Free articles covering the tools, techniques, and thinking behind modern quantitative finance — from Python and cloud infrastructure to calculus, probability, derivatives pricing, and portfolio theory.
Also explore our quant firms directory, quant jobs by city, and free tools.
The core Python skills you need to break into quantitative finance — variables, functions, data structures, classes, error handling, and the patterns that matter most for quant roles.
Decorators, generators, context managers, and the patterns that separate beginner Python from production-grade quantitative code.
How NumPy array operations power everything from portfolio risk calculations to Monte Carlo simulations — and why it is so much faster than plain Python.
How to use Pandas DataFrames for real financial workflows — loading market data, calculating returns, handling time series, and avoiding common pitfalls.
Learn the SQL fundamentals that matter for finance — querying trade data, aggregating positions, joining reference data, and understanding relational databases.
CTEs, window functions, query optimisation, and the advanced SQL patterns used in trading platforms and financial data pipelines.
How to structure databases for trading platforms — normalisation, schema design, indexing strategies, and the tradeoffs that matter in financial systems.
Why financial firms use specialised time series databases for market data, tick storage, and monitoring — and when you should consider one.
A practical comparison of data formats used in finance — when to use CSV, JSON, Parquet, or columnar storage, and why the choice matters more than you think.
How Git works, why every finance developer needs it, and the workflows that keep trading system code safe and auditable.
How continuous integration and deployment work in finance — automated testing, build pipelines, deployment strategies, and why they matter for trading infrastructure.
Unit tests, integration tests, property-based testing, and the testing strategies that keep financial systems reliable and correct.
Systematic approaches to finding and fixing bugs — from print statements to debuggers, logging strategies, and the mindset that makes debugging efficient.
The software design patterns that matter most in finance — Strategy, Observer, Factory, and others that help build maintainable trading systems.
Object-oriented and functional programming are not rivals — they solve different problems. Here is when each approach shines in financial applications.
How APIs work, RESTful design principles, and practical patterns for building and consuming financial data APIs.
How modern software development lifecycle practices apply in finance — code review, environments, release management, and building reliable systems.
The Knight Capital collapse explained: stale deployments, dead code, missing safeguards, and what this incident teaches aspiring quants about engineering and risk.
How containerisation works, why finance teams use Docker, and practical patterns for packaging and deploying trading system components.
What cloud computing means for financial services — the major providers, core services, cost models, and why finance firms are migrating to the cloud.
The core AWS services that matter for finance — EC2, S3, RDS, Lambda, and the architectural patterns used in trading platforms and data pipelines.
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, and Fargate — without managing a single server.
Why Rust is gaining traction in finance — memory safety without garbage collection, zero-cost abstractions, and the performance characteristics that matter for trading.
Why C++ remains the language of choice for performance-critical finance — low-latency trading, derivatives pricing, and the modern C++ features that matter.
JIT compilation, SIMD instructions, GPU computing with CUDA, and FPGAs — the hardware acceleration techniques used in high-performance financial systems.
How the internet works under the hood — DNS, TCP/IP, HTTP, firewalls, and the networking concepts that matter for building financial applications.
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 to secure financial applications — authentication, authorisation, encryption, common vulnerabilities, and the security mindset every developer needs.
Sigma notation, function composition, set theory shorthand — the symbolic language you actually need before tackling quant finance maths.
Compound interest, log returns, continuous growth — the exponential function and its inverse are everywhere in quantitative finance. Here is why.
Rates of change, areas under curves, optimisation — calculus is the engine behind derivatives pricing, risk management, and portfolio construction.
Portfolio weights are vectors. Covariance is a matrix. Risk decomposition uses eigenvalues. Here is the linear algebra every quant actually needs.
From Markowitz to gradient descent — optimisation is how quants find optimal portfolios, calibrate models, and minimise risk. Here is how it works.
Master the probability concepts every quant needs — expected values, distributions, Bayes' theorem, the Central Limit Theorem, and risk-neutral pricing. With financial examples throughout.
The statistical methods every quant trader needs — volatility estimation, hypothesis testing, regression, and factor models. Learn the statistics that actually get used on trading desks.
From a drunk stumbling home to the Black-Scholes equation — random walks and Brownian motion are the mathematical heartbeat of modern finance.
Learn how Monte Carlo simulation is used in quantitative finance — from options pricing and risk management to portfolio analysis. Includes Python code examples and practical applications.
Equity, fixed income, FX, derivatives — how financial markets actually work, who the participants are, and where quantitative engineers fit in.
Present value, future value, discounting, NPV — the concept that a pound today is worth more than a pound tomorrow underpins all of finance.
Bond pricing, yield to maturity, duration and convexity — the fixed income concepts that form the backbone of interest rate modelling.
What derivatives are, how they work, and why they matter — the contracts at the heart of quantitative finance.
Mean-variance optimisation, the efficient frontier, and the Capital Asset Pricing Model — how modern finance thinks about building portfolios.
A clear guide to option pricing models — the binomial tree, risk-neutral valuation, and the Black-Scholes formula. Learn how derivatives are valued and why it matters for every quant.
A clear guide to the options Greeks — delta, gamma, theta, vega, and rho — plus volatility modelling. Learn how traders use these sensitivities for hedging and risk management.
A comprehensive guide to quantitative risk management — Value at Risk, expected shortfall, credit risk, stress testing, and the mathematical tools behind modern risk frameworks.
A practical introduction to algorithmic trading — alpha signals, execution algorithms, backtesting pitfalls, and what systematic trading actually looks like at quant firms.
A practical, no-fluff guide to landing your first quant role — what to learn, what to build, how to interview, and how to stand out in a crowded applicant pool at banks, hedge funds, and prop firms.
The volatility risk premium is real, well-documented, and has blown up more accounts than almost any other strategy. Here's why it works, why it kills, and what you need to understand before touching it.
A comprehensive breakdown of quant finance salaries in the UK for 2026 — covering quant developers, traders, analysts, and researchers across experience levels and top firms.
A practical roadmap for becoming a quantitative analyst, developer, trader, or researcher — covering required skills, qualifications, career paths, and how to break in without a PhD.
Everything you need to know about finding quant jobs — where to search, what firms are hiring, the different role types, and how to stand out as a candidate in quantitative finance.
The most common quant interview questions across probability, mental maths, coding, market making, and behavioural categories — with detailed answers and tips from real interview processes.
An in-depth guide to quantitative trading strategies — from statistical arbitrage and market making to momentum and machine learning approaches. Learn how quant funds actually make money.
A clear explanation of what a quant is, the different types of quant roles, what they earn, and how to become one. Covers quant analysts, quant developers, quant traders, and quant researchers.
Everything you need to know about becoming a quantitative analyst — from the skills and qualifications required to salary expectations and career progression at banks, hedge funds, and prop trading firms.
A comprehensive guide to financial engineering and quantitative finance Master's programmes — including top UK and global programmes, what you will study, career outcomes, and whether a degree is worth it.
A clear explanation of the Black-Scholes options pricing model — the formula, the assumptions behind it, intuitive understanding of each component, and a complete Python implementation.
A complete guide to quantitative hedge funds — how they generate returns, the top firms to work for, compensation structure, and what it takes to get hired at a quant hedge fund.
A curated list of the best books for learning quantitative finance — from probability and stochastic calculus to trading strategies and career advice. Organised by skill level and topic.
Learn how Python is used in quantitative finance — from data analysis and backtesting to derivatives pricing and machine learning. Includes practical examples and a learning roadmap.
Learn what algorithmic trading is, how it works, and how to get started. Covers strategy types, technology requirements, Python implementation, and common pitfalls for aspiring algo traders.
Everything you need to know about becoming a quant developer — the technology role at the heart of quantitative finance. Covers required skills, salary expectations, career paths, and how to break in.