Credential program on the practitioner path
Certificate in Statistical Arbitrage
Five courses and a capstone on market-neutral trading: time series foundations, pairs and baskets, cointegration and the validation that keeps spread strategies honest.
Statistical arbitrage bets that prices which have moved together will keep doing so, and trades the gap when they drift apart. The idea is simple. The difficulty lies in telling a stable relationship from a coincidence, in surviving the times when a stable relationship breaks, and in keeping enough of a small edge after costs. The Certificate in Statistical Arbitrage teaches all three.
What does the certificate cover?
Time series models for prices and spreads; the construction of pairs and baskets; cointegration tests and error-correction models; signal design for mean reversion; and the validation and cost modelling that a market-neutral book depends on.
Who is it for?
Quantitative traders and researchers who want a rigorous grounding in market-neutral strategies, and engineers moving into systematic equity or futures desks.
The courses in this program
- 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.
- 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.
- 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.
- 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.
- 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.
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.
What is the statistical arbitrage capstone?
You build a market-neutral portfolio of spreads on a universe you choose: select pairs or baskets, test for cointegration, set entry and exit rules, and size and cost the book. It is judged on data held out from development. The report must show how much of the result survives costs and how the strategy behaves when relationships break.
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
Is statistical arbitrage still profitable?
Simple pairs strategies on large stocks have been crowded for decades, and the course says so. What remains is careful work on universes, costs and regime changes. The certificate teaches the method; it makes no promise about returns.
What background do I need?
Statistics to regression level and working Python. Time Series Analysis for Finance is part of the program.
How long does the certificate take?
154 hours of study. At eight hours a week, about five months.