Specialist track, course code AT
Algorithmic Trading
Strategy design, backtesting, position sizing and performance measurement, from the first rule-based system to a costed, validated strategy.
An algorithmic trading strategy is a rule that turns data into orders, and a claim that the rule makes money after costs. Most of the work lies in testing that claim honestly: defining the rule before seeing the result, charging realistic costs, sizing positions so a losing run does not end the account, and measuring performance in a way that separates skill from luck.
What will I learn in the algorithmic trading track?
The track opens with a free introduction, then Algorithmic Trading Foundations, which ends with paper trading and the step to real money. At Practitioner level, Quantitative Trading covers research, the Kelly criterion and the Sharpe ratio in one course, Backtesting Methods covers validation, and Python for Algorithmic Trading builds a full strategy in Backtrader.
How is this different from a trading signals course?
No course in the track sells a strategy. Each one teaches a method and then asks you to apply it to market data you choose, with costs charged, and to defend the result in an assessed project.
Open courses in this track
Free primers. Read, watch and work the exercises before you spend anything.
Foundation courses in this track
The mathematics, statistics, data and market knowledge every later course assumes.
Practitioner courses in this track
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.
- 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.
Credentials that use this track
- Foundation Certificate in Computational FinanceThe entry quantitative finance certificate, and the opening stage of both the Professional Diploma and the Fellowship. Six courses that settle the mathematics, statistics, data and market knowledge every later course assumes, closed by a capstone.
- Certificate in Algorithmic TradingSeven courses and a capstone that take a strategy from idea to a costed, sized and validated system, traded on paper and reported the way a trading desk would read it.
- Certificate in Statistical ArbitrageSeven courses and a capstone on market-neutral trading: time series foundations, pairs and baskets, cointegration, regimes, costs and the validation that keeps spread strategies honest.
- Certificate in Quantitative Portfolio ManagementSeven courses and a capstone on building portfolios with numbers: mean-variance and its failures, robust optimization, Black-Litterman views, factors, asset allocation, bonds, attribution and risk.
- Professional Diploma in Algorithmic TradingThe 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.
- Fellowship in Computational FinanceThe top credential on the researcher path. Twenty-one courses and a dissertation supervised in writing and examined by two faculty members.
Questions applicants ask
Can I learn algorithmic trading without a finance degree?
Yes. Algorithmic Trading Foundations assumes basic statistics and Python, not a finance degree. Market mechanics are taught inside the course.
Which credential does this track lead to?
The Certificate in Algorithmic Trading. Its courses also count toward the Professional Diploma in Algorithmic Trading, the top credential on the practitioner path.