Electricity trading in Europe has grown significantly more complex, driven by increased volatility from renewable energy sources and external geopolitical factors. Market dynamics now shift minute by minute, and traditional methods relying on human intuition and historical experience no longer suffice. The client’s goal was to create a tool that could provide accurate forecasts on trading volumes, price directions, and supply/demand shifts—while alerting traders to anomalies or opportunities. The system had to support real-time decision-making and be robust enough for use in intraday and day-ahead markets. Building this required deep domain knowledge in energy markets, scalable data infrastructure, and advanced AI capabilities.
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Our Data Engineering team built the core infrastructure to support real-time electricity trading insights. Using Python, SQL, and Apache Airflow, we developed a scalable pipeline for automated ingestion and transformation of time-sensitive market data. This information was stored in a time-series database (TimescaleDB) and deployed on AWS (S3, EC2) for performance and flexibility. The cleaned datasets were then used to fuel predictive models and visualized through Power BI, enabling accurate forecasts and timely trading decisions.
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The Proof of Concept successfully demonstrated the ability to forecast trading opportunities, reduce reliance on manual processes, and equip traders with real-time decision support. It validated the use of machine learning to handle highly volatile, non-stationary market data, and laid the foundation for algorithmic trading automation. The modular, cloud-native architecture also ensures the system can scale as needed—both across additional markets and more complex trading strategies.
