Python Trading Research Analyst

Freedx is a newly established cryptocurrency exchange dedicated to providing a seamless, secure, and innovative trading experience. The team comprises experienced professionals from the crypto, finance, and technology sectors. Freedx aims to be the most trusted and reliable cryptocurrency exchange, focusing on security, transparency, and scalability to empower clients in managing their digital assets.

• Strong proficiency in Python, especially pandas, numpy, scipy, sklearn, statsmodels.
• Understanding of machine learning techniques for time series or microstructure-style data.
• Solid foundation in statistics: distributions, variance, regressions, probability.
• Experience building models for prediction, classification, or clustering (projects, coursework, or work experience).

Nice to Have:

• Experience with deep learning for timeseries (PyTorch or TensorFlow).
• Knowledge of PostgreSQL/TimescaleDB for large dataset queries.
• Experience analyzing tick-level or Level 2 data from Binance/KuCoin/Bybit/etc.
• Exposure to crypto markets or market-making concepts.

1. Market Data Analysis:

• Analyze orderbook data: spreads, depth, liquidity layers.
• Study market microstructure (order flow, volatility patterns).
• Examine market behavior 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 findings into models for production strategies.

5. Backtesting & Simulation:

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