Demand response as a trading asset: turning load into flexibility
When it comes to renewable energy, flexibility is not a quality usually associated with this type of energy source. This all changes with flexible solutions
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Alternative data in power trading: gaining an edge beyond traditional datasets
Power trading has historically relied on data, but the scope of what constitutes 'useful data’ is broadening. While traditional datasets such as prices,
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Scenario analysis for power trading: stress-testing portfolios properly
Electricity markets are shaped by constant uncertainty: weather swings, renewable variability, fuel disruptions and shifting system conditions. When
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Data pipelines for power trading: building the infrastructure behind algorithmic strategies
Algorithmic power trading relies on more than just models and strategies. Its foundation is a less visible yet equally vital element: data infrastructure.
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Geopolitics in power markets explained: why global events drive electricity prices
Electricity markets have always been shaped by physical constraints and local system conditions. For most of their history, that was enough to explain price
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Human vs algorithm decision-making in power trading: finding the right balance
The growth of algorithmic trading has drastically transformed the way power markets are traded. Automated systems are now key in generating signals,
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Model risk in power trading: why algorithms fail during stress events
Algorithmic trading now plays a key role in modern power markets, enabling faster execution, improved data analysis, and more structured decision-making.
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Machine learning for power price forecasting: what works and what doesn’t
Machine learning (ML) has become a widely discussed tool in power trading, often seen as a way to achieve more accurate price forecasts and automate
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Portfolio positioning using forward curves: from signals to strategy
The core concept of portfolio positioning using forward curves is identifying when a market features prices higher in the current market or in the future