52 courses, every level of the ladder
Course catalog
Quantitative finance courses, online and taught in cohorts, grouped by level. Open courses are free. Every paid course ends in a certificate with a verification code. Taken as part of a Certificate, Diploma or Fellowship, a course also ends in a proctored exam with a pass mark of 60%.
Three sample explainer films
Each course comes in three formats. An explainer film for each advanced idea, drawn and narrated so that the idea can be followed on a first viewing. An audio edition of the general subject, for study away from the desk. And a workbook with notes, worked exercises and a cheat sheet for every unit. The three films below are samples from the courses they link to.
Open courses
Free primers. Read, watch and work the exercises before you spend anything.
- AI Trading: What Works and What Does Not A sober introduction to artificial intelligence in trading: where machine learning is used by professional firms, why most AI trading claims fail on inspection, the specific traps of financial data, and how to judge a strategy or product that says it uses AI.
- Algorithmic Trading: An Open Introduction What is algo trading? A plain account of what an algorithm decides, the main families of strategy, the costs that eat returns, and what it takes to test an idea honestly.
- High-Frequency Trading Explained What is high-frequency trading? A plain account of what HFT firms do, how market making and arbitrage earn small amounts many times a day, why speed matters, and what the evidence says about its effect on markets.
- How to Start Trading: An Open Introduction How to start trading without the usual expensive first year: what an account and a broker are, how much capital you need, what each trade costs, how to limit a loss, and how to write a first plan before the first order.
- Introduction to Quantitative Finance What is quantitative finance? A free primer on what the field is, what quants actually do in banks, funds and trading firms, and what you need to learn to join them.
- Is Day Trading Gambling? An Open Course Is day trading gambling? Sometimes it is. This course shows the difference in numbers: whether the expected result of a trade is positive after costs, whether you can show it on a record, and whether you can stop.
- Monte Carlo Simulation, From First Principles What is a Monte Carlo simulation? A free course that builds one from first principles: random numbers, the law of large numbers, simulated price paths and their error bars.
- What Is Drawdown in Trading? An Open Course What is drawdown in trading, and why does it end more trading careers than any single loss? Peak-to-trough losses, the arithmetic of recovery, losing streaks, and how size decides the drawdown you will live through.
Foundation courses
The mathematics, statistics, data and market knowledge every later course assumes.
- Algorithmic Trading Foundations: From Idea to Paper to Live An algorithmic trading course on the working basics: how electronic markets match orders, the order types an algorithm uses, three simple strategy families, the costs of trading, and a first backtest you can defend. It also teaches paper trading, and how to decide when a strategy is ready for real money.
- Day Trading for Beginners: Intraday Trading Done Properly Day trading for beginners without the sales pitch: how the trading day is structured, which instruments suit it, the rules and costs that apply, the main setups, and the evidence on how many day traders make money. It also teaches intraday trading routines and limits.
- Financial Markets and Instruments A financial markets course for people who will build models of markets: what each instrument is, who trades it, where and how it trades, and what happens between the click and the settled position.
- How to Trade Stocks: Orders, Costs and the Spread How to trade stocks with a clear picture of what happens behind the button: exchanges and brokers, every common order type, margin and short selling, corporate actions, settlement and the records you keep for tax. It also teaches the bid-ask spread and every other cost of a trade.
- Mathematics for Quantitative Finance: Calculus and Linear Algebra The calculus, linear algebra, optimization and differential equations that pricing, risk and portfolio models are built on, revised and applied to finance. It also teaches the linear algebra for finance behind portfolios and factor models.
- Modern Portfolio Theory and the CAPM Markowitz's framework from first principles: expected return and covariance, the benefit of diversification, the efficient frontier, the minimum variance and tangency portfolios, and the capital market line, all computed in Python. It also teaches the capital asset pricing model.
- Probability for Quantitative Finance, with Statistics The probability every quant course assumes: distributions, expectation, conditioning and the limit theorems, with each idea tested on real return data. It also teaches the statistics for finance that every model rests on.
- Python and SQL for Finance A Python for finance course with the Python every later course assumes: loading and cleaning market data with pandas, computing returns and risk with NumPy, plotting, and writing code another person can rerun. It also teaches SQL for finance, so your data lives in a database rather than in files.
- Stop Losses, Position Sizing and Your Trading Edge Where to put a stop loss and how large a position to take, as one decision: fixed-fraction sizing, volatility-based stops, the risk reward ratio, daily and monthly loss limits, and what happens when the market gaps through your stop. It also teaches how to measure your trading edge as expectancy.
- Technical Analysis: Reading Charts and Price Action Technical analysis as a set of claims you can check: moving averages, momentum oscillators, volume indicators and chart patterns, what each measures, what the published evidence says, and how to test one on your own market with a spreadsheet. It also teaches how to read stock charts from scratch and price action trading.
- Trading Psychology, the Trading Journal and Overtrading Trading psychology taught from the research on decisions under risk: why traders hold losers and cut winners, how overconfidence grows after a good run, what tilt and revenge trading look like, and the routines that protect you from yourself. It also teaches the trading journal and overtrading and how to stop it.
Practitioner courses
Lab-based courses. You build, test and cost real strategies and systems on market data.
- Backtesting: Methods, Pitfalls and Validation Why most backtests lie and how to build one that does not: the biases that inflate results, realistic execution, walk-forward and cross-validation designs, and statistics that account for how many ideas you tried.
- Bond Pricing, Yield Curves and Mortgage-Backed Securities Bond pricing and the yield curve from first principles to desk practice: price and yield, duration and convexity, bootstrapping and fitting curves, carry and roll-down, and hedging a bond portfolio's interest rate risk. It also teaches mortgage-backed securities and prepayment risk.
- Factor Investing and the Fama-French Models How equity factors are defined, built and tested: value, momentum, size, quality and low volatility, the construction of long-short factor portfolios, combining factors, and the multiple-testing problem that hides behind hundreds of published factors. It also teaches the Fama-French factor models.
- FRM Exam Preparation: Part I Structured preparation for the FRM Part I exam: the four Part I topic areas taught with worked problems, timed practice sets, and a mock exam. Independent preparation; not affiliated with or endorsed by GARP.
- Fundamental Analysis, News Trading and Global Macro Fundamental analysis reads a company's accounts to judge what it is worth and how its results may move the share price. This course teaches the three financial statements, the main valuation ratios, earnings quality and guidance, and how traders use fundamentals to choose which stocks to trade and which events to respect. It also teaches news trading around scheduled releases and global macro trading.
- How to Trade Futures and Read Order Flow How to trade futures without learning the hard way: what a contract is, how tick values and margin turn a small move into a large profit or loss, how expiry and rolling work, which contracts suit a small account, and how to size positions so leverage never decides for you. It also teaches order flow trading.
- Implied Volatility, the Smile and Volatility Trading What option prices say about expected volatility: solving for implied volatility, the smile and skew, the term structure, building and fitting a volatility surface, and the no-arbitrage conditions a surface must satisfy. It also teaches volatility trading: variance swaps, straddles and the volatility risk premium.
- Machine Learning for Trading Applying machine learning to trading problems without fooling yourself: building features and labels from market data, linear, tree and neural network models, time-series cross-validation, feature importance, and turning predictions into a tested strategy.
- Model Risk Management and Basel III Capital How financial institutions control the risk of using models: what counts as a model, inventories and tiering, independent validation, conceptual soundness, benchmarking and outcomes analysis, ongoing monitoring, and the governance expected by supervisors. It also teaches Basel III market risk capital.
- Options Strategies Options strategies taught the way a professional builds them: from payoff and Greeks, with a view on direction, volatility and time, then sized, tested on history and costed for spreads and assignment.
- Options Trading for Beginners: Calls, Puts and Risk Options trading for beginners, taught with payoff diagrams and a spreadsheet instead of formulas: what calls and puts are, how premium, time decay and implied volatility set the price, the common strategies such as covered calls and spreads, and the ways options lose money for the people who buy them.
- Order Books, Execution Algorithms and Trading Costs How the limit order book works and what it reveals: matching rules, queue priority, depth and liquidity, order flow imbalance, and the short-term price dynamics that execution and market-making strategies depend on. It also teaches VWAP and TWAP execution algorithms, transaction cost analysis and smart order routing across venues.
- Portfolio Optimization, Risk Parity and Black-Litterman Portfolio construction that survives real data: estimating covariance with shrinkage, constrained and robust optimization, risk budgeting and risk parity, hierarchical risk parity, and the costs of rebalancing. It also teaches asset allocation and risk parity and the Black-Litterman model.
- Prop Trading Firms and How Bank Traders Think Prop trading firms offer traders a funded account after a paid evaluation. This course explains how the evaluations and their rules work, why trailing drawdown rules fail so many traders, what the fees and payouts mean for your expected return, and how to use your own record to decide whether, and how, to attempt one. It also teaches how bank and proprietary traders think about risk.
- Python for Algorithmic Trading, with Backtrader How research code becomes a trading system: an event-driven engine, a broker connection, order and position state, risk checks, logging, and a strategy that runs on a schedule without you watching it. It also teaches Backtrader, the open-source backtesting library.
- Quantitative Trading: Edge, Sizing and the Sharpe Ratio The research process behind quantitative trading: forming a hypothesis, building a signal, turning signals into positions, accounting for costs and capacity, and deciding whether an edge is real. It also teaches the Sharpe ratio and the other measures of trading performance and position sizing with the Kelly criterion.
- Statistical Arbitrage, Pairs Trading and Cointegration How statistical arbitrage strategies are built and tested: mean-reverting spreads, residuals from factor models, market-neutral portfolio construction, trading rules, transaction costs and the ways stat arb strategies decay. It also teaches pairs trading and mean reversion and cointegration and spread trading.
- Swing Trading and Momentum Trading Swing trading holds positions for days to weeks, which suits people who cannot watch a screen all day. This course covers scanning for candidates, the main setups, the overnight and gap risk that comes with holding, earnings and event dates, and a weekly routine you can keep. It also teaches momentum trading.
- The Black-Scholes Model and the Option Greeks Option pricing from the ground up: payoffs and no-arbitrage bounds, the binomial model, the replication argument, the Black-Scholes equation and formula, risk-neutral valuation, and numerical pricing in Python. It also teaches the option Greeks and hedging with them.
- The Trading Plan and Evidence-Based Trading A trading plan turns a set of habits into a written business. This course covers where trading ideas come from, which information sources are worth your time and money, how to choose markets and setups, the risk limits that protect the account, and the review cycle that keeps the plan honest. It also teaches evidence-based technical analysis.
- Time Series Analysis and Financial Econometrics The time series methods behind trading signals and risk forecasts: stationarity, autocorrelation, ARMA models, unit roots, volatility clustering and honest forecast evaluation. It also teaches financial econometrics: regression, its failures and their fixes.
- Trend Following Strategies, Carry and Systematic Futures How systematic trend followers trade: time-series momentum, moving average and breakout signals, volatility-targeted sizing, diversified futures portfolios, and what a century of evidence says about returns and drawdowns. It also teaches systematic futures trading: rolls, margin and contract choice and the carry trade in currencies and commodities.
- Value at Risk, Expected Shortfall and Stress Testing How value at risk is calculated, tested and misused: historical simulation, variance-covariance and Monte Carlo methods, volatility-weighted variants, time scaling, regulatory backtesting, and what VaR does not tell you. It also teaches expected shortfall, also called CVaR and stress testing and scenario design.
Researcher courses
Proof-level courses. Stochastic models, estimation theory and the literature behind each method.
- Alternative Data and NLP for Finance Alternative data and natural language processing as funds use them: turning news, filings and earnings calls into signals with language models, judging satellite, card and web data, and testing whether any of it adds return once costs and timing are honest.
- C++ for Quantitative Finance and Low-Latency Systems Modern C++ for the code where speed matters: pricing libraries, simulation engines and trading components. Memory, ownership, templates, the standard library, testing and profiling, and exposing C++ to Python research code. It also teaches low-latency C++ for trading systems.
- Credit Default Swaps, Copulas and Counterparty Risk Credit default swaps from the contract to the model: what is protected and paid, hazard rates and survival curves, bootstrapping a CDS curve with the ISDA standard model, index products, and the basis between bonds and CDS. It also teaches copula models for portfolio credit risk and counterparty credit risk and CVA.
- Financial Machine Learning: Deep and Reinforcement Learning The research methods developed for machine learning on financial data: sampling with information-driven bars, triple-barrier labels and meta-labeling, sample weights for overlapping outcomes, purged cross-validation, feature importance, and statistics of backtest overfitting. It also teaches deep learning for finance and reinforcement learning for trading.
- GARCH Models, Market Regimes and Bayesian Methods The GARCH model and its family, from Engle's original ARCH to asymmetric and multivariate GARCH: derivation, maximum likelihood estimation, diagnostics and forecasting. It also teaches hidden Markov models for market regimes and Bayesian statistics for estimation under uncertainty.
- High-Frequency Trading and Market Making The models behind high-frequency strategies: optimal market making with inventory risk, adverse selection, short-horizon signals from the book, latency arbitrage, and how to test strategies whose results depend on queue position. It also teaches market making strategy and inventory risk.
- Interest Rate Models The models banks use to price options on interest rates: short-rate models from Vasicek to Hull-White, the Heath-Jarrow-Morton framework, the market models behind caps and swaptions, the move from LIBOR to SOFR, and calibration to market prices.
- Numerical Methods in Finance The numerical methods behind every pricing and risk system: Monte Carlo and its variance reduction, finite differences for pricing equations, lattices, Fourier methods, root finding and optimization for calibration, with attention to error and stability.
- Stochastic Calculus and Stochastic Differential Equations Brownian motion, the Ito integral, Ito's lemma, change of measure and martingale pricing, derived carefully and applied to the models used on derivatives desks. It also teaches stochastic differential equations and how to solve them.
- Stochastic Volatility and the Heston Model Stochastic volatility in depth: why volatility needs its own process, the Heston model and its characteristic function, Fourier pricing methods, calibration to a volatility surface, simulation schemes, and rough volatility in outline.
Courses grouped into credentials
Several courses together, plus a capstone assessed by the faculty, earn a named Institute credential.