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indrxjith/README.md

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LinkedIn Kaggle QuantConnect Email


⚡ What I Do

I sit at the intersection of quantitative finance and machine learning — designing data-driven systems that extract signal from noise, price risk, and build edge in financial markets.


🧠 Skills

🐍 Python & Data Stack

Python Pandas NumPy SciPy scikit-learn Statsmodels Plotly SQL

📈 Quant & Finance

Portfolio Optimization Factor Models Backtesting Black-Scholes VaR/CVaR Monte Carlo

🤖 Machine Learning

XGBoost LightGBM LSTM Logistic Regression Feature Engineering Walk-Forward Validation

🛠️ Tools & Platforms

Git FastAPI Streamlit Bloomberg Terminal QuantConnect Kaggle


🚀 Featured Project

📊 Quantitative Portfolio Optimization & Rolling Backtest

A full quant research pipeline — from raw data to risk-adjusted returns — with Monte Carlo optimization, efficient frontier construction, and rigorous out-of-sample validation.

Metric Result
📈 Rolling CAGR 25%+
⚡ Sharpe Ratio ~1.0
🛡️ Sortino Ratio > 1.0
🏆 Benchmark Outperforms SPY

What's inside:

  • 🎲 Monte Carlo simulation — 2,000 portfolios per rebalance cycle
  • 📉 Efficient Frontier visualization with optimal Sharpe portfolio
  • 🔄 Rolling backtest — 5-year train · 1-year test · annual rebalance
  • 📐 Full risk suite — VaR, CVaR, Sharpe, Sortino, drawdown analysis
  • 📊 Covariance & correlation matrix analysis

✦ Stack: Python · Pandas · NumPy · SciPy · Plotly · Statsmodels
✦ Optimization: Maximize Sharpe Ratio via Monte Carlo · Walk-forward validated


📊 GitHub Stats


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