Artificial Intelligence in Energy Trading (CLASSROOM) - AIET

Artificial Intelligence in Energy Trading (CLASSROOM) - AIET

Course Summary

Artificial intelligence should strengthen human judgment, risk discipline, and market understanding - not replace accountability. Participants learn how to combine energy market structure, fundamentals, systematic signals, alternative data, and AI-assisted workflows into an auditable trading process.

This course develops an applied program on AI-enabled energy trading. It connects intelligent tools to the existing curriculum themes of energy market regimes, positive expectancy models, risk management, execution, trader psychology, and simulation-based learning.

By the end of the course, you will be able to:

• Distinguish automation, machine learning, generative AI, and agentic trading workflows.
• Identify where AI can improve signal discovery, scenario analysis, execution, and post-trade review.
• Test whether an AI-generated idea has economic value after costs, slippage, crowding, and regime changes.
• Build human-in-the-loop controls for model risk, data quality, explainability, and limits.
• Use AI to enhance - not bypass risk governance, trader judgment, and accountability.
• Execute and critique AI-assisted trading decisions in realistic case studies and simulations.

Course Content

Day 1:

Session 1: What intelligent trading means

• Automation vs. machine learning vs. generative AI vs. agentic workflows; where AI fits in the energy trader’s decision loop; human strengths versus machine strengths.

Session 2: Case Study: AI versus the false breakout

• Participants compare a visually compelling breakout with an AI-generated trade-confidence assessment, liquidity context, volatility percentile, and prior analogues. The group identifies what the model missed.

Session 3: Diagnostic Trading Simulation: Human-in-the-loop energy desk

• Teams receive evolving price, inventory, weather, fundamental, and flow information across crude oil, refined products, and natural gas. An AI assistant proposes scenarios and trades; participants accept, modify, or reject recommendations under position, liquidity, drawdown, and concentration limits.

Session 4: Data quality, provenance, and feature design

• Futures rollovers and forward curves; contract specifications, session boundaries, survivorship bias, look-ahead bias, alternative data, news, weather, logistics, flows, and market microstructure.

Session 5: AI-assisted technical and fundamental analysis

• Prompting for chart review, trend/volatility studies, Donchian and narrow-range signals, cross-market relationships, fundamental scenario extraction, and source verification. Participants build a reusable prompt library for market and chart review, fundamental and news scenario extraction, trade thesis/counter-thesis, pre-trade risk checks, execution/liquidity review, post-trade review, and source/provenance verification.

Session 6: Market regimes and model fit

• Doldrums, steady trend, choppy, parabolic, and crash regimes; how volatility, liquidity, narrative, and fundamentals alter model behavior; detecting regime shifts with quantitative and qualitative evidence.

Debrief

• Where did the AI add information, speed, or discipline?
• Where did it create false precision, anchoring, or overconfidence?
• Which decisions required human accountability?

Day 2:

Session 1: Positive expectancy models and strategy evaluation / Expectancy = (Win Rate x Average Win) - (Loss Rate x Average Loss). 

• Win rate, payoff ratio, expectancy, profit factor, drawdown, time-under-water, turnover, slippage, and capacity. Translating AI ideas into testable hypotheses. AI finds patterns, not edge: test each economic hypothesis out of sample after costs, crowding, and regime change.

Session 2: AI for systematic strategy design / Signal definitions, feature selection, walk-forward testing, out-of-sample validation, Monte Carlo analysis, regime segmentation, and avoiding overfitting. 

• Use walk-forward tests to separate durable hypotheses from curve-fitted noise.

Session 3: Execution intelligence and market microstructure

• Liquidity seeking, execution algorithms, spoofing and icebergs, sweepers, market impact, order placement, slippage modeling, and AI-assisted execution monitoring.
  

Session 4: Case Study 1: AI-generated trade with hidden data leakage

• Participants audit a seemingly strong model whose features contain contract-roll leakage, revised data, or future information. The exercise covers reproducibility and challenge procedures.

Session 5: Governance, controls, and operating model

• Model inventory, approval gates, human override, audit trails, data permissions, cybersecurity, vendor risk, monitoring, incident response, and post-trade review.

Session 6: Trading Simulation: AI breakout and portfolio challenge

• Multi-asset energy-futures simulation across crude oil, refined products, natural gas, and power, using trend, narrow-range, volatility, and fundamental signals. Teams manage entries, scaling, stops, OCO logic, correlation, and capital limits while an AI agent supplies changing research. Teams use and refine the reusable prompt library, recording prompt inputs, sources, model outputs, overrides, and exceptions

Session 7: Case Study 2: Energy shock and model failure

• A weather (Natural Gas Desk) or geopolitical shock (Oil & Refined Products Desks) changes supply expectations, correlations, volatility, and liquidity. Teams determine whether to follow, fade, reduce, or suspend the model.

Session 8: Implementation roadmap

• Prioritize use cases by value, feasibility, control burden, and time to learning. Define 30-, 60-, and 90-day pilots.

Debrief

• Which AI-assisted use case is ready for a controlled pilot?
• What validation evidence, risk limits, and human overrides are required before deployment?
• Which decision, execution, and post-trade-review practices should the team carry into its 30/60/90-day plan?