Winter 2021
• This constitutes a contract you are deemed to have accepted upon review.
As we proceed through the course, I may add or drop topics, or alter the order in which we cover some of the topics based on class learning.
• The class requires a personal computer. We will be working in the R lan- guage and become proficient implementing concepts with real world data.
No prior experience is required other than your commitment to learn.
• Strictly no Powerpoint / Excel in this class.
Overview and Objectives
This course is designed and taught by market professionals. The curriculum re- flects a trader development program in industry with a strong trading analytics base to grow onto as a trader in today’s marketplace.
• Develop a strong foundation using data science tools and workflow used in leading organizations.
• Learn through experimentation via trading in real markets and periodic trading simulations.
• Develop analytics knowledge in commodity markets that is transportable across markets.
• Build self awareness on where your interests and aptitudes are best de- ployed in a career where commercial optimization and trading is critical to delivery of the business model.
• 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.
Who should be interested in this class?
• You are committed to building a skill set that will differentiate you in the workplace and is consistent with how leading organizations operate.
• You are interested in learning about trading with a solid background in trading analytics.
• 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 judgement 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.
Learning Approach
• Excellence in your career depends on one’s ability to:
– Build a foundation of knowledge on concepts / theory.
– Create linkages between concepts to solve real-life problems.
– Implement them at Enterprise level.
• Class notes include theory, current market applications and clear and repro- ducible examples.
• Class presence is important in your learning process:
– Your participation is expected.
– Class time uses an active learning approach.
– Questions and answers are essential to effective learning.
• There will be live implementation in every class. I am committed to making errors in front of you - voluntary or not. That is one learns.
Prerequisites
• This course will draw from knowledge gained in basic statistics and finance and your personal drive and desire to learn.
• FIN 301, FIN 412 and open to4thyear students only.
• The following classes would be an asset in your success.
– FIN488 Data Science in Commodities & Finance.
– FIN413.
Supporting Material
Required Course Materialfree online book
Chester Ismay and Patrick C. Kennedy.
Getting used to R, RStudio, and R Markdown
• There areno required textbooksto purchase.
• Class notes are available on theRstudio Connect UoA Server.
1. Log on with CCID credentials.
2. Send me an email and will authorize you to see the material.
Course Modules
Classes Modules
2021-01-13 Introduction, Basics of Commodity Markets I and R Tour 2021-01-20 Data Wrangling I
2021-01-27 Data Wrangling II
2021-02-03 Basics of Commodity Markets II 2021-02-10 Instruments, Execution and Pricing 2021-02-24 Supply Demand Models
2021-03-03 Trading Process 2021-03-10 Analytic Toolkit I 2021-03-17 Analytic Toolkit II 2021-03-24 Exam
2021-03-31 Quantitative Trading I 2021-04-07 Quantitative Trading II
2021-04-14 Trading Psychology and Simulation
Availability
I will be available if you have questions outside course times. To book time:
• Go to my websiter4tropic.com, selectContactandBook an appointment.
• Please respect the 30-min booking time by being prepared.
• Send me aself contained Rmd documentso I can reproduce your issues.
• I will update the meeting with Google Meet links.
Grading
• Grades are assessed on your absolute and relative performance.
• Due dates of assignments, projects and/or exams worth 10% or less are contained in the class notesGradingchapter.
• Strict policy of zero scores for late submissions unless pre-clearance is sought and accepted, irrespective of any previous draft document you have sent me. It is your accountability to deliver on time.
• Open-book exam(s) reflecting a real life environment.
– 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 dec- laration and assign 100% of the remaining course marks including the missed exam on assignments on an individual basis since a large propor- tion of the grades are on group assignments.
– I will not be providing an exam bank of questions.
• Gradeswill be weighted as shown below. For each you will be provided with the assessment methodology and most importantly with the support you need to succeed. You will be given a clear indication of how you will be measured and graded prior to any assignment or project.
Category Measure Description
Assigmement 5.0% Getting Started with R, our data science tool.
Exam 25.0% 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. See under Course Modules for exam date.
Trading Strategy Assignment 25.0% Your will be working in teams over multiple weeks on trading a commodity market in real time. The assignment will end the week following Reading Week.
Algorithmic Trading Strategy 25.0% You will be 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. The project is due at the beginning of the last class of the term
Negotiation Game 10.0% Commercial negotiation is a critical element of trading. You will be working in teams to negotiate the best win-win deals. The Negotiation Game will be during the last class session and requires a minimum of 12 participants. In the event we have less than 12 participants, Professor reserves the right to award full score to all and/or replace with an alternative with similar objectives.
Final Simulation 10.0% 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 0.0% Class participation is expressed in the other grades and taken for granted in your commitment to success. With online delivery, it is a strong expectation.
Online Delivery
Digital Recording And Copyrights
• The recording of classes is strictly not authorized for any purposes.
• 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 accommoda- tion plan. Student or instructor 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).
Zoom Registration
• You are required to pre-register using your ualberta.ca account.
• I will not admit registration using a personal email account.
Active Participation
• You webcam must be on at all times.
• It is your sole responsibility to be on time:
– I usually cover review and logistics items on assignments.
– I do not auto-admit participants. Whilst I make every effort to admit late participants in the waiting room, my attention is focused on students attending, not those who are late.
• I routinely do breakout group exercises and you being logged in and not present in class is not acceptable and your sole accountability.
Academic Integrity
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 behaviour 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 behaviour will not be tolerated in class.
Policy about course outlines can be found inCourse Requirements, Evalua- tion Procedures and Gradingof the University Calendar.
