The presence of uncertainty is what gives rise to trading opportunities. Understanding what creates and drives uncertainty is a critical skill for any trader across the commodities spectrum (oil, refined products, natural gas, power, emissions, agricultural, base and precious metals).
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Jan 2020 Apr 2020 Jul 2020 Oct 2020 Jan 2021
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Through Time
Potential Profit and Loss
Professor: Philippe Cote 2020-01-06
Overview and Objectives
This course has been designed and is taught by market professionals.
The curriculum reflects what a trader development program would look like in industry with a strong trading analytics base to grow onto as a trader in today’s marketplace. We complement the core curriculum with external guests from industry. All analytics is in the R language and you are expected to learn analytical concepts and become proficient in your ability to implement them with real world data. The course is designed such that no prior experience is required other than your commitment to learn.
This course has the following key components:
• Develop a strong foundation using the tools of today and tomorrow (Data Science tools) required in the workplace at top organizations.
• Periodic trading simulations as we learn through experimentation i.e. learn from mistakes.
• We emphasise developping market analytics knowledge in core commodity markets that is transporatable over learning about market structure amd nomenclature across all markets. Succesfull traders have transferable skills across markets - they know what to look for and how to approach a new market they are exposed to.
• Develop self awareness on where your interests and talent are best de- ployed in a career where commercial optimization and trading is critical to delivery of the business model.
• A focus on trading analytics, starting with market fundamentals and for- ward pricing, for the purpose of understanding risk/reward in commercial strategies. This is NOT a micro or macro economic course focusing solely on market fundamentals (supply and demand balance). Our area of interest is, having formed a view on market fundamentals, to what extent the infor- mation is reflected in current prices and the risk/reward associated with the various ways to monetize it.
• Unique modules on:
– Algorithmic trading which is not only of growing interest but a fantastic tool to learn and teach the basics of discipline in trading.
– Negotiation skills game.
– Trading regulations.
• Periodic direct interactions with trading professionals.
Everything we do and learn in class has a purpose. Whilst it may not be obvi- ous at all times, if unclear I encourage you to ask. It constitutes an opportunity for all to learn.
The professor has extensive leadership experience in trading and risk man- agement and always open to any questions.
Who should be interested in this class?
• You are interested in learning about trading.
• You have an interest in commodities more specifically. Because of its phys- ical nature, trading in commodities has idiosyncrasies not present in main- stream financial products. It is a growth market that requires specialized skills.
• You are interested in developing your analytical skills: use of Data Science tools, practical knowledge of risk management, speak and understand trader language to interact effectively with them.
• You are interested in a career in public policy and want a solid foundation to understand market microstructure.
• Gain effective knowledge that would also enable you to make a judgment about the quality of opportunities offered to you as you start your career.
Who may NOT be interested in this class?
• You are interested in a course solely focused on trading simulations.
• Your have an interest in learning about trading that is unmatched by a ded- icated commitment to learn about trading analytics modern techniques i.e data science workflow.
• We do not work in MS Excel and MS Powerpoint.
Prerequisites
• This course will draw from knowledge gained in basic statistics and finance classes.
• A strong personal drive and desire to learn.
• Whilst no prior experience is required, familiarity with numerical program- ming is an asset. The course is designed to take you from no prior experi- ence to intermediate level provided you have the commitment to do so.
Supporting Material
• The class notes will be self-contained and available as an online book for your perusal.
• We will make extensive use of market data APIs for data analysis, including Morningstar Marketplace.
• You will also make extensive use of public websites, among other, the Energy Information AdmistrationandRBN Energy.
Throughout this course, you will learn to use data science tools. We will be using the R software as a tool.
Course Schedule
Will be shared in class.
Learning Path
• Trading, and the class requires a high degree of self learning and is more mini-Capstone project oriented.
• Constant attention is required in trading simulation and in the learning journey i.e. not something conducive to studying the night before the class.
• Everything we learn from a technical skills standpoint is provided through class notes with both theory, market application and clear and reproductible examples. You must do you homework after class to understand class note material. This course builds on from week to week.
• The approach is to review the course material and show you along the way how to implement it in an integrated fashion (analyze/strategize/think, code, visualize, present) using R as the data science tool.
• I will be available if you have questions outside the course times. Send me an email [email protected] your contact details and questions.
Grading
Yourgradeswill be assessed as shown below. Your final grade will be based on your absolute achievement and relative performance in the class.
Category Measure Description
Assigmement 5% Getting Started with R, our data science tool.
Exam 25% The exam will test to your ability to implement and understand the material covered in matters of data and trading analytics to support your trading decisions. The exam is on 26 Feb 2020.
Trading Strategy Assignment 25% Your will be working in teams over multiple weeks on trading a commodity market in real time.
Algorithmic Trading Strategy 25% You will tasked to design and implement an algo trading strategy and gained tremendous insights into this new field whilst puting to practice concepts of risk/reward and creativity in this new field.
Negotiation Game 10% Commercial negotiation is a critical element of trading. You will working in team to negotiate the best win-win deals. The Negotiation Game will be during the last class session.
Final Simulation 10% We will run a full simulation based on knowledge gained in the class.
Traders from the industry will join me in the assessment. A great opportunity for those of you interested in working in the field. The final simulation will be during the last class session.
Class Participation 0% Class participation is expressed in the other grades and taken for granted in your commitment to success.
Theexamis open-book so as to reflect real work life. Communication with others is prohibited and subject to the Academic Integrity and Honesty UoA policies. Where an exam is missed under circumstances approved under Uni- versity rules and policies, I reserve the right to ask for a statutory declaration and assign 100% of the remaining course marks including the missed exam on assignments on an individual basis since a large proportion of the grades are on group assignments.
Yourgradeswill be weighted as shown above. For each you will be provided with the assessment methodology and most importantly with the support you need to succeed. Your final grade will be based on your absolute achievement and relative performance in the class.
You will be given a clear indication of how you will be measured and graded prior to any assignment or project. The exam is new and all material tested will be covered in my class notes which contain many examples. This is consistent with the Access to Evaluative Material Procedure of the Assessment Policy, found at the University ofAlberta Policies and Procedures Online.
Academic Integrity and Honesty
The University of Alberta is committed to the highest standards of academic integrity and honesty. Students are expected to be familiar with these stan- dards regarding academic honesty and to uphold the policies of the University in this respect. Students are particularly urged to familiarize themselves with the provisions of theCode of Student Behaviourand avoid any behavior which
could potentially result in suspicions of cheating, plagiarism, misrepresentation of facts and/or participation in an offence. Academic dishonesty is a serious offence and can result in suspension or expulsion from the University. Cases of academic dishonesty and disruptive behavior will not be tolerated in class.
Examples of disruptive behavior in class are:
• Digital devices usage for personal reasons.
• Talking while the professor is lecturing,
• Exchanging information (with other students) not related to the lecture in course,
• Reading from books, newspapers, magazines, websites or any material not related to the lecture in course. Dosing and sleeping is on your account.
Audio or Video Recording, digital or otherwise, of lectures, labs, seminars or any other teaching environment by students is allowed only with the prior written consent of the instructor or as a part of an approved accommodation plan.
All content, digital or otherwise, created and/or used within the context of the course is to be used solely for personal study, and is not to be used or distributed for any other purpose without prior written consent from the content author(s) including copying and/or distribution at your current and/or future workplace(s).
Policy about course outlines can be found inCourse Requirements, Evalua- tion Procedures and Gradingof the University Calendar.
