Credential program on the practitioner path
Professional Diploma in Algorithmic Trading
The top credential on the practitioner path. Eight courses and a 40-hour capstone covering everything between a trading idea 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 sceptic 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 and then asks you to run them together.
What does the diploma cover?
Quantitative trading research and backtesting validation; Kelly and fractional sizing; performance measurement and the statistics of luck; execution algorithms and transaction cost analysis; statistical arbitrage; Python for trading systems; and value at risk for book-level limits.
Who is it for?
Practitioners who have completed a specialist certificate or have equivalent experience, and who want a single credential that shows they can build and run a systematic book, not just a backtest.
The courses in this program
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
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: [email protected].
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
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. Faculty examine it in a recorded viva of about forty minutes.
Is this credential accredited?
No. It is an unaccredited Institute credential. It records that you passed every assessed component listed on this page, marked by the Institute's faculty. It is not a university degree and is not issued by Carnegie Mellon University. Each credential carries a verification code that an employer can check with [email protected].
Questions applicants ask
How is the diploma different from the Certificate in Algorithmic Trading?
The certificate covers one strategy from idea to validated result. The diploma adds execution, statistical arbitrage and risk, and its capstone runs two strategies as one book. Five certificate courses count toward the diploma, so the certificate is a natural first step.
Is the diploma a university qualification?
No. It is an unaccredited Institute credential. It is not a degree and is not issued by Carnegie Mellon University.
How long does the diploma take?
238 hours of study. At eight hours a week, about seven months.