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.

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.

Financial Engineering in IRFX in C++

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.

Fit implied vol curves to option prices using SVI and SABR

Volatility surface modelling library

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

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

Code Repo for my Undergrad Thesis

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

Implementation and empirical study of the SABR stochastic volatility model (Hagan et al. 2002) on SPY/QQQ option chains 2014–2023.

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

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

Neural network calibration engine for Heston & SABR models — ~1000x faster than classical optimizers.

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