I build end-to-end quantitative research systems: raw data ingestion, factor modeling, backtesting, and risk analysis.
My main track is quant.
My secondary track is cybersecurity, with an interest in secure and reliable systems.
I care about:
- extracting signal from noisy markets
- reproducible research pipelines
- modeling real-world constraints instead of toy results
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Reconstructed a survivorship-bias-free S&P 500 history, replicated the 12–1 momentum strategy, modeled transaction costs, and ran CAPM / FF3 / FF5 + UMD regressions with Newey–West errors. |
Multi-strategy backtesting engine with automated runs, reproducible logs, and risk analytics including Sharpe, drawdown, and validation workflows. |
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Event-driven hospital operations model built from real workflow observations, designed to surface bottlenecks earlier than manual monitoring. |
A central place for projects, research, background, and the bigger story behind what I build. |
[2026-03] studying factor behavior under crash/reversal regimes
[2026-03] improving turnover-aware backtesting assumptions
[2026-02] refining reproducible research workflows and reporting
[2026-02] exploring security as a systems-level complement to quant infrastructure
languages python | r | html/css
libraries pandas | numpy | matplotlib | statsmodels
tools git | github actions | alpaca api | excel
methods time-series | econometrics | backtesting | simulation
- published quantitative research
- built reproducible backtesting pipelines from scratch
- interested in alpha research, market structure, and robust systems
- exploring cybersecurity through a systems-and-infrastructure lens
- Portfolio: https://dshan12.github.io
- GitHub: https://github.com/dshan12
- Email: darshansathishkumar@gmail.com
markets are adaptive systems. edge comes from structure.