CEC Applied Commodity Trading – Bridging Practice and Theory

The target audience would be, professionals in the commodity industry in a trading related role, or in the finance industry related to alternative investments. The participant could, for example, have a commodities or finance background, seeking a deeper understanding of trading and links to data science. Alternatively, the participant could be a data scientist working in such a sector, seeking a deeper understanding of the trading context to which the data science might be applied. The course would also target private investors who want to explore quantitative trading approaches in greater detail within these sectors.

The participants acquire both theoretical and practical skills in the following areas:

• Familiarity of the basic concepts in trading of commodity futures and CFDs.
• Familiarity with traditional market analysis tools as well as trade and risk management techniques.
• Develop a personal trading process based on clear rules and rigorous use of data.
• Develop an intuitive understanding of the mathematical logic behind the rules and data metrics.
• Develop ability to critically assess changes of a trading strategy from a quantitative perspective.
• Familiarity with systematic strategies and presence of CTA (Commodity Trading Advisor) funds in commodity markets

Description

Trading commodity denominated securities, such as futures or contracts for difference (CFDs), either for hedging or speculation, is typically driven by fundamental commodity knowledge together with an intuitive market understanding. However, the trading decisions are in practice largely based on data following strict pre-determined rules. For this reason, even a human-operated trading approach becomes a quantitative subject, where decisions should have logical support on mathematical grounds at the same level of rigor as the fundamental rationales. 

In this interdisciplinary continuing education course, the practical aspects of trading are reviewed with a quantitative analysis perspective. The course is built up in steps from the perspective of the trader. Each area is introduced from a practical point of view, including all elements of the typical trading process used in the commodity industry. In parallel, each of the practical areas will be explained within a data analysis context. Not all aspects of what is typical in practice can be explained scientifically, however, many can be explained within a mathematical framework. In these cases, the ability to identify the main ingredients as well as their relations from such a framework, provide valuable as well as complementary tools to develop a reliable trading process. Despite the particular focus on commodities in this course, the same approach can also be applied to other asset classes, e.g. FX rates, equity, stock- indices, CFD’s etc. 

Throughout the course, the practical as well as theoretical knowledge will be applied through developing a personal trading plan which will be assessed at the end. The quantitative view of each area should improve the ability to both understand the reasons behind rules and data metrics, as well as to adapt the approach including knowledge and principles from data science

Module “Concepts in trading”

Content: 

• Basic concepts related to trading commodity-based instruments, as well as to working with a data-driven process.

Objectives:

• Understand typical trading platform concepts, common type of charts and functionality. Different types of data and vendors, together with common issues and opportunities.

Module “Trading process”

Content: 

• Traditional technical as well as fundamental market analysis techniques, together with related empirical market results. Practice related to trade management, and review of different indicators.

Objectives:

• Familiarity with stylized facts observed historically in commodity market data. Process to control risk and reward objectives. Understand industry standard data and metrics.

Module “Managing risk”

Content:

• Practice related to risk management. Review aspects of sizing and portfolio risk. Systematic Strategies and CTA funds.

Objectives:

• Framework to manage risk. Develop a practical intuition about what is mathematically optimal with respect to risk and reward. Understand conceptual difference between discretionary vs systematic trading strategies, and presence of CTA funds in the commodity markets.

Module “Personal trading plan”

Content: 

• Development and simulation of personal trading plan. Examination based on a presentation of the final trading plan.

Objectives:

• Develop a personal trading plan, applying skills learned in the course.

The course will be delivered in weekly sessions, each consisting of six lectures. The lectures would take place in a classroom (Campus Lagerstrasse Zurich), however, the course can partly be followed online upon request. The students will be assigned self-study material as well as exercises between the weekly sessions. The self-study work will be followed up at the beginning of each proceeding session, where remaining questions will be addressed. The examination of the course will be based on a presentation of the participants personal trading plan, which will be built up during the course and which will be assessed in the final module with respect to all areas that have been covered in the course. The personal trading plan will be developed in a simulation environment within a professional trading platform.

Presentation

Participation in the course and successful completion of the assessment are certified with a microcredential worth 4 ECTS credits. Participants who attend the course without completing the assessment will receive a certificate of attendance without ECTS credits.

The course takes place every Wednesday from 2:00 p.m. to 7:10 p.m. (including a 30-minute break) on the following dates:

11.03.2026
18.03.2026
25.03.2026
01.04.2026
08.04.2026
15.04.2026
06.05.2026
13.05.2026

Team of instructors:

• Dr. Stefan Ask (data science) 
• Markus Amstutz (trading instructor)

Admission to the course generally requires a university degree or comparable professional competence.

The course is not a data science course, in the sense that it does not require any programming skills, nor familiarity with any specific statistical methods.