Energy Trading: Price Forward Curve

Case Study


As the share of renewables grows in the energy mix, the unpredictability of electrical supply and demand creates the need to compute Price Forward Curves (PFC) more frequently.

The PFC is used to hedge trades and is often a source of alpha for trading firms.

In a nutshell, what Quasar delivered

  • Deployment of complete market and weather data capture, from start to finish, in less than a week
  • 100X speed up on Price Forward Curve computation
  • Storage footprint divided by 10
  • Future proof solution thanks to built-in scalability


A Price Forward Curve is heavily influenced by supply and demand, especially for commodities like electricity. That’s because transporting and storing energy is extremely expensive.

Seasonality is a typical factor of price variation. With the introduction of renewables in the energy grid, the price variation has a much greater unpredictability.

This change requires trading firms to incorporate weather data and recompute the PFC regularly, possibly every minute. That puts massive stress on the system that was not designed for that data pressure.

Energy trading firms typically built a system architecture relying on a relational database that was sufficient to handle previous volumes. The increased workload on the database results in unrealistic budgets or infrastructure size.

The solution

Quasar is connected directly to the market data provider and captures the data without a third-party tool. Capture is done with virtually no latency.

Quasar uses its unique suite of compression algorithms to compress the data faster than it arrives and use its micro-indexes to reduce future lookup and feature extraction time. This results in significantly reduced disk usage without impacting the queryability of the data.

Each instrument is stored as a timeseries a two dedicated tables (one for orders, one for trades). Weather data is organized by source.

Tables can be tagged at will, enabling a flexing querying mechanism based on those tags. Tags can be queried recursively. For example, obtaining all the instruments of an index or all the weather data of a specific region.

Queries use a SQL interface, dedicated API, or one of the numerous connectors available. For example, traders can load data directly into Tableau using the Quasar ODBC driver. Quants can load timeseries directly into Python Pandas using the Quasar Pandas API.

Quasar has built-in capabilities to accelerate the computation of Price Forward Curve. It can transform the data in real-time to significantly simplify the effort needed to get a result.


  • Firms can recompute the Price Forward Curve as often as needed, increasing alpha
  • Firms can keep up with the market and have linear scalability capabilities thanks to the clustering capabilities of Quasar, increasing their agility and competitiveness
  • Compatibility with all standard analytical tool suites for maximum flexibility
  • Faster time to a solution: Quasar abstracts away all data engineering complexity
  • Outstanding TCO thanks to the combination of data compression and built-in aggregation functions, saving on disk storage, CPU power, and development time

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