50 courses, every level of the ladder
Course catalog
Quantitative finance courses, self-paced and online, grouped by level. Open courses are free. Every paid course ends in an assessment marked by faculty and a certificate with a verification code.
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.
- 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.
- 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.
Foundation courses
Short courses that fix the mathematics, statistics and code every later course assumes.
- Algorithmic Trading Foundations 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.
- Mathematics for Quantitative Finance The calculus, linear algebra, optimisation and differential equations that pricing, risk and portfolio models are built on, revised and applied to finance.
- Modern Portfolio Theory 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.
- Paper Trading: From Simulation to Live Paper trading done properly: how simulated accounts fill orders, the gaps between paper and live results, how to design a paper period with a clear pass or fail, and a checklist before real money.
- Probability for Quantitative Finance The probability every quant course assumes: distributions, expectation, conditioning and the limit theorems, with each idea tested on real return data.
- Python 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.
- Statistics for Finance and Trading Estimation, testing and regression for financial data, with the traps that make trading research go wrong: dependence, fat tails and testing too many ideas.
- The Capital Asset Pricing Model The model that defines beta and the idea of a risk premium: the CAPM's assumptions and formula, estimating beta from data, using the model for cost of capital and performance, and the empirical tests that led to factor models.
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.
- Building a Backtester with Backtrader A working command of Backtrader, the open-source Python backtesting framework: feeds, strategies, indicators, realistic costs, position sizing and analyzers, and a clear view of what it cannot model.
- Execution Algorithms: VWAP, TWAP and Beyond How large orders are traded without moving the price against you: VWAP and TWAP schedules, implementation shortfall, market impact models, the Almgren-Chriss trade-off, and transaction cost analysis.
- Factor Investing 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.
- 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.
- Futures Trading for Systematic Traders The mechanics systematic traders need in futures markets: contract specifications, margin and leverage, rolling and continuous price series, carry, and building a diversified portfolio across asset classes.
- Implied Volatility and the Volatility Smile 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.
- Limit Order Books 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.
- 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 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.
- Option Greeks The sensitivities that options traders manage: delta, gamma, vega, theta and rho, how they behave across strikes and time, delta and gamma hedging, the link between gamma and theta, and hedging a book in simulation.
- Pairs Trading and Mean Reversion The pairs trade from selection to testing: how to find candidate pairs, the distance and cointegration methods, hedge ratios, spread z-scores, trading rules, and the reasons pairs stop working.
- Performance Measurement for Trading Strategies The statistics used to judge trading strategies and managers: the Sharpe ratio, the Sortino, Calmar and information ratios, drawdown, attribution, and the uncertainty in every one of them.
- Portfolio Optimization Portfolio construction that survives real data: estimating covariance with shrinkage, constrained and robust optimisation, risk budgeting and risk parity, hierarchical risk parity, and the costs of rebalancing.
- Position Sizing and the Kelly Criterion How much to bet: the Kelly criterion and its derivation, why full Kelly is dangerous with estimated edges, fractional Kelly, volatility targeting, and sizing across several strategies.
- Python for Algorithmic Trading 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.
- Quantitative Trading 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.
- Statistical Arbitrage 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.
- Stress Testing How to design and run stress tests on a portfolio: historical and hypothetical scenarios, shocking risk factors consistently, reverse stress testing, liquidity effects, and presenting results that lead to decisions.
- The Black-Scholes Model 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.
- Time Series Analysis for Finance The time series methods behind trading signals and risk forecasts: stationarity, autocorrelation, ARMA models, unit roots, volatility clustering and honest forecast evaluation.
- Trend Following Strategies 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.
- Value at Risk 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.
Researcher courses
Proof-level courses. Stochastic models, estimation theory and the literature behind each method.
- ARCH and GARCH Volatility Models The GARCH model and its family, from Engle's original ARCH to asymmetric and multivariate GARCH: derivation, maximum likelihood estimation, diagnostics and forecasting.
- Cointegration Models for Spread Trading The econometrics behind spread trading: unit roots, Engle-Granger and Johansen cointegration tests, vector error correction models, baskets of more than two assets, and optimal entry and exit for an Ornstein-Uhlenbeck spread.
- Expected Shortfall and CVaR The risk measure that replaced VaR in bank trading-book rules: coherence and subadditivity, estimating expected shortfall with historical, parametric and extreme value methods, the elicitability debate and backtesting, and minimising CVaR in portfolios.
- Fama-French Factor Models The empirical asset pricing toolkit: the Fama-French three- and five-factor models, time-series regressions and the GRS test, Fama-MacBeth cross-sectional regressions, standard errors, and how to judge whether a strategy has alpha.
- Financial Machine 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.
- Hidden Markov Models for Market Regimes Regime-switching models for markets: hidden Markov models, their algorithms and estimation, and how to use inferred regimes without fooling yourself.
- High-Frequency Trading 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.
- Low-Latency C++ for Trading Systems How a trading system gets from a market data packet to an order in microseconds: cache-aware data structures, lock-free communication, avoiding the operating system on the hot path, and measuring latency honestly. You need solid C++ before you start.
- Reinforcement Learning for Trading Reinforcement learning applied to sequential trading decisions: Markov decision processes, dynamic programming, Q-learning and policy gradient methods, and their use in optimal execution, market making and portfolio allocation, with the simulation and evaluation problems that limit them.
- Smart Order Routing How orders are split across competing venues: market fragmentation, best-execution rules, fee and rebate structures, fill probability and adverse selection by venue, and routing formulated as an optimisation problem.
- Stochastic Calculus for Finance 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.
- Stochastic Differential Equations for Finance How stochastic differential equations are solved, simulated and estimated, with the mean-reverting, short-rate and volatility models that use 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.
- The Black-Litterman Model The Black-Litterman model in full: reverse optimisation to implied equilibrium returns, expressing views, the Bayesian combination and its derivation, the choice of tau and the view uncertainty matrix, and extensions to views on factors.
Courses grouped into credentials
Several courses together, plus a capstone marked by faculty, earn a named Institute credential.