Overview
This course provides a practical and analytical introduction to the design, evaluation, and governance of algorithmic trading systems for commodity markets. Topics include market microstructure, order books, liquidity, bid-ask spreads, tick data, latency, order types, execution algorithms, signal generation, feature engineering, trend and mean-reversion strategies, seasonality, calendar and cross-commodity spreads, inventory signals, and event-driven behaviour.
Students work with data and examples from metals, energy, agriculture, and freight markets, distinguishing exchange-traded derivatives from physical commodity markets. Technical content covers data ingestion and cleaning, backtesting, transaction costs, slippage, market impact, look-ahead bias, survivorship bias, overfitting, train-test validation, paper trading, monitoring, deployment, and version control.
Risk management and governance address position limits, pre-trade checks, kill switches, model validation, audit trails, cybersecurity, explainability, and compliance with market-conduct rules. Through practical development and documentation, students prototype a strategy, evaluate performance realistically, diagnose weaknesses, implement safeguards, and present a complete system design.
Learning Outcomes
- Analyse commodity-market microstructure, liquidity, order types, spreads, latency, and execution constraints.
- Construct robust data-ingestion and cleaning workflows for tick, order-book, and market-event data.
- Engineer and justify features and trading signals for trend, mean-reversion, seasonal, spread, inventory, and event-driven strategies.
- Implement and document a prototype algorithmic trading system using appropriate software engineering and version-control practices.
- Evaluate strategy performance using realistic transaction costs, slippage, market impact, validation procedures, and risk-adjusted metrics.
- Diagnose look-ahead bias, survivorship bias, overfitting, data leakage, regime dependence, and other sources of unreliable backtest results.
- Design paper-trading, monitoring, deployment, and incident-response procedures for algorithmic systems.
- Implement safeguards including position limits, pre-trade checks, kill switches, model validation, audit trails, cybersecurity controls, and explainability measures.
- Assess the operational and regulatory implications of algorithmic trading and compliance with market-conduct rules.
- Present and defend a documented commodity algorithmic trading system design to a technical and professional audience.
Timetable
| Type | Length | Frequency | Period |
|---|---|---|---|
| Lecture | 2 hours | Weekly | All semester |
| Lab | 2 hours | Weekly | All semester |
| Tutorial | 1 hour | Weekly | All semester |
| Workshop | 2 hours | Fortnightly | Second term |
Assessment Schedule
| Type | Description | Weighting |
|---|---|---|
| Assignment | Market data and microstructure analysis | 15.00% |
| Assignment | Feature engineering and strategy prototype | 15.00% |
| Test | Technical concepts and quantitative methods test | 15.00% |
| Deliverable | Backtesting and validation report | 15.00% |
| Deliverable | Risk, governance, and deployment plan | 10.00% |
| Capstone | Documented algorithmic trading system and presentation | 30.00% |
Prerequisites
Teaching Staff & Programs
This course is delivered jointly by faculty from the participating programs listed below. In line with the Douchewater Way, the University of Sexology tailors core instruction directly to each cohort's specific discipline — adapting curriculum to program needs rather than forcing students into a one-size-fits-all model. Learn more about our approach at The Douchewater Way.
