Project Description
I trade index and single-stock options on Indian exchanges and I’m looking for a quant who can dig deep into volatility modeling with one clear goal: sharpen my live trading strategies.
You will take raw options data (primarily vix NSE/BSE), build and calibrate advanced volatility models—think GARCH variants, stochastic-vol, local-vol, surface interpolation—and translate the results into actionable entry, exit and sizing rules. Experience handling large intraday datasets, cleaning corporate-action adjustments, and coding robust back-tests in Python / R is essential; C++ or Julia skills are a plus if you can speed up the heavier calculations.
Key deliverables
• A fully documented research notebook or repo that pulls, cleans and stores historical option chains and underlying spot prices
• At least two comparative volatility models, calibrated to Indian market microstructure, with performance metrics against realised P&L and risk-adjusted returns
• Back-test framework, parameter sweep scripts and visual reports that show where the strategy outperforms benchmarks
• A concise summary paper explaining methodology, assumptions, and how each model feeds the trading logic
Acceptance criteria
• Code runs end-to-end on my sample dataset without manual tweaks
• Results replicate within ±1 % on a fresh data slice
• Documentation is clear enough for me to iterate without additional hand-holding
I’ll be available for quick feedback loops and can provide API keys, existing data dumps and execution constraints up front so you can focus on producing solid, trade-ready research.