Notes on Quantitative Finance and Machine Learning by Cristian Quintero

This git repository is the result of storing some of the personal notes about models used in quantitative finance and from time to time deep learning and reinforcement learning. All those are result of lessons learned in academy and professional work.

Lecture Notes

A bit of Maths

• Laplace Transformation here

Interest Rates

• Bonds here

Options

• Binomial Tree Model here
• Black-Scholes equation here

Stochastic Process

• Geometric Brownian Motion (GBM) here

Codes

Derivatives

• European Option Greeks (using Tensorflow) here
• European Option (using C++ implementation) here

Market Risk

• VaR with Extreme Value Teory (EVT-VaR) here
• Cornish Fisher VaR (mVaR) here

Simulation

• Monte Carlo Integration here
• Random Numbers Generation here

Portfolio Theory

• Markowitz (unfinished) here

Other models

• Nelson and Siegel Model, example here

Stochastic Calculous

• Quadratic Variation on Brownian Motion here
• Random Walk here
• Geometric Brownian Motion here
• Brownian Bridge here

LLM

• MCP server calculating HVaR (testing) here

Technical Issues / Challenges

• How to use C and C++ code in Python by using Swig here
• Counter argument on forecasting misconception when Kernel regression is used without clear distinction between In-Sample data for training and Out-of-sample data for testing. here

Enjoy it!