quantt - Quantitative Finance Resources

A careful derivation of Ito's lemma, the stochastic-calculus chain rule that underpins Black-Scholes, geometric Brownian motion, and every SDE-based model in modern quantitative finance. With worked examples in Python.

A rigorous but accessible derivation of geometric Brownian motion - the SDE, the closed-form solution via Ito's lemma, the log-normal distribution, exact simulation, and its role in Black-Scholes and Monte Carlo pricing.

A rigorous guide to Expected Shortfall - what it is, why it superseded Value at Risk in regulatory frameworks, how to estimate it from historical, parametric and Monte Carlo methods, and how to backtest it.

A practical, statistically rigorous guide to testing whether a financial time series is mean-reverting, using the Augmented Dickey-Fuller test, the Hurst exponent and half-life estimation, with Python examples.

A rigorous but readable guide to the Ornstein-Uhlenbeck process - its SDE, distribution, discrete-time form, exact simulation, and maximum-likelihood calibration to financial data.

A rigorous but accessible walkthrough of risk-neutral pricing - the change of measure, no-arbitrage principle, the fundamental theorem of asset pricing, and why option prices are expectations under Q, not P.

A practical, worked-example guide to constructing a zero-coupon yield curve by bootstrapping - from deposits and futures to swaps, with a Python implementation.

A rigorous guide to duration and convexity - Macaulay duration, modified duration, dollar duration, DV01, and convexity - with derivations, worked examples and Python code.

We backtested 1,000 strategies that we knew contained no signal at all. More than half the time, the best of them had a Sharpe ratio above 1.0. A simulation study of selection bias, the expected maximum Sharpe ratio, and why a parameter sweep flatters you less than you fear.

Cohen, Malloy and Nguyen's Lazy Prices paper found that small year-on-year changes in 10-K filings predict large negative returns.

The volatility risk premium is real, well-documented, and has blown up more accounts than almost any other strategy.

How to take a Python financial model from running 150 scenarios in a Lambda function to processing over a million using AWS Step Functions, Batch.

How to secure financial applications — authentication, authorisation, encryption, common vulnerabilities, and the security mindset every developer needs.

Why latency matters in trading, how to measure it, where the bottlenecks are, and what firms do to minimise it — from co-location to kernel bypass.

How financial firms process massive datasets — batch and streaming architectures, ETL patterns, data lakes, and the tools that power modern data infrastructure.

JIT compilation, SIMD instructions, GPU computing with CUDA, and FPGAs — the hardware acceleration techniques used in high-performance financial systems.

Why C++ remains the language of choice for performance-critical finance — low-latency trading, derivatives pricing, and the modern C++ features that matter.

Why Rust is gaining traction in finance — memory safety without garbage collection, zero-cost abstractions.

The Knight Capital collapse explained: stale deployments, dead code, missing safeguards.

How the internet works under the hood — DNS, TCP/IP, HTTP, firewalls, and the networking concepts that matter for building financial applications.