ALGORITHMIC TRADING

The Algorithmic Trading Certificate, delivered in partnership with Enel and taught in English, aims to bridge the gap between theory and practice in computerized financial markets. Through four Decision Cases, students develop trading strategies, assess the associated risks and learn to implement selected strategies as algorithms. Within the programme, particular emphasis is placed on the Commodities Capstone Decision Case, which allows students to apply quantitative tools and decision models to crude-oil and refined-product markets, spot-futures relationships, and storage, transportation and refining decisions. The simulations require students to make individual real-time trading and risk-management decisions. English-language delivery broadens the pool of potential participants and develops the ability to communicate technical decisions in international settings and under time pressure.

By the end of the course, students will be able to: 1. understand order-driven markets, price formation, liquidity and the main order types; 2. interpret geopolitical and economic news and EIA inventory reports and assess their potential impact on crude-oil prices; 3. build models to forecast and value commodity spot and futures prices; 4. identify arbitrage opportunities between spot and futures markets, across different locations and between crude oil and refined products; 5. assess the economic and operational implications of storage, pipeline transportation and refining decisions; 6. design futures and options strategies to manage price and volatility risk; 7. identify and manage price risk, basis risk, operational risk, liquidity risk, transaction costs and position limits; 8. use Excel and real-time data to support decision-making and, in the Algorithmic Trading Case, turn basic arbitrage or market-making strategies into VBA/Python algorithms; 9. explain and communicate trading decisions in English during simulations and competitions.

General knowledge of financial markets and basic familiarity with Excel. Previous programming experience is not required. Students must be able to follow classes, materials and technical discussions in English.

The course uses the Rotman Interactive Trader (RIT) platform and Excel applications linked to real-time data to analyse price formation, liquidity provision and risk-taking in dynamic market conditions. The programme is organised around four Decision Cases: Commodities Capstone, Market Microstructure, Options Trading and Hedging, and Algorithmic Trading. Instruction is concentrated in the first two weeks: each four-hour meeting introduces two cases, normally for approximately two hours each. The final four weeks are devoted to application: one case per week, normally with approximately one hour of guided practice and approximately three hours of competition, including multiple heats or iterations, short strategy-review intervals and a final debrief. The distinguishing focus of the Algorithmic Trading Certificate, delivered in partnership with Enel, is the Commodities Capstone Decision Case. Students operate individually in markets for crude oil, futures contracts and refined products. They interpret geopolitical and economic news and inventory statistics, quantify their expected impact on prices and make trading decisions in spot and futures markets. The case is structured around four interacting models: the fundamental model, the storage model, the transportation model and the refinery model. Students assess the costs and economic benefits of using storage facilities, pipelines and refineries and identify arbitrage opportunities across maturities, locations and products. An Excel Support Sheet linked to real-time data is used to monitor markets and news, produce forecasts and generate trading signals. In the Commodities Capstone Case, orders are executed individually through the platform; automated order submission is addressed separately in the Algorithmic Trading Case. The Market Microstructure Case covers order-driven markets, market and limit orders, depth, spreads, liquidity and institutional orders. The Options Case addresses arbitrage relationships, delta-neutral strategies, hedging and volatility. The Algorithmic Trading Case introduces real-time data retrieval, arbitrage, market making, VBA/Python algorithms, testing and risk controls.

Rotman School of Management, University of Toronto, Release Files and teaching materials made available under the applicable terms of use: - Rotman School of Management, RIT Case Brief - COM5- Commodities Capstone; - Rotman School of Management, RIT2 Trader Support - COM5 - Commodities Capstone, Excel Support Sheet Template; - Market Microstructure 1 (Order-Driven Markets), Market Microstructure 2 (Liquidity), Market Microstructure 3 (Alternative Trading Venues); - Commodities 1 (Energy Trading), including Case Brief, Case Tutorial and Support Sheet; - Options 1 (Puts & Calls), Options 2 (Hedging), Options 3/4 (Trading Volatility); - Algorithmic Trading 1 (Arbitrage) and Algorithmic Trading 2 (Market Making); - RIT Real-Time Data and RIT VBA Introduction; - Case Description, Performance Evaluation Tool, instructor-prepared Excel models and supplementary notes. Microsoft, Visual Basic Developer Center: selected lessons and tutorials indicated by the instructor.

The course adopts an experiential learning approach and is delivered in English. The first two meetings combine short lectures, case briefings, platform demonstrations, Excel model building and guided exercises. The final four meetings are simulation and competition laboratories. Activities include individual case analysis, model building and calibration, real-time data analysis, practice sessions, competitions over multiple heats or iterations, and structured debriefs. In the Commodities Capstone Case, decisions and performance are individual; peer discussion is used to review models, strategies and results. Videos and other digital materials may be used when they support the learning objectives.

For the award of the Algorithmic Trading Certificate, assessment is based on four final applied assessments, one for each Decision Case. Each assessment is normally preceded by an approximately one-hour practice session, which has a preparatory function. The evaluation takes into account the results achieved, consistency across the different iterations, compliance with position and risk limits, model quality, strategic coherence and the ability to explain decisions. With particular reference to the Commodities Capstone Decision Case, the assessment also considers the interpretation of news and EIA data, forecasting quality, the identification of arbitrage opportunities and the consistency of storage, transportation and refining decisions. P&L is not considered in isolation from risk discipline and the quality of the decision-making process.

A final project may be assigned to students who demonstrate strong analytical ability in commodity markets, crude-oil price modelling, spot-futures relationships, arbitrage across locations and products, the economics of storage, transportation and refining, or risk management. At the end of the course, students with the strongest overall results may be considered for the preparation and selection process for the LUISS Team participating in the Rotman International Trading Competition. Selection will take into account competition results, quantitative and programming skills, risk discipline, reliability, teamwork and the ability to operate and communicate in English. Completion of the Certificate does not automatically imply selection for the Team.