About the job
Join our innovative trading team as a Python Trading Research Analyst! We are seeking a highly intelligent, inquisitive, and analytical individual who has a passion for Python programming and market analysis. This role offers the opportunity to gain hands-on experience in real-world trading environments and engage in market-making research, transforming data into actionable insights.
About Freedx
Freedx is a cutting-edge cryptocurrency exchange that aims to provide a secure, seamless, and pioneering trading experience. Founded by a team of experienced professionals from the finance, technology, and cryptocurrency sectors, our mission is to shape the future of digital finance. We aspire to be the most trusted exchange, empowering our users to manage their digital assets with confidence. Our core principles are security, transparency, and scalability, and we continuously innovate to meet the dynamic needs of traders, offering state-of-the-art tools and services to help clients achieve their financial objectives.
Become a part of our vision as we redefine the standards of digital asset trading, delivering true freedom and reliability in the crypto space.
Your Responsibilities
1. Market Data Analysis
- Conduct comprehensive analysis of order book data, including spreads, depth, and liquidity layers.
- Examine market microstructure, focusing on order flow and volatility patterns.
- Investigate market behaviors across various volatility regimes.
- Research basis, funding, contango/backwardation, and cross-exchange deviations.
2. Statistical Modelling
- Create basic statistical models to identify regime shifts and volatility clusters.
- Analyze relationships between features and realized PnL.
- Develop simple metrics to assess market quality and liquidity.
3. Machine Learning Research
- Implement ML methods for order book features and short-horizon price predictions.
- Assess model stability, latency impacts, and risks of overfitting.
4. Algorithm Analysis
- Test hypotheses and ideas derived from traders.
- Organize findings into models for integration into production strategies.
5. Backtesting & Simulation
- Construct lightweight simulation environments for market-making and directional strategies.
- Evaluate strategy variations under different market conditions.
- Model hedging efficacy across exchanges and instruments.
- Conduct scenario-based stress tests for price spikes, dislocations, and liquidity shortages.
