📒 A collection of notes exploring Quantitative Finance concepts with Python

Various different projects for quantitative finance understanding

Quantitative Finance is a repository dedicated to exploring and understanding key concepts and methodologies in the field of quantitative finance. It delves into topics such as portfolio optimization, financial modeling, risk management, and algorithmic trading

This course introduces the technical skills required for data-driven analysis of business and financial data. Emphasis is placed on applying statistical methods to summarize and make inferences from complex data and to develop quantitative models to assist business decision making. The software packages Excel and R will be used to obtain quantitative solutions to financial problems. Topics include: understanding the concept of risk, portfolio construction and analysis, regression and time series models used in finance and simulation techniques.

This is a collection of notes exploring a number of topics in Quantitative Finance (QF). The idea is to use Python 🐍 to illustrate the theoretical concepts and help you to get a better understanding of each topic.

A two month journey to become a Quantitative Researcher — starting from zero to understanding the foundations of quantitative finance, statistics, and programming. Built with React.js and Vite for fast and modular web development. Explore the roadmap, resources, and personal progress logs for an aspiring quant.

This repository contains a jupyter notebook with various examples in understanding Quantitative Finance

Option pricing tools using Black-Scholes and Binomial Tree models. Calculate call and put prices across multiple strikes, leveraging real-time data from Yahoo Finance. Includes adjustable parameters like volatility, interest rates, and steps. Perfect for showcasing quantitative finance skills and understanding derivatives pricing.

This repo is meant to track my OOP learning process, mainly Python, but will move to C++ later on. I will be focusing on advanced concepts and reproducing my answer, logic and understanding of quantitative finance questions in various formats to enhance my practical application and understandings.

Welcome to my collection of Quantitative Finance projects. This repository is a curated set of hands-on Python-based projects that demonstrate my understanding and implementation of core financial theories, portfolio analysis, and data-driven investment strategies.

A compact Python project that prices basic financial instruments — European options (Black–Scholes), coupon bonds, and a simple deterministic swap. Built for educational and portfolio purposes to demonstrate understanding of quantitative finance and market valuation models. Includes CLI, unit tests, and a Google Colab notebook ready to run.

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Creating Finance and Quantitative projects to further my understanding of markets and increase my understanding of coding in the financial workplace

Educational implementation of binomial tree option pricing with detailed documentation and interactive visualizations for understanding quantitative finance fundamentals.

This is a Collection of Projects made to show off the combination of coding and quantitative finance, and increase ones understanding within the field.

My journey through the book Quantitative Finance with Python: I intend to read it in my spare time to further my understanding of this fascinating subject.

This project models and quantifies risk exposures in Uniswap v3 liquidity provision using a quantitative finance framework. It focuses on understanding how LP positions behave under price movements, volatility, and jump risk.

In my AMS 320 Quantitative Finance class, I'm learning about zero rates and their role in bond pricing. I'm creating a zero rate calculator to try to build a robust system and to reinforce my understanding.

This repository documents my journey in applying mathematical and engineering principles to computer science. My goal is to develop a deeper understanding of coding, financial markets, and data manipulation through hands-on projects in Python, quantitative finance, and algorithmic trading.

Master’s graduate in Quantitative Finance and Data Science. Building a portfolio of projects in Python, SAS, and Power BI to highlight both technical skills and business understanding. My goal: to leverage analytical expertise to support data and risk teams.

This repository serves as a centralized knowledge base for my evolving expertise in quantitative finance. It documents the trading strategies, research frameworks, and systematic workflows I’ve developed as I build toward a professional-level understanding of quant research and systematic trading.

Simulate stock prices using Geometric Brownian Motion (GBM). Simulating prices is fundamental for pricing derivatives. In the case of GBM, it is the key part of pricing equity options using Black-Scholes. Understanding how to simulate stock prices is foundational for any quantitative finance work.

I’m interested in RAG and quantitative finance, and instead of just reading about them, I’m learning by building. This project is my way of understanding how financial data systems work in practice and how RAG can be applied to real financial data, step by step.