the most trusted and reliable exchange, empowering users with the freedom to manage their digital assets confidently and efficiently. Security, transparency, and scalability are at the core of everything we do. We continuously innovate to meet the evolving needs of traders, offering cutting-edge tools and services that help our clients achieve their financial goals.

Join us as we redefine the standards of digital asset trading and bring true freedom and reliability to the crypto world.

What You Will Do

1. Market Data Analysis

• Analyze orderbook data: spreads, depth, liquidity layers.
• Study market microstructure (order flow, volatility patterns).
• Examine how markets behave across different volatility regimes.
• Research basis, funding, contango/backwardation, cross-exchange deviations.

2. Statistical Modelling

• Build basic statistical models to detect regime shifts and volatility clusters.
• Explore relationships between features and realized PnL.
• Develop simple metrics to evaluate market quality and liquidity.

3. Machine Learning Research

• Apply ML methods to orderbook features and short-horizon price prediction.
• Evaluate models for stability, latency impact, and overfitting risks.

4. Algorithm Analysis

• Test hypotheses and ideas from traders.
• Structure your findings into models that can feed into production strategies.

5. Backtesting & Simulation

• Build lightweight simulation environments for MM and directional strategies.
• Test strategy variations under different market assumptions.
• Model hedging effectiveness across exchanges/instruments.
• Run scenario-based stress tests (vol spikes, price dislocations, liquidity dry-ups).