Implementation of option pricing models using Numba that performs better. This entire project has utilized as little libraries as possible, even though certain models have their own Machine Learning Model with assessment and performance.

Using Finite Element and Finite Difference Methods to Price European Options

This repository documents a complete ML workflow to model Uber fares in Paris, from granular EDA and feature engineering to building and fine-tuning a stacking regressor on 10k real-world rides.

Dex formulas

Full-stack Bitcoin options pricing dashboard using the Heston Stochastic Volatility Model. Features MLE parameter calibration, Monte Carlo simulation, multi-method pricing (Heston, MC, Black-Scholes), real-time Deribit data, and interactive visualization. Built with FastAPI + React.

🚗 A dynamic pricing and insurance risk modeling system using Python, XGBoost, SHAP, and DVC. Predicts claim severity and probability, enabling risk-adjusted premium strategies with full reproducibility and CI/CD.

This project investigates the relationship between smartphone features and their prices using various regression techniques.

Dynamic pricing for launching a pushup feature

Distributed engineering cost modeling and team topology pricing platform for CTO decision making.

A Python library that simplifies working with QuantLib by providing high-level abstractions for common quantitative finance tasks. The library handles market conventions, rate helpers, and calibration boilerplate so users can focus on pricing logic rather than QuantLib's low-level API.