Insights

On 29 October 2026 ACER published the first guidance for the revised REMIT reporting rules, including TRUM version 8 and new XML formats. This guide explains the three phases from October 2027 to July 2028, who reports what, and the new Algorithm ID field for orders and trades on organised market places.

Since May 2024, REMIT has a rule for algorithmic trading in wholesale energy. This guide explains which software it covers, the records it asks you to keep for five years, what the regulator can ask to see, and the Algorithm ID that every reported transaction will carry from 29 October 2027.

Master dark spread calculations for coal-fired power plants: efficiency factors, carbon emissions at 0.96 tCO2/MWh, fuel switching economics, and why European coal is disappearing. Includes Python implementation, worked examples with 2026 prices, and comparison to spark spreads.

How the global gas market sets UK prices: Henry Hub as a floor, LNG shipping costs and netback, Europe against Asia for cargoes, the UK's small storage, NBP-TTF basis risk and the storage break-even, with a Python calculator.

Mark-to-market P&L from first principles: the formula, why the sign matters, realized vs unrealized P&L, a hedged six-trade portfolio, a Python calculator, live vs settlement prices and common mistakes.

Master the physical foundations of energy trading: instant grid balance, merit order dispatch, and how infrastructure constraints create trading opportunities. The essential foundation for understanding spark spreads, gas shipping, and storage arbitrage.

Master position aggregation from first principles: signed quantities, the three-key rule, physical vs financial splits, delta exposure, and hedge ratios. Includes Python implementation of a position engine - the foundation every trading system uses.

Master spark spread calculations: efficiency factors, carbon emissions, hourly variation, and why plants run at losses. Includes Python implementation and real-world trading strategies for CCGT dispatch decisions.

Comprehensive guide to physical vs financial energy trading in UK and European markets. Covers spatial arbitrage (NBP-TTF pipeline trading), temporal arbitrage (storage economics and forward curves), quality arbitrage (spark spreads), and the software architecture challenges of building integrated ETRM systems. Essential for engineers, quants, and aspiring energy traders.

Learning an ETRM system is like learning Excel to be a CFO. Discover why the real value lies in understanding the business and the market, not the specific trading tool.

Learn energy trading fundamentals from a software engineer's perspective. This comprehensive guide explains spot vs forward markets, hedging mechanics, physical vs financial settlement, exchange vs OTC trading, and how Python-powered analytics pipelines create competitive advantage in UK energy markets.

The business analyst works out what a trading firm's ETRM system must do when a product, a rule or a market changes. This roadmap covers the job, why there is demand now, what it pays and what the interview tests. Then four stages, about 80 hours, and five things an employer can open.

What a quantitative developer on a short-term power desk does, why the role is in demand, what it pays and what the interview tests. Then a roadmap in four stages, a plan for the first thirty days, and the five things to put in front of a hiring manager.

What a desk-side power market analyst does, why the role is in demand in 2026, what it pays and what the interview tests. Then a four-stage roadmap, the first thirty days, and the five things to put in front of a hiring manager.

What a head of trading technology does, why energy desks are hiring for the role, what it pays in London, and what the interview tests. Then a four-stage roadmap, the first thirty days, and the five things to put in front of a hiring manager.

To thrive in today's market, modern software engineers need a diverse skill set beyond just coding. This guide details the essential technical and business competencies, from mastering a back-end language like Python to understanding cloud infrastructure and CI/CD pipelines. Future-proof your career by focusing on these fundamental skills that separate good developers from great ones.