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

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!

Project Showcase

That's a wrap on our Fall 2025 Project Showcase! To close out the fall, our cohort presented the culmination of a semester’s worth of modeling, research, and applied curriculum. We were thrilled to host an evening of strong analysis, intricate technical work, and insightful discussion!

Teams delivered projects spanning power market forecasting, oil and gas fundamentals, and data-driven commodity analytics, including tools modeling hourly grid dynamics, predicting hydrocarbon production trends, and leveraging weather and fundamentals to forecast price movements across key U.S. markets. Collectively, these models provided a holistic view of the industry and revealed meaningful insights into current trends.

Congratulations to our presenters for their hard work and dedication this semester, and a huge thank you to our industry partners for spending the evening with us and for your valuable feedback and continued support. We are eager to continue tackling real-time market trends and developing models to drive trade strategy as we head into the coming semester. Looking forward to an exciting spring!