Small, self-contained implementations of concepts I'm learning through Shreve's Stochastic Calculus for Finance II and Cartea–Jaimungal–Penalva's Algorithmic and High-Frequency Trading.
Each notebook is a focused 1–2 hour exercise: a short prose intro in my own words, the code, plots, and a short "what I learned / what's next" section. These are not production projects — they are how I make sure I actually understand what I read.
The full project work (e.g. an Avellaneda–Stoikov market maker, statistical-arbitrage backtester) lives in dedicated repos starting in M3.
stochastic-calc/— Brownian motion, Itô calculus, SDEs, geometric Brownian motionpricing/— Black–Scholes, Monte Carlo pricers, Greeks, implied volatilitymicrostructure/— order-book mechanics, trade-sign classification, simple LOB simulators
- quant-journey — written notes and weekly logs that reference these notebooks
- islp-labs — statistical-learning chapter labs
Python 3.11+, NumPy, pandas, SciPy, Matplotlib, Jupyter. No frameworks beyond that for the foundational notebooks.