I'm a Quantitative Developer specializing in building production-grade algorithmic trading systems, market data infrastructure, and options analytics platforms. With 100+ repositories spanning the entire trading technology stack, I architect end-to-end solutions for quantitative finance.

• 🏦 Building Multi-Client Order Management Systems for institutional trading desks
• 📊 Designing real-time market data pipelines processing Level-2 order book data
• 💹 Developing options trading frameworks with IV analysis, Greeks, and multi-leg strategies
• 🔬 Creating backtesting engines for equity, futures, and options strategies
• 🌐 Integrating with major Indian & global brokers: Zerodha, Motilal Oswal, IBKR, HDFC, Angel One
• 📈 Focused on quantitative finance, systematic trading, and market microstructure

Enterprise-grade Order Management System supporting multiple trading accounts with real-time P&L, risk management, and broker integrations.

• Tech: Python, ClickHouse, Redis, Motilal XTS API
• Features: Multi-client support, real-time position tracking, automated risk controls

High-performance ETL pipeline for ingesting, transforming, and storing market data at scale.

• Tech: Python, ClickHouse, ZeroMQ
• Features: Real-time tick data ingestion, OHLCV aggregation, historical data management

Execution observatory for intraday trading desks using Level-2 order book data. Post-trade forensics and liquidity analysis.

• Tech: Python, ClickHouse, Kite Connect
• Features: Order book replay, execution quality metrics, liquidity state monitoring

Real-time options chain construction with Greeks calculation, IV analysis, and strike selection.

• Tech: Python, NumPy, Options Pricing Models
• Features: Live Greeks, IV surface, multi-exchange support

Sophisticated backtesting engine for complex multi-leg options strategies with realistic slippage and execution modeling.

• Tech: Python, Pandas, ClickHouse
• Features: Multi-leg spreads, IV crush strategies, P&L attribution

End-to-end library for building, backtesting, and deploying deep-ITM put + futures regime-based hedges.

• Tech: Python, Machine Learning, Options Analytics
• Features: Regime detection, dynamic hedging, risk-adjusted returns

Options payoff visualization and strategy analysis tool for complex options structures.

Scalable market data distribution system with pub/sub architecture for multi-client consumption.

• Tech: ZeroMQ, Redis, WebSockets
• Features: Real-time streaming, data normalization, multiple broker feeds

High-frequency tick data ingestion with microsecond precision timestamping.

• Tech: Python, ClickHouse, Binary protocols
• Features: Sub-millisecond latency, data validation, historical replay

Realistic market data simulator for paper trading and algorithm testing.

• Tech: Python, Historical data modeling
• Features: Order book simulation, realistic spread & slippage

Modular framework for developing, testing, and deploying systematic trading strategies.

• Tech: Python, Statistical Analysis, Backtesting
• Features: Signal generation, portfolio construction, risk management

Comprehensive equity backtesting platform with realistic transaction costs and market impact.

Real-time signal generation system for systematic trading strategies.

Real-time M2M (Mark-to-Market) dashboard for tracking client positions, P&L, and risk metrics.

• Tech: Python, Streamlit/Dash, Real-time WebSockets
• Features: Live P&L tracking, position monitoring, risk alerts

Trading desk analytics platform with performance attribution and execution quality metrics.

Options Implied Volatility analysis dashboard using Angel One Smart API.

Full-featured OMS with Zerodha Kite Connect integration.

Algorithmic trading system deployed on Interactive Brokers.

Production OMS integrating with Motilal Oswal's XTS platform.

Implementation of quantitative momentum strategies based on academic research.

• Tech: Python, Pandas, Statistical Analysis
• Topics: Factor investing, momentum anomaly, portfolio optimization

Value investing strategies using quantitative screening and fundamental analysis.

ML project predicting data scientist salaries using regression and tree-based models.

• 🔬 Building microservices-based trading infrastructure with event-driven architecture
• 📊 Developing advanced options analytics with volatility surface modeling
• 🤖 Exploring machine learning applications in systematic trading
• 🏗️ Creating production-grade backtesting frameworks with realistic market simulation
• 📈 Researching market microstructure and high-frequency trading patterns

expertise = {
    "Quantitative Finance": [
        "Options Pricing & Greeks",
        "Portfolio Optimization",
        "Risk Management",
        "Market Microstructure",
        "Statistical Arbitrage"
    ],
    "Trading Systems": [
        "Order Management Systems (OMS)",
        "Execution Management Systems (EMS)",
        "Multi-Client Architecture",
        "Real-time Risk Controls",
        "Post-Trade Analytics"
    ],
    "Market Data": [
        "High-Frequency Tick Data",
        "Order Book Analysis (Level-2)",
        "Time-Series Databases",
        "Data Normalization",
        "Historical Replay Systems"
    ],
    "Strategy Development": [
        "Systematic Trading Strategies",
        "Options Strategies (Spreads, Strangles, Butterflies)",
        "Backtesting & Simulation",
        "Signal Generation",
        "Alpha Research"
    ],
    "Infrastructure": [
        "Low-Latency Systems",
        "Distributed Systems",
        "Message Queuing (ZeroMQ)",
        "Time-Series Storage (ClickHouse)",
        "Real-time Processing (Redis)"
    ]
}

I'm always interested in collaborating on:

• 🚀 Quantitative trading systems and algorithmic strategies
• 📊 Market data infrastructure and analytics platforms
• 💹 Options pricing and derivatives analytics
• 🔬 Open-source fintech projects
• 📈 Systematic investing and portfolio management