Front Office Quant - Commodities (Gas & Power)

What the job is, how a trading desk thinks, and your first runnable Python.

1. What a Front-Office Quant Actually Does
2. The Trading Desk Ecosystem
3. A Day in the Life of a Gas & Power Desk
4. Prices, Returns, and the Language of Markets
5. Setting Up Your Python Lab
6. Python Building Blocks for Quants
7. Your First Market Calculation in Python
8. How This Course Builds Four Real Apps

How gas and power actually trade - hubs, seasonality, spikes, and real data.

1. What Makes a Commodity (and Why Energy Is Special)
2. Natural Gas: From Wellhead to Burner Tip
3. Electricity: The Commodity That Can't Be Stored
4. Hubs, Zones, and Delivery Points
5. Reading the Forward Market: Months, Quarters, Seasons
6. Seasonality and Why Winter Costs More
7. Spikes, Non-Storability, and Fat Tails
8. Supply, Demand, and the Merit Order
9. The Players: Who Trades Gas & Power and Why
10. Loading Real Market Data with Pandas

Every piece of maths you need, built from zero: stats, probability, simulation.

1. The Mathematical Roadmap: How the Pieces Fit
2. Numbers, Units, and Sanity Checks
3. Averages, Spread, and the Shape of Data
4. Probability Without Tears
5. The Normal Distribution and Why It's Everywhere
6. Volatility: Measuring How Much Prices Move
7. Correlation: How Two Prices Move Together
8. Optimization Intuition: Finding the Best Choice
9. A Gentle Introduction to Calculus (Rates of Change)
10. Randomness Over Time: Random Walks & Brownian Motion
11. Mean Reversion: Why Energy Prices Come Back
12. Monte Carlo: Pricing by Simulation

From raw market quotes to a usable, tested forward curve - the reusable Component 0.

1. What a Forward Curve Is and Why Everything Needs One
2. Market Conventions: Day-Counts, Calendars, and Rolls
3. From Traded Quotes to a Price for Every Day
4. Interpolation: Filling the Gaps Sensibly
5. Bootstrapping a Consistent Curve
6. Adding Seasonal Shape to the Curve
7. Calendars, Peak/Off-Peak, and Delivery Periods
8. Discounting: The Time Value of Money
9. Component 0 Build: The Curve Builder Library
10. Testing and Trusting Your Curve

Forwards, swaps, options, Black-76, implied vol, calibration, and Monte Carlo.

1. Forwards and Futures: Locking a Price
2. Swaps: Turning Floating into Fixed
3. What an Option Is (Insurance for Prices)
4. Payoffs and Break-evens You Can Draw
5. The Idea Behind Black-Scholes (No Fear)
6. Black-76 for Commodity Options
7. Implied Volatility: The Market's Fear Gauge
8. Calibrating Models to Market Prices
9. Asian Options: Pricing the Average
10. Pricing Options by Monte Carlo
11. Project 1 Kickoff: A Swing Contract's Building Blocks

The instruments that define energy trading: swing, spread options, and storage.

1. Swing Contracts: The Right to Vary Volume
2. Valuing Swing: Intrinsic vs Extrinsic
3. Dynamic Programming for Swing (Plain-English)
4. Project 1 Build: Gas Swing Pricing Engine
5. The Spark Spread: Gas Into Power
6. Spread Options: Optionality on a Difference
7. Pricing Spread Options (Margrabe & Monte Carlo)
8. Project 2 Build: Power Spread Option Risk
9. Gas Storage as an Option on Time
10. Intrinsic Storage Value via Linear Programming
11. Extrinsic Storage Value (Rolling Intrinsic & Simulation)
12. Project 3 Build: Storage Valuation Model

Greeks, hedging, Value at Risk, stress testing, and explaining the day's P&L.

1. Position, Exposure, and P&L Explained
2. The Greeks: Delta, Gamma, Vega, Theta
3. Computing Greeks by Bumping
4. Hedging: Turning Risk into a Plan
5. Value at Risk (VaR) the Honest Way
6. Stress Testing and Scenario Analysis
7. P&L Attribution: Explaining the Day
8. Aggregating Risk Across a Book

How real pricing libraries are structured so they last, scale, and stay testable.

1. Anatomy of a Pricing Library
2. Designing Clean, Reusable Pricers
3. Market Data In, Risk Out: The Pipeline
4. Caching, Reuse, and Not Repricing the World
5. Object Models for Trades and Curves
6. Project 4 Foundations: A Book and a Pricer

Production Python: numerics, vectorised speed, data quality, tests, reproducibility.

1. Numerical Methods Every Quant Uses
2. Optimization with SciPy (and a Taste of LP)
3. Vectorisation: Making NumPy Fly
4. Profiling: Finding the Slow Line
5. Benchmarking: Loops vs Vectorised vs Compiled
6. Monte Carlo at Scale
7. Numerical Pitfalls and How to Avoid Them
8. Data Quality: Bad Data Is the Real Enemy
9. Writing Tests You Can Trust
10. Reproducibility & Structuring a Real Project
11. Reading/Writing Market Data & Failing Loudly
12. From Notebook to Production Module

Turning a trader's intent into models, fast quotes, and clear explanations.

1. What Traders Actually Want From a Quant
2. Pricing Under Time Pressure
3. Explaining a Model to a Trader
4. Trade Idea to Priceable Structure (and When to Say No)

Validation, benchmarking, documentation, governance - making models defensible.

1. What Model Risk Is and Why Banks Care
2. Validating, Benchmarking & Reconciling a Model
3. Documentation That Survives Audit
4. Limits, Controls, Governance & a Tour of Regulation

Integrate, ship, and defend the four working projects as one quant platform.

1. Capstone Setup + Integrating Projects 1-3
2. Project 4 Build: Trader Risk Dashboard (Streamlit)
3. Defending Your Work: A Desk & Model-Risk Review