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TEXAS ENERGY TRADING CURRICULUM

WHAT MAKES US UNIQUE?

Texas Energy Trading is dedicated to developing both fundamental energy expertise and hands-on skills in energy-related projects, including proficiency in Python and other software commonly used on the trading floor.

TRADING CURRICULUM

Crude Oil

  • Overview of the crude oil supply chain from upstream extraction to downstream refining

  • Key global producers and importers (OPEC, U.S., China, India) and benchmarks (WTI vs. Brent)

  • Factors impacting price (geopolitics, inventories, production cuts, demand cycles)

Refined Products (Gasoline/Diesel)

  • Crude oil refining process and refinery economics (crack spreads, margins)

  • Seasonal demand patterns (e.g., summer driving season) and regulatory impacts

  • Pricing mechanisms and logistics (RINs, blending mandates, pipeline constraints)

​Natural Gas/LNG

  • U.S. natural gas fundamentals: shale production, storage, seasonality

  • LNG export markets: major players, pricing hubs (Henry Hub vs. JKM vs. TTF)

  • Mock trading scenario: use supply and demand inputs to justify a trading position

NGLs (Butane, Ethane, Propane, etc.)

  • Difference between natural gas and NGLs; extraction via gas processing plants

  • End uses: petrochemicals, home heating, fuel blends and their price links to crude/gas

  • Major players in the U.S. midstream space and international trade flows

Power/Electricity

  • Power grid basics: generation types (thermal, renewable), ISO/RTO structure

  • Concepts like locational marginal pricing (LMP), congestion, and load forecasting

  • Deregulated vs. regulated markets and arbitrage price spreads

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PYTHON CURRICULUM

Setup

  • Introduce students to the course and expectations

  • Create a Python environment and run the "Hello World" program

Functions & Data Structures

  • Discuss the basics of functions and data structures

  • Introduce students to the different types of data structures, such as lists, arrays, dictionaries, and sets

  • Practice working with data structures in Python

  • Kaggle & Other Resources to find data

​Read & Write to CSVs, Files, Pandas, etc.

  • Explain how to read and write data to CSV files

  • Introduce students to the Pandas library and how to use it to read and write data frames

  • Visualize data

  • Practice working with CSV files and Pandas in Python

​Big Data Project Part 1: Load Dataset & Start Feature Engineering

  • Load a real-world dataset into Python

  • Start to explore the data and perform some basic feature engineering

  • Discuss the challenges of working with big data

Big Data Project Part 2: Make a Prediction & Further Extension

  • Develop a model to make predictions on the data

  • Evaluate the performance of the model

  • Discuss how to extend the project further, such as by adding new features or using a different machine learning algorithms

Project Presentations

  • Present your projects & get real-time feedback!

  • Learn how you can further progress in the field! 

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