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Browse resources for quantitative finance, from mathematical foundations and Python to pricing models, strategy research and interview preparation. Choose a collection to see the available downloads. A free email sign-up gives you access to the library.

014 resourcesMarket makingModels, code and selected reading on spreads, inventory risk and how liquidity gets made. Resources from Q4Quant posts live here as the collection grows.

0232 resourcesStatistical arbitrageA three-part statistical-arbitrage guide: basic concepts, worked maths and a runnable Python research project. Includes nine article figures, 15 social onepagers and related lessons.

0319 resourcesOptions pricingAn eight-part teaching series covering replication, binomial trees, BSM and market variants, Monte Carlo, local and stochastic volatility, jumps and Fourier methods. Three illustrations per article, a reproducible synthetic Python lab and 16 visual notes.

0412 resourcesBacktesting and strategy evaluationBuild an inspectable Python backtest: timing, cash, costs, walk-forward selection and risk reporting.

058 resourcesProbability for quant interviewsDefine the experiment, condition carefully, calculate expectations and check waiting-time models with runnable Python.

0621 resourcesMaths, finance and programming for quantsWorked examples and visual notes from the maths, finance and programming for quants series.

0711 resourcesPortfolio construction and optimisationWorked examples and visual notes from the portfolio construction and optimisation series.

0832 resourcesVolatility forecasting and riskThirty visual notes and an offline Python lab connecting volatility measurement, forecasting, tail risk and portfolio decisions.

0914 resourcesMachine learning for financial time seriesTwelve visual notes, four articles and a reproducible Python experiment for financial feature engineering, leakage checks, time-series validation and cost-aware baseline comparisons.

101 resourcesQuant interview practice: worked Python solutionsDownload runnable solutions and numerical experiments from the quant interview practice Dispatch series.

111 resourcesStochastic calculus and Monte Carlo for financeRunnable Python examples and reproducible figures for martingales, Brownian motion, Itô’s lemma, GBM, risk-neutral valuation and Monte Carlo.

123 resourcesAI and portfolio researchFive curated papers, a FactorBench deep dive and a runnable Python lab for evaluating signals after costs.