Personal knowledge repository
Welcome to my personal knowledge repository. Here I share a condensed overview of concepts I have learned throughout my journey. The repository is organized into seven thematic pillars. Use the left sidebar to browse topics, click a title to open it, or use the arrow to expand subtopics. You can also navigate the current page with the table of contents on the right.
This repository is not designed as a traditional course, but as a synthesis of each topic with the essentials to remember. Many courses and tools can help you learn these subjects in depth. If you want to go further, do your own research or ask a LLM for more detail. With the constant flow of information we receive, we forget quickly and retain little. This repository helps you revisit concepts and recover what may have been forgotten.
Mathematics
Linear algebra, analysis, optimization, and numerical methods — mathematical foundations for quantitative modeling and finance.
Probability
Probability foundations, distributions, and stochastic processes — from the axiomatic framework to Brownian motion.
Statistics
Descriptive statistics, inference, regression, and time series, with interactive distribution visuals and code examples.
Machine Learning
General concepts, classical models, deep learning, reinforcement learning, and applications to financial markets.
Quantitative finance
Markets and products, options and derivatives, volatility, risk, portfolio management, microstructure, and systematic research.
Engineering & Programming
Python, performance (C++, CUDA), data structures, design patterns, and data engineering — the quant technical stack.
Tools
Development environment, version control, notebooks, and day-to-day analysis tools.