Energy
In energy trading, traders have traditionally used analytics and business rules applied to market price data, weather data, and other real-time and historical data to enhance trading decisions. In spread trading, e.g. spark spread trading, traders have traditionally exploited cross commodity price correlation for trading strategies. The resurgence of energy and power trading, the increase in trading volume, and the rapid rise and volatility of oil prices have caused the correlations among energy assets to grow even more numerous and complex than ever. As a result of the integration of energy trading with the trading of other asset classes (caused by market evolution with trends such as the rapid growth of hedge funds), energy trading can now benefit from data derived from other asset classes such as equity markets. The Aleri Streaming Platform can enable companies to exploit their creativity to make more profitable trades while managing risk.
For example, the Aleri Streaming Platform would enable continuous analytics and monitoring of real-time data feeds to alert a trader instantly or to automate a trade when certain conditions were met, such as:
- 10 minute moving average of a basket of energy equities moved into a particular price range
- 5 minute moving average price of oil moved into a particular price range
- 10 minute moving average price of the Euro/US Dollar FX pair moved into a particular price range
- a short term price forecasted using a price model for power or gas moved into a particular price range based on real-time changes of feed data
- all of the above happening simultaneously
The Aleri Streaming Platform is well suited for managing real-time data feeds and applying continuous analytics and monitoring to take automated action. The number of criteria can be far more numerous and more complex than in the example above. These simple examples could be extended so that the price forecast model could take, as inputs, real-time market load, temperature forecasts, and other market data numbers to continuously recalculate a forecast. These other factors could provide additional derivative analytics on the average prices with complex rules comparing the results. Additionally, the ranges and other thresholds could help users make real-time changes to the business rules being applied. More fundamental calculation rules could be changed without stopping the Aleri Streaming Platform. This flexibility, combined with its real-time data monitoring and analytics, enable users to rapidly react to changing marketplaces while performing complex real-time automated analysis to make better informed decisions.
There are many other areas and instances in the energy sector where considerable amounts of real-time data are produced from internal and external sources such as market price feeds, generation plants, SCADA systems, gas schedulers, and load forecasting models. Companies have historically extracted this data in batches and analyzed it to make various operational decisions. By adding the real-time capabilities of The Aleri Streaming Platform, they now have the ability to manage this data in real-time to produce flexible automated decisions, alerting, and forecasting.