OIPD computes the market's expectations about the probable future prices of an asset, based on information contained in options data.
Vollab (Volatility Laboratory) is a python package for testing out different approaches to volatility modelling within the field of mathematical finance.
Daily Volatility trading strategies on Index Equity Options
Closed-form solutions and fast calibration & simulation for SABR-based models with mean-reverting stochastic volatility
Quantitative Finance Library & Option Trading Tool
Jupyter notebooks implementing Finance projects
A quantitative research project exploring hybrid volatility forecasting. Integrates parametric surface models (SVI/SSVI) and Risk-Neutral Density (RND) extraction with Deep Learning (LSTM + Self-Attention) to predict future Implied Volatility surfaces.
A package that utilises QT and OpenGL graphics to visualise realtime 3D volatility surfaces and analytics.
Quantitative finance library for volatility surface modelling in C++20
An interactive toolkit visualising options pricing and Greeks across Black-Scholes and Monte Carlo models with comparative analytics.
Live updating dynamic volatility surface constructed from options prices in C++
Modular multi-asset-class Monte Carlo engine for pricing exotic derivatives and structured products with calibrated implied volatility surfaces (Heston, local vol, SVI) and a user-friendly Django web interface.
Implied volatility surfaces from SPX option chains data (both calls and puts), interpolation for continuous querying, and GUI to visualize surfaces and calculate Black-Scholes prices and IVs
Toolkit for option market research: SABR/SVI baseline calibration, neural network volatility surface models, fast Greeks inference, and reinforcement learning agents for dynamic hedging.
active investing
Black-Scholes options analysis platform that combines theoretical pricing models with real-time market data to calculate options. Platform powered by implementing Heston, GARCH, and Jump-Diffusion models with Numba-accelerated Monte Carlo simulations.
Implied Volatility Calibration via raw-SVI
BEVL Toolkit is a Python library for constructing Break-Even Volatility (BEVL) surfaces — the volatility level that makes the expected P&L of a delta-hedged option equal to zero.
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