• Quantitative Developer at Pranjali Growcap Pvt. Ltd. (New Delhi) — building enterprise-grade algo trading infrastructure for Indian & US equity markets
• Architected automated systems handling 150+ trading accounts with real-time RMS, order execution, and P&L tracking across ₹400+ Crore turnover
• Published PyZData on PyPI — an open-source market data library with 23 stars and 13 forks used by traders and quant researchers
• Deep expertise in broker API integrations (Zerodha, Interactive Brokers, XTS), low-latency Python systems, and financial data pipelines

Open Source Published on PyPI — Most forked project

Problem: Granular historical and intraday options data is locked behind expensive vendor subscriptions, blocking backtesting and research for independent quants.

Solution: Published pyzdata — available as a Python library (pip install pyzdata), CLI tool, and browser-based Streamlit web app. Downloads OHLCV + Open Interest candle data for any stock, index, or F&O instrument (NSE, BSE, NFO, MCX) with intervals from 1-minute to daily. Supports parallel download threads, configurable retries, caching, and a typed exception hierarchy.

Impact: 23 stars, 13 forks, provides institutional-quality data for backtesting and research at zero cost. Used by quant researchers and retail traders across the Indian markets.

Python PyPI Streamlit CLI Multithreading Pandas

Private Enterprise — Pranjali Growcap Pvt. Ltd.

Problem: Manual order execution and risk management across 150+ trading accounts created bottlenecks, errors, and compliance risks.

Solution: Built a custom Python SDK automating the full trade lifecycle — signal generation, multi-broker order placement (Zerodha, Interactive Brokers, XTS), real-time RMS checks, position reconciliation, and P&L tracking.

Impact: Powers ₹400+ Crore in turnover, eliminated manual intervention entirely, enforced real-time risk limits, and reduced execution latency across all accounts.

Python Zerodha API Interactive Brokers XTS API WebSocket Multithreading

Private Enterprise — Pranjali Growcap Pvt. Ltd.

Problem: Executing systematic options selling strategies on US markets required a fully automated, crash-recoverable system capable of running multiple strategies concurrently on a headless AWS server with zero manual intervention.

Solution: Built a ZMQ hub-spoke trading engine — a central TWSMasterHub maintains a single TWS connection while 5 independent strategy processes (SRE, SRE SL, BRE, B120, SRE TP) communicate via ZeroMQ. Features a 13-module SDK with CSV state machines for crash recovery, smart order execution with price chasing and stale-fill detection, ATM pointer-walk strike selector, exchange-hosted STP LMT orders, and real-time monitoring via Google Sheets and Telegram.

Impact: Production system on AWS EC2 executing options strategies across SPX, SPY, and ES — fully hands-off from market open to close with complete audit trail and automated IBC-managed TWS restarts.

Python 3.12 ZeroMQ ib_async Interactive Brokers AWS EC2 IBC Google Sheets API Telegram Bot

Private Enterprise — Pranjali Growcap Pvt. Ltd.

Problem: Backtesting options selling strategies across millions of parameter combinations on Indian, MCX, and US markets — no commercial platform supported the required customization depth.

Solution: Built pgcbacktest — a NumPy-vectorized backtesting engine with O(log n) price lookup, synthetic future ATM detection, SD-based strike routing, and 37-level portfolio stop-loss. Includes a FileLock-based orchestrator running N parallel terminals (any terminal claims any date), a Polars + DuckDB dashboard aggregator, and a tkinter GUI pushing live heatmaps to Excel via xlwings.

Impact: Processes 50,000+ parameter combos × 250+ dates × 6 DTEs across NIFTY, BANKNIFTY, SENSEX, MCX, and 40+ US symbols in 8–24 hours on 16 parallel terminals. Powers all strategy research at Pranjali Growcap.

Python NumPy Polars DuckDB Parquet FileLock xlwings tkinter

• Exploring low-latency execution in C++ for sub-millisecond order routing
• Scaling trading infrastructure with containerized microservices on AWS
• Deepening expertise in options pricing models and volatility surface modeling
• Building more open-source fintech tools for the Indian markets ecosystem
• Publishing additional Python packages to PyPI