Resources
Books, courses, libraries, datasets, and all our tutorial tracks to accelerate your quant finance journey.
Tutorial Tracks
Recommended Books
Python for Finance
by Yves Hilpisch
The definitive guide to using Python for financial analysis, algorithmic trading, and computational finance.
Options, Futures, and Other Derivatives
by John C. Hull
The standard textbook for derivatives pricing. Every quant has read this.
Quantitative Trading
by Ernest Chan
Practical guide to building and running systematic trading strategies. Great for self-funded traders.
Advances in Financial Machine Learning
by Marcos Lopez de Prado
Cutting-edge ML techniques specifically for finance. Covers feature engineering, backtesting pitfalls, and more.
The Rust Programming Language
by Steve Klabnik & Carol Nichols
The official Rust book. Clear, thorough, and freely available online too.
Programming Rust
by Jim Blandy, Jason Orendorff & Leonora Tindall
A deeper dive into Rust for experienced programmers. Excellent systems-level coverage.
Free Courses and Platforms
MIT 18.S096 — Topics in Mathematics with Applications in Finance
https://ocw.mit.edu/courses/18-s096-topics-in-mathematics-with-applications-in-finance-fall-2013/
Khan Academy — Statistics and Probability
https://www.khanacademy.org/math/statistics-probability
Coursera — Financial Engineering and Risk Management (Columbia)
https://www.coursera.org/specializations/financialengineering
The Rust Book (free online)
https://doc.rust-lang.org/book/
Exercism — Rust Track (free practice problems)
https://exercism.org/tracks/rust
QuantConnect — Free backtesting platform
https://www.quantconnect.com/
Alpaca — Commission-free API trading and paper trading
https://alpaca.markets/
Python Libraries for Finance
pandas
Data manipulation and analysis. The backbone of financial Python.
numpy
Fast numerical computation. Arrays, linear algebra, random numbers.
yfinance
Download market data from Yahoo Finance. Free and easy.
matplotlib
Plotting and visualisation. The standard charting library.
scipy
Scientific computing. Optimisation, statistics, signal processing.
scikit-learn
Machine learning. Classification, regression, clustering.
zipline-reloaded
Backtesting framework originally built by Quantopian.
QuantLib (Python)
Industrial-strength derivatives pricing and risk analytics.
Rust Crates for Finance
polars
Blazing fast DataFrames. The Rust alternative to pandas.
ndarray
N-dimensional arrays for Rust. Similar to numpy.
statrs
Statistical distributions and functions.
csv
Fast CSV parsing. Essential for financial data.
chrono
Date and time handling. Crucial for time-series data.
reqwest
HTTP client for fetching data from APIs.
serde
Serialisation/deserialisation. Parse JSON, TOML, and more.
Practice Datasets
Yahoo Finance (via yfinance)
Free daily/weekly/monthly stock data. Good for learning.
FRED (Federal Reserve Economic Data)
Macro-economic time series. Interest rates, GDP, unemployment.
Kaggle Finance Datasets
Community-uploaded datasets for practice and competitions.
Quandl / Nasdaq Data Link
Professional-grade financial data. Some free, some paid.