Pratik Kale | Quant Portfolio

Pratik D. Kale

Quantitative Research • Cloud Engineering • Financial Modeling

I am a quantitative researcher and cloud engineer focused on turning market data into reliable signals and production systems. My work spans dynamic term-structure modeling, options hedging, anomaly detection, and reinforcement learning for execution—shipping models with clear risk controls and reproducible results.

Beyond finance, I build resilient data pipelines and APIs, moving from research notebooks to services with disciplined backtesting and automation. Prior work includes engagements with NITI Aayog, DRDO, and NIC, alongside leadership in technical festivals and community initiatives.

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linkedin.com/in/pratikdkale • github.com/pratikdkale • pratikdkale@gmail.com • New York, NY

Focus Areas

Term Structure (DNS/Kalman) Execution & RL Options & Greeks Time-Series Anomaly Detection Cloud Data Engineering

Toolbox

Python NumPy/Pandas statsmodels scikit‑learn XGBoost PyTorch SQL AWS Docker Terraform Airflow Git

Recent research threads

Macro‑aware Dynamic Nelson–Siegel slope forecasts with regime tests.
Delta–Gamma hedging behavior across market regimes and carry.
Execution with PPO under market‑impact constraints vs TWAP/VWAP.
Rare‑event detection in sparse data with stability checks.

Earlier highlights (from my journey)

Worked with NITI Aayog (NIC, New Delhi) on government tech projects.
Collaborated with DRDO researchers on analytics reporting.
Built a fully functional portal at NIC for Atal Tinkering Labs (AIM).
Led national‑level tech festival operations as convener/co‑convener.

About Me

Focus Areas

Toolbox

From DrunkKalam

From QuantQuill