Strategy LAB

Indian Market Strategy Lab is a personal-use algorithmic trading research platform I built for backtesting and paper-trading strategies against Indian equities and Nifty-family index options (NIFTY, BANKNIFTY, FINNIFTY, MIDCPNIFTY, NIFTYNXT50). The platform has a Python/FastAPI backend and a React/Vite frontend styled after a classic Bloomberg-terminal aesthetic — dense, monospace, high-contrast panels built for fast scanning rather than decoration. I engineered a multi-timeframe data layer that polls NSE’s public endpoints and resamples 1-minute base candles into eight standard timeframes via pandas, with local SQLite accumulation and an automatic fallback/merge system if the configured storage drive goes offline. The centerpiece is a dual-mode Strategy Engine — traders can build signals visually with dropdown rule blocks or write raw Python in an embedded Monaco code editor — both compiling to the same interface and running through a sandboxed execution environment (RestrictedPython) so user-submitted code can never touch the filesystem or network. On top of that engine sit a historical Backtest Engine with equity-curve and drawdown analytics, and a live Paper Trading Engine that polls near-real-time NSE prices over WebSocket and simulates fills using a Black-Scholes options-pricing model where real historical premium data doesn’t exist.