Quantitative Finance Project: Statistical Arbitrage & Pairs Trading Strategy Analyzing 20 Tech Stocks from the S&P 500 using Cointegration, Mean Reversion, and Backtesting.

Assignment on implied volatility pairs trading for Trubeacon Quant Researcher Internship

Used three models to test this Stratergy based on Implied volatiles of Bank Nifty and Nifty. The three models tested were z-score model, residual model and hedge ratio model. Z-score working the best only with a high drawdown and the other two after removing bias showcased not much mean reversion.

A custom passion project where I can get some hands on work with researching, building out, and thoroughly backtesting a quant trading model based on statistical arbitrage (through pairs trading)!

AlphaTerminal — a modular quant-finance web app (Next.js) + FastAPI backend featuring pairs trading, portfolio optimization, options pricing, volatility forecasting, market-making simulation, and research tools.

A production-grade quant research engine for pairs trading from cointegration screening through live-parameter simulation, risk decomposition, and portfolio construction. Built to the standards used at systematic hedge funds.

A Python project for statistical arbitrage using pairs trading. Implements cointegration-based pair selection, z-score signal generation, parameter optimization via backtesting, and performance visualization. Includes modular code, a comprehensive report, and example results for quant research and portfolio analysis.

Quantitative finance models implementing Black-Scholes & binomial tree options pricing with delta hedging simulations. Includes pairs trading strategy using cointegration tests and Z-score signals. Demonstrates advanced pricing models, risk management, and statistical arbitrage for quant research.