Oil Forward Contracts in Python

Learn to model crude oil forward contracts, and build commodity trading systems using Python

What you’ll learn
Build a Python model for oil forward contracts with realistic company data
Calculate profit & loss (P&L) for forward contracts at different spot prices and understand who wins/loses
Understand professional trading terminology: gross notional value, notional limits, and Value at Risk (VaR)
Create a portfolio system that tracks multiple forward contracts and enforces position limits
Model realistic oil industry transactions: producers selling to refiners (upstream to downstream flow)

Requirements
Basic Python knowledge

Description
Ever wondered how oil traders at Goldman Sachs, Shell, BP, and Vitol manage billions of dollars in crude oil contracts?  In this beginner-friendly course, you’ll learn the fundamentals of oil forward contracts using real Python code. No finance degree required, just basic Python knowledge and curiosity about how global oil markets actually work.What You’ll Build:You’ll create a complete forward contract modeling system from scratch, using the same professional architecture employed by trading desks at Goldman Sachs Commodities, J.P. Morgan, Morgan Stanley, and major oil trading houses like Vitol and Trafigura.  What You’ll Learn:How forward contracts work and why companies use them to lock in oil prices months in advanceCalculate profit & loss (P&L) to see who wins and loses when oil prices changeProfessional trading terminology used at investment banks: gross notional value, notional limits, and Value at Risk (VaR)Build Python classes that separate pure calculations (functions) from state management (classes)Track multiple contracts in a portfolio with position limits, exactly like Goldman Sachs and Shell trading desksUnderstand the oil supply chain: producers, refineries, and how crude becomes gasolineLearn how both investment banks and oil companies structure their commodity trading operationsReal Trading Desk Insights:The course includes detailed information about how actual trading desks operate at investment banks (Goldman Sachs Commodities, Morgan Stanley, J.P. Morgan) and integrated oil companies (Shell Trading, BP Trading, ExxonMobil). You’ll learn about desk structure, team roles, Bloomberg terminals, and position limits used in the real world.Why This Course Is Different:Most trading courses are either too theoretical (endless lectures) or too advanced (assumes finance background). This course finds the sweet spot: practical Python coding with real-world examples from both Wall Street and the energy sector, starting from the basics.You’ll use realistic company names, actual trading desk structures, and professional terminology – giving you a genuine taste of how commodity trading works at major investment banks and oil companies.Perfect For:Python developers exploring trading careers at banks or energy companiesFinance students wanting hands-on coding experience for trading desk rolesEnergy professionals understanding how investment banks trade commoditiesAnyone curious about how Wall Street and Big Oil trade billions dailyRequirements:Just basic Python knowledge (classes, functions, loops). We’ll explain all the finance concepts as we go.Start learning the basics of commodity trading used at Goldman Sachs and beyond!

Who this course is for
Python developers curious about commodity trading and how oil markets work in the real world
Finance students who want to learn practical Python applications in energy trading beyond just theory
Career changers exploring quantitative roles in energy trading, commodity desks, or risk management
Energy sector professionals (engineers, analysts) who want to understand how traders model and price oil contracts
Python developers with basic programming skills who are curious about breaking into commodity trading or financial modeling
Self-learners who want to understand how billions of dollars in oil are traded daily using Python and professional tools

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