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.
A clear, practical introduction to stochastic calculus for finance - covering Brownian motion, Ito's lemma, stochastic differential equations, and how they connect to options pricing and risk management.
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.
Cohen, Malloy and Nguyen's Lazy Prices paper found that small year-on-year changes in 10-K filings predict large negative returns. Here is what the paper actually says, and how Snowflake Cortex AI and Semantic Views collapse the original eight-year engineering pipeline into an afternoon's work.
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.
Real 2026 quant finance salaries in the UK - graduate to PM-level pay for quant developers, traders, analysts and researchers across banks, hedge funds and prop firms like Jane Street, Citadel and Two Sigma.
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.
Guide to finding quant jobs - where to search, how hiring differs by firm type, 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 shaped by commonly reported interview formats.
9 quantitative trading strategies that work in 2026 - statistical arbitrage, pairs trading, market making, momentum, mean reversion, machine learning, options vol, HFT and crypto. With code examples, edge sources and the realistic Sharpe ratios.
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.
Practical guide to becoming a quantitative analyst - skills, qualifications, salary expectations, and career progression at banks, hedge funds, and prop trading firms.
The best financial engineering and quantitative finance Master's programmes in 2026 - ranked. Imperial, Oxford, CMU MSCF, Baruch MFE, Princeton MFin, Berkeley, NYU Courant and more, with placement rates, fees and what each programme actually teaches.
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.
20 best books for quantitative finance and trading - from Hull's Options to Shreve's Stochastic Calculus, Lopez de Prado's ML, and the Green Book. Organised by topic and skill level, with the must-reads for quant interviews flagged.
Interview guide for Citadel and Citadel Securities - typical stages from online assessment to superday, with question themes across quant research, trading, and software engineering.
A complete breakdown of the Jane Street interview process for quant traders, researchers, and software engineers - with real questions, preparation strategies, and tips from candidates who made it through.
A complete guide to interviewing at Optiver for trading, quant research, and technology roles - with real questions from the mental maths test, trading games, and technical rounds.
Guide to becoming a quantitative trader - skills, qualifications, salary expectations, daily responsibilities, and common routes into the role at competitive firms.
Complete 2026 ranking of the best proprietary trading firms - Jane Street, Citadel Securities, Hudson River Trading, Optiver, Jump and 14 more. Salaries, interview difficulty, hiring bar and what each firm looks for.
A complete guide to Jump Trading - their strategies, technology stack, interview process, salaries, and how to land a role at one of Chicago's top prop trading firms.
An honest review of 'A Practical Guide to Quantitative Finance Interviews' by Xinfeng Zhou - what it covers, how to use it effectively, and whether it's still relevant for quant interviews in 2026.
A clear explanation of the butterfly spread options strategy - how it works, payoff diagrams, when to use it, and Python code to model the trade. Covers both call and put butterflies.
How much do quantitative analysts actually earn? Detailed salary data by seniority level, firm type, and location - covering New York, London, Hong Kong, and more.
Overview of Hudson River Trading - trading strategies, technology, interview themes, publicly discussed compensation bands, and how candidates typically prepare.
A practical guide to high frequency trading - what HFT firms actually do, the technology behind it, common strategies, top firms, and how to build a career in HFT.
Learn what the information ratio is, how to calculate it, and why portfolio managers use it to measure skill. Includes Python code and comparisons with the Sharpe ratio.
A practical explanation of the volatility smile - why implied volatility varies across strike prices, what causes it, and how traders and quants use it for pricing and risk management.
Overview of Flow Traders - ETP market making, technology, interview themes, compensation discussion in industry reporting, and career paths at this Amsterdam-listed firm.
A complete guide to IMC Trading - their market making operations, technology, career opportunities, interview process, and salaries at this Amsterdam-based proprietary trading firm.
A practical guide to factor investing - what factors are, why they generate returns, the main factor premiums, and how quantitative investors build factor-based portfolios.
Detailed breakdown of hedge fund salaries in the UK - from analyst to portfolio manager, across quant funds, macro funds, and multi-strategy platforms. Realistic 2026 figures.
A practical guide to the Heston stochastic volatility model - the mathematics behind it, why it matters for option pricing, calibration, and Python implementation.
A practical guide to cointegration - what it means, how to test for it using the Engle-Granger and Johansen methods, and how quant traders use it for pairs trading strategies.
A practical comparison of Bloomberg Terminal alternatives for quants, traders, and analysts - from free tools like Yahoo Finance to professional platforms like Refinitiv and FactSet.
A clear guide to Value at Risk - what it is, the three methods for calculating it, Python implementations, and why every risk manager and quant needs to understand VaR.
A clear explanation of put-call parity - the fundamental relationship between call and put option prices, with the formula, worked examples, and how traders use it to spot arbitrage.
A practical guide to the Sortino ratio - how it improves on the Sharpe ratio by focusing only on downside risk, with the formula, Python code, and interpretation guidelines.
A complete guide to Two Sigma Investments - their data-driven approach to investing, technology stack, career opportunities, interview process, and compensation.
Overview of D.E. Shaw - hybrid investment approach, technology, career paths, interview themes, and publicly discussed compensation context at this systematic hedge fund.
A complete guide to Virtu Financial - their market making operations, technology, public company status, career opportunities, and how to land a role at this global HFT firm.
A practical guide to implied volatility - what it is, how to calculate it, IV rank vs IV percentile, the VIX, IV crush, and how options traders use it to find opportunities.
A clear explanation of heteroscedasticity - what it means for your regression models, how to detect it with the Breusch-Pagan and White tests, and practical fixes with Python code.
An honest look at AI trading bots in 2026 - which ones actually work, which are marketing hype, and what professional quants think about retail AI trading software.
A hands-on tutorial for Backtrader - Python's most popular backtesting framework. Build your first strategy from scratch with complete code examples and best practices.
A practical guide to mean reversion trading - the theory behind it, how to identify mean-reverting assets, common strategies, and Python code for building your own mean reversion system.
A practical guide to the Calmar ratio - how it measures return relative to maximum drawdown, the formula, Python code, and how it compares to the Sharpe and Sortino ratios.
A clear guide to the Treynor ratio - how it measures return per unit of systematic risk using beta, the formula, Python code, and when to use it instead of the Sharpe ratio.
A practical guide to market microstructure - how orders are matched, how prices form, the role of market makers, and why understanding market structure is essential for quant traders.
A practical guide to the Vasicek model - the mathematics of this classic interest rate model, parameter interpretation, bond pricing, Python simulation, and comparison with other short-rate models.
A practical guide to Granger causality - what it really means, how to run the test in Python, how to interpret results, and applications in finance and trading.
An honest review of the Oxford Algorithmic Trading Programme - what it covers, who it's for, cost, and whether it's worth the investment compared to other quant finance courses.
A hands-on introduction to QuantLib in Python - how to install it, price options and bonds, build yield curves, and use the library for quantitative finance calculations.
An honest comparison of algorithmic trading software and platforms - from free Python frameworks to professional-grade systems, with pros, cons, and who each tool is best for.
A practical guide to autocorrelation - what it means in time series data, how to detect it using ACF plots and the Durbin-Watson test, and why it matters for financial modelling and trading.
A practical guide to momentum trading - the theory behind it, common strategies from simple moving averages to cross-sectional momentum, Python implementations, and risk management.
A hands-on guide to pairs trading - how to find cointegrated pairs, calculate the spread, build entry and exit signals, and implement the strategy in Python with full code examples.
A clear explanation of quantitative trading - how it works, the strategies quant traders use, the technology behind it, and how it differs from discretionary and algorithmic trading.
An honest review of 'Heard on the Street' by Timothy Crack - what it covers, who it's for, how to study it effectively, and how it compares to other quant interview prep books.
A practical guide to maximum drawdown - the formula, how to calculate it in Python, what constitutes a good drawdown, and how fund managers use it to evaluate risk and strategy performance.
A practical guide to the Arbitrage Pricing Theory (APT) - how it works, the formula, key differences from CAPM, and how quants use multi-factor models to price assets.
A practical guide to statistical arbitrage - what stat arb strategies are, how they work, the main approaches, and how quant hedge funds and prop trading firms implement them.
A practical guide to options market making - how market makers quote prices, manage risk through delta hedging, profit from the spread, and the technology behind modern options desks.
An honest review of the CQF programme - what it covers, who it's for, how much it costs, career outcomes, and whether it's worth the investment compared to an MFE or self-study.
A clear explanation of latency arbitrage - how HFT firms profit from speed advantages, the technology behind it, the ongoing debate about fairness, and how exchanges are responding.
A detailed breakdown of quant researcher salaries by seniority, firm type, and location - covering base pay, bonuses, total compensation, and how to maximise your earning potential.
The full SIG (Susquehanna International Group) interview guide - online assessments, the famous poker round, real probability and game-theory questions, and a six-week preparation plan that maps onto our coding tests.
The full Belvedere Trading interview guide - online assessments, the trader test, real probability and options questions, and a focused four-week prep plan from Chicago's options market-making firm.
The full Jump Trading interview guide - online assessments, the tech-heavy interview style, real coding and probability questions, and a six-week prep plan for one of the world's most secretive HFT firms.
The full Hudson River Trading (HRT) interview guide - the famous coding-heavy phone screens, real algorithm and probability questions, and a six-week prep plan from one of the world's top algorithmic trading firms.
The full Two Sigma interview guide - online assessments, the data-science-heavy phone screens, real coding and ML questions, and a six-week prep plan from one of the world's largest systematic hedge funds.
The full IMC Trading interview guide - the famous trader assessments, real probability and options questions, and a four-week prep plan from one of Amsterdam's leading options market-making firms.
The full Akuna Capital interview guide - the famous junior trader test, real probability and options questions, and a four-week prep plan for the Chicago options market-making firm.
The full DE Shaw interview guide - the famously hard quant research interviews, real coding and probability questions, and a six-week prep plan from one of the world's largest systematic hedge funds.
The full DRW interview guide - the diverse trader and engineering tracks, real coding and probability questions, and a five-week prep plan for one of Chicago's biggest and broadest prop trading firms.
The full Flow Traders interview guide - the famous trader test, real probability and ETF questions, and a four-week prep plan from one of Amsterdam's leading ETF market-making firms.
The full XTX Markets interview guide - the famously hard machine-learning quant researcher process, real coding and statistics questions, and a six-week prep plan from London's leading systematic FX firm.
The full Virtu Financial interview guide - the trader and engineering tracks, real probability and coding questions, and a five-week prep plan from one of the world's largest market makers.
The full Radix Trading interview guide - the famously selective process, real coding and probability questions, and a six-week prep plan from one of Chicago's smallest and highest-paying HFT firms.
The full Tower Research Capital interview guide - the famously deep coding rounds, real probability and systems questions, and a six-week prep plan for one of the world's largest HFT firms.
The complete Jane Street internship guide - the application timeline, OA, interview process, intern salary in London and New York, what interns actually work on, and how the return offer process works.
The complete Citadel and Citadel Securities internship guide - the application timeline, OA, interview process, intern salary in London, New York and Chicago, what interns work on, and how the return offer process works.
The complete Optiver internship guide - the application timeline, the famous trader assessment, intern salary in Amsterdam, Sydney, Chicago and London, what interns work on, and how the return offer process works.
Detailed Jane Street compensation breakdown by role and level - graduate trader, software engineer, quantitative researcher and senior trader pay in New York, London and Hong Kong, plus signing bonuses, year-one guarantees and partner-track economics.
Detailed Citadel and Citadel Securities compensation breakdown by role and level - portfolio manager, quant researcher, trader and engineer pay in Chicago, New York, London and Hong Kong, plus PM economics and signing bonuses.
Detailed Two Sigma compensation breakdown by role and level - quantitative researcher, software engineer and modeller pay in New York, London and Hong Kong, plus signing bonuses and bonus structure.
Detailed Hudson River Trading (HRT) compensation breakdown by role and level - software engineer, quantitative researcher, algo engineer and FPGA engineer pay in New York, London, Singapore and Boulder, plus signing bonuses and bonus structure.
The complete Barclays Quantitative Analytics guide - what the QA division does, the role split between London and New York, salary by level, the interview process and how to break in.
The complete Goldman Sachs Strats and QIS guide - what Strats actually does, the role split across asset classes, salary by level, the interview process and how to break in.
The complete JPMorgan Quantitative Research guide - what QR does across the investment bank, the role split between London and New York, salary by level, the interview process and how to break in.
30 of the most-asked probability questions in quant finance interviews, with worked solutions. Covers expected value, conditional probability, Bayes, generating functions, optimal stopping and martingales - everything that comes up at SIG, Jane Street, Optiver and beyond.
25 of the most-asked brain teasers in quant finance interviews, with worked solutions. Lateral-thinking puzzles, hat puzzles, weighing problems, racing horses, and the classics that come up at SIG, Jane Street and Optiver every cycle.
20 of the most-asked coding questions in quant developer and quant trader interviews, with worked solutions in Python and C++. Covers data structures, algorithms, low-latency systems and the patterns that come up at HRT, Jane Street, Citadel and Two Sigma.
50 mental math drills for quant trader interviews, plus the techniques that actually work for two-digit multiplication, percentages, square roots and rapid arithmetic. Built for SIG, Optiver, IMC, Akuna and Belvedere prep.
25 of the most-asked quantitative researcher interview questions, with worked solutions covering statistics, machine learning, signal design, time-series methodology and the practical questions that come up at Two Sigma, DE Shaw, XTX, Citadel and Renaissance.
25 of the most-asked quantitative trader interview questions, with worked solutions covering market making, options theory, mental math, market microstructure and the trading-game scenarios that come up at SIG, Optiver, IMC, Jane Street and Citadel Securities.
25 of the most-asked quant developer interview questions, with worked solutions covering systems design, low-latency C++, distributed systems, order books and the engineering questions that come up at HRT, Citadel Securities, Two Sigma, Jane Street and Jump Trading.
The complete 2026 roadmap for preparing for quant finance interviews - what to read, what to drill, how to schedule your prep, and which firms to target. Built for graduates, career-switchers and current quants targeting the 2026 recruiting cycle.
How to run effective mock interviews for quant finance prep - format, scoring rubric, common feedback patterns, where to find practice partners, and how to use Quantt's coding tests as structured practice.
20 of the most-asked derivatives pricing questions in quant finance interviews, with worked solutions covering Black-Scholes, Greeks, exotic options, fixed-income derivatives and the questions that come up at Goldman, JPM, Morgan Stanley, Barclays and SIG.
20 Python-specific quant interview questions, with worked solutions covering numpy internals, pandas memory layout, vectorisation, and the language depth that top firms (Two Sigma, Citadel, XTX, AQR) actually test.
20 C++-specific quant interview questions, with worked solutions covering the memory model, lock-free programming, modern C++ features, and the language depth that top firms (HRT, Jump, Citadel Securities, Tower, Radix) actually test.
15 of the most-asked linear algebra questions in quant finance interviews, with worked solutions covering eigenvalues, PCA, matrix factorisations, and the practical applications that come up at Two Sigma, AQR, BlackRock and DE Shaw.
15 of the most-asked time series questions in quant finance interviews, with worked solutions covering ARIMA, GARCH, cointegration, stationarity tests and the methodology pitfalls that come up at Two Sigma, AQR, DE Shaw, Renaissance and Citadel.
A side-by-side comparison of MFE, MFin and CQF qualifications - cost, duration, format, employer recognition, exit outcomes and the right choice for graduates, career switchers and working quants.
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.
Guide to becoming a quant developer - the technology role at the heart of quantitative finance. Covers required skills, salary expectations, career paths, and typical ways to break in.