I am a quantitative finance student with background in computer science, machine learning, and statistical modelling, with a growing focus on systematic trading, alpha research, and market microstructure.
My core interest are in integrating stochastic modelling, econometrics, and machine learning into coherent research pipelines.
- End-to-end alpha research workflow:
- Idea generation → expression templating → simulation → filtering → storage
- Custom simulation orchestration using WorldQuant BRAIN API
- Correlation-aware retry logic and performance-based ranking
- Local database ingestion for research-scale experimentation
- Deep learning models applied to financial time-series and limit order book data
- Comparative analysis: linear models vs deep sequence models
- Focus on when ML adds signal, not just whether it can fit data
- FastAPI backends for research tooling
- Async pipelines, logging, reproducibility
- Dockerised development environments
- Systematic trading & signal research
- Stochastic Control in Discrete Time
- Market microstructure
- Regime-aware models
- Robust model validation
- Agentic AI for research workflows
- Combining theory with empirical ML
- Portfolio Danruksujarit.com
- LinkedIn: Thiraphat Ruksujarit
- GitHub: You’re already here
“Prediction is cheap. Understanding is expensive.”