Credential program on the practitioner path
Certificate in Algorithmic Trading
Six courses and a capstone that take a strategy from idea to a costed, sized and validated system, with performance reported the way a trading desk would read it.
An algorithmic trading strategy makes a claim: that a rule applied to data produces returns after costs. The Certificate in Algorithmic Trading is about testing that claim properly. It covers how to state a strategy before testing it, how to build a backtest that does not leak future information, how to charge realistic costs, how to size positions so that a bad run does not end the account, and how to report performance so that a reader can tell skill from luck.
What will I be able to do after the certificate?
Build a research and backtesting pipeline in Python, validate a strategy with walk-forward and out-of-sample tests, size it with the Kelly criterion and its fractional variants, and report it with Sharpe, drawdown and the statistics that show how much of the result could be chance.
Who is it for?
Software engineers moving into trading, discretionary traders who want to systematise, and analysts who review strategies and need to know what a sound backtest looks like.
The courses in this program
- 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.
- 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.
- 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.
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].
- 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 algorithmic trading capstone?
You design one strategy, write its rules and its test plan before looking at the result, then build, cost and size it in Python. It is judged on data held out from development. The submission is the code and a report of about 3,000 words that a risk manager could read and act on. Faculty mark it against published criteria: whether the test plan was followed, whether costs and sizing are realistic, and whether the conclusions match the evidence.
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
Will the course give me a profitable strategy?
No course here sells a strategy. The certificate teaches a method for testing whether a strategy works, and most ideas fail that test. Learning to reject them cheaply is the skill.
What programming level do I need?
Comfortable Python: functions, pandas data frames and reading other people’s code. Python for Algorithmic Trading is part of the program and covers the trading-specific parts.
How long does the certificate take?
158 hours of study. At eight hours a week, about five months.