I focus on data-driven research in financial markets, combining statistical analysis, programming, and disciplined experimentation to study market behavior and develop systematic trading ideas. My background in software engineering supports a rigorous, reproducible approach to quantitative research.
- Quantitative & systematic trading strategies
- Market data analysis and signal research
- Time-series modeling and statistical inference
- Risk, return, and portfolio-level evaluation
- End-to-end research pipelines: idea → data → test → refine
- Python (research, data analysis, backtesting)
- SQL (market & alternative data handling)
- JavaScript
- C / C++ (systems & performance fundamentals)
- Pandas, NumPy
- Statistical analysis & probability theory
- Time-series analysis
- Data visualization (Matplotlib / Plotly)
- Custom research & backtesting frameworks
- Linux
- Git & GitHub
- MySQL, MongoDB
- Equities
- Crypto-assets
- Market factors & cross-sectional signals
- Volatility & return dynamics
- Markets are probabilistic systems, not deterministic ones
- Emphasis on process over prediction
- Preference for simple, explainable models before complexity
- Risk management is integral, not an afterthought
- Continuous iteration based on data, not narratives
- Hacktoberfest 2023 – Open Source Contributor
- Interested in collaborating on quant research, data analysis, and research tooling
- Email: rahulmeenaoffical@gmail.com
- GitHub: https://github.com/iamrahulmeena
- LinkedIn: https://linkedin.com/in/iamrahulmeena
Systematic edges emerge from disciplined research, robust testing, and respect for uncertainty.