Python Financial ENGineering (PyFENG package in PyPI.org)

Delta hedging under SABR model

Stochastic volatility models and their application to Deribit crypro-options exchange

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 SABR model.

Financial Engineering in IRFX in C++

C++20 quantitative finance library for volatility surface modelling and derivatives pricing.

High-performance implied volatility surface library with a C++ engine (SABR/SSVI/eSSVI) and Python/Streamlit dashboard. Features arbitrage enforcement, vega-weighted calibration, Lee moment bounds, full audit trail, and 327 tests passing.

Code of numerical experiments in Master's thesis [TBD]

Fit implied vol curves to option prices using SVI and SABR

Real-time volatility sensitive detection and correction for any sequential generative process. AutoTune, feedback learning, reflexive analysis, Monte Carlo SDE bands, Kalman filtering, GARCH variance modeling, signal detection, and domain anchoring.

Code Repo for my Undergrad Thesis

SABR caplet pricing: MC simulation, Hagan (2002) benchmark, and SABR calibration to Heston-generated market vols. Built for ERDOS Institute Fall 2025 Quant Finance Bootcamp

Calibrate the SABR stochastic volatility model to SPX implied volatility surfaces and benchmark smile-aware delta-hedging strategies against the Black–Scholes baseline.

Quant libraty with pricing power of C++ wrapped inside python classes

Pricing & calibration engine (15+ models; API + CLI + Streamlit UI)

Provide a high-performance implied volatility surface engine with C++ core and Python dashboard for accurate options pricing and risk management.