Credential program on the practitioner path
Professional Diploma in Algorithmic Trading
The top credential on the practitioner path. Twenty-one courses and an 80-hour capstone covering everything between the mathematics and a book you could defend to a risk committee.
A trading operation is more than a strategy. It is research that can be repeated, validation that a skeptic would accept, sizing that survives losing runs, execution that does not give the edge back, and risk limits that someone outside the strategy can monitor. The Diploma in Algorithmic Trading covers each of these in four certificates and then asks you to run them together.
What does the diploma cover?
The Foundation Certificate lays the mathematics, statistics, econometrics, Python, SQL and market knowledge. The Certificate in Algorithmic Trading covers research, backtesting, time series, pairs and statistical arbitrage, sizing, machine learning and paper trading. The Certificate in Systematic Investing covers trend, factors, carry, asset allocation, futures, options and risk. The Certificate in High-Frequency Trading covers market microstructure: order books, execution and its costs, market making, routing and low-latency C++.
Who is it for?
People who want a single credential that shows they can build and run a systematic book, not just a backtest.
The courses in this program
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
What every course contains
Every paid course comes with the first seven and the certificate, whichever level it sits at. The proctored exam and the credential come when the course is taken as part of a Certificate, Diploma or Fellowship. Everything is self-paced: the material is yours to study in any order, at any hour, for the whole of the license.
- Workbooks. Written to train the thinking patterns and the important concepts of the course, with worked problems and the reasoning behind each answer, so that you learn to think the way the subject is thought.
- Audio edition. Focused, well-presented recordings with explainers and examples of the material, made to be interesting, relevant and entertaining, so that the general subject can be learned away from the desk.
- Notes. Pre-produced notes with every fact the course relies on, written out in full, so that nothing has to be transcribed from the films or the audio.
- Cheat sheet. One sheet with every important learning concept of the course on it, to keep beside you during the work and to revise from before the exam.
- Named sources. Every fact in the notes names its source, the paper, the book or the dataset, for anyone who wants to dig in further.
- Explainer films. Focused films, in the style of the sample films on the catalog page, that walk through the most difficult concepts of the course with visual explanations of a high standard.
- Transcript. A full transcript of every film and every audio recording, searchable and quotable.
- Proctored exam. Taken as part of a Certificate, Diploma or Fellowship, the course ends in a proctored multiple-choice questionnaire, with a pass mark of 60% and three retakes included. A course taken on its own is assessed by its coursework and has no exam.
- Verifiable certificate. A certificate of completion that a third party can verify: an employer sends the code on it to the credentials office and receives written confirmation of the course, the date and the result.
- Credential of the program. The Certificate, Diploma or Fellowship of the program, track or path the course belongs to, awarded when every course in it is passed. Which one depends on the program you choose.
What should you know before you start?
The courses in this program build on the courses below. They are not part of the program and not included in its price. If you have not studied this material, take them first. Admissions can tell you whether your background already covers them: the academy office.
- 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.
- 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.
What is the diploma capstone?
You run a complete systematic trading operation on paper: two uncorrelated strategies researched and validated on held-out data, sized as one book, executed through a simulated algorithm with realistic costs, and monitored with VaR limits. The submission is the code, a research memo for each strategy and a risk report for the book. Two faculty members examine it against published criteria and return a written report.
What does the credential record?
It records that you passed the proctored exam of every course listed on this page and the capstone, assessed by the Institute's faculty. The credential is issued by the Institute. Each one carries a verification code that an employer can check with the credentials office.
Are there scholarships and financial aid?
A small number of need-based awards are made each intake to students who could not otherwise study, on documented evidence of financial hardship. Most applications are declined. Before you apply, look at what this program already saves you: it costs less than its courses bought alone, and full-fee students are what make every award possible. Read the requirements before you apply.
Questions applicants ask
How is the diploma different from the Certificate in Algorithmic Trading?
The certificate is one of four parts of the diploma: the Foundation Certificate, the Certificate in Algorithmic Trading, the Certificate in Systematic Investing and the Certificate in High-Frequency Trading, and its capstone runs two strategies as one book. Each certificate you pass counts toward the diploma.
How does an employer check the diploma?
Each diploma carries a verification code. The credentials office confirms the holder, the date of award, the courses passed and the capstone result to anyone who writes with that code.
How long does the diploma take?
234 hours of material across the twenty-one courses, each hour with its audio edition and workbook, and a capstone of 80 hours of your own work. The license runs 24 months, so you set the pace around a job.