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 and a short course on paper trading. At Practitioner level it covers quantitative trading research, backtesting methods and validation, the Kelly criterion and position sizing, performance measurement, and futures trading for systematic traders.
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
Short courses that fix the mathematics, statistics and code every later course assumes.
- 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.
- Paper Trading: From Simulation to Live Paper trading done properly: how simulated accounts fill orders, the gaps between paper and live results, how to design a paper period with a clear pass or fail, and a checklist before real money.
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.
- Futures Trading for Systematic Traders The mechanics systematic traders need in futures markets: contract specifications, margin and leverage, rolling and continuous price series, carry, and building a diversified portfolio across asset classes.
- 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.
- 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.
- 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.
Credentials that use this track
- Foundation Certificate in Computational FinanceThe entry quantitative finance certificate. Four Foundation courses that fix the probability, statistics and Python every later course assumes, and a first look at how trading systems are built.
- Certificate in Algorithmic TradingSix 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.
- Certificate in Statistical ArbitrageFive courses and a capstone on market-neutral trading: time series foundations, pairs and baskets, cointegration and the validation that keeps spread strategies honest.
- Certificate in Systematic InvestingSix courses and a capstone on rule-based investing over months and years: asset pricing, trend following, factors and the portfolio construction that holds them together.
- Certificate in Quantitative Portfolio ManagementSix courses and a capstone on building portfolios with numbers: mean-variance and its failures, robust optimisation, Black-Litterman views, factors, attribution and risk.
- Professional Diploma in Algorithmic TradingThe 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.
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.