OIPD computes the probabilities of an asset's future price as implied by the options market.

Streamlit IV surface visualizer (Yahoo Finance + Black–Scholes). Explore IV vs expiry and strike/log-moneyness.

Systematic Volatility Research and Backtesting for equity options

Vollab (Volatility Laboratory) is a python package for testing out different approaches to volatility modelling within the field of mathematical finance.

A practical introduction to derivatives pricing and risk : 10 Jupyter notebooks from 17 years on trading desks.

options market making engine in C++20 — SVI vol surface, Black-Scholes pricing, lock-free SPSC queues, delta hedging, and real-time PnL attribution.

Closed-form solutions and fast calibration & simulation for SABR-based models with mean-reverting stochastic volatility

This project aims to construct the Equity Implied Volatility surface under the Stochastic Volatility Inspired (SVI) model.

Quantitative Finance Library & Option Trading Tool

This project aims to construct the Equity Implied Volatility surface under the SABR model.

An interactive toolkit visualising options pricing and Greeks across Black-Scholes and Monte Carlo models with comparative analytics.

https://arbitragefreessvi.streamlit.app/

This project aims to calibrate dividends and volatility surfaces from listed European and American equity option prices.

This project aims to strip the Equity Local Volatility surface and implement the associated PDE pricing method.

Python SDK for the FlashAlpha options analytics API — live options screener, gamma exposure (GEX), DEX/VEX/CHEX, options flow, 0DTE, VRP, volatility surfaces, greeks

Advanced volatility trading strategies for crypto options (Deribit & Binance). Professional Python implementation featuring delta-neutral strategies (straddles/strangles with Whalley-Wilmott bands), Volatility Risk Premium (VRP), dispersion trading, and volatility surface trading.

Arbitrage-free implied volatility surface engine — live SPY options via yfinance, Newton-Raphson IV extraction, SVI parameterization (Gatheral 2004), Durrleman no-arbitrage enforcement, and interactive Streamlit dashboard.

Institutional-grade quantitative finance terminal: C++20 engine + Python. Options pricing (Black-Scholes, Heston, Merton, Monte Carlo, PDE), Kalman filtering, GARCH forecasting, 3D volatility surfaces, backtesting, walk-forward optimization, Markowitz portfolios - on live market data.

A quantitative research project exploring hybrid volatility forecasting. Integrates parametric surface models (SVI/SSVI) and Risk-Neutral Density (RND) extraction with Deep Learning (MoE) forecasting 10-day total variance changes.

A package that utilises QT and OpenGL graphics to visualise realtime 3D volatility surfaces and analytics.