The energy market has undergone significant changes in recent years. With the liberalisation of the market and the increasing influence of small and decentralised producers, it has become increasingly complex and volatile. In Germany, where renewable energies accounted for almost half of the electricity produced in 2022 (BNetzA), this process of a decentralised, more complex market structure has long since become reality. The classical method of trading, where human agents executed every transaction, is no longer sufficient to keep up with the demands of the market.
This is where algorithmic trading comes into play.
Algorithmic trading, or Algo trading, is a new approach which is primarily widespread in the financial markets. It uses algorithms and mathematical models to make decisions about buying and selling. The algorithm takes into account all relevant data, such as weather and power consumption data, to make forecasts about the energy market. The core of Algo trading is the trading algorithm, which determines when to buy and sell, and can be fully automated for even faster and more efficient decision-making.
Algo Trading or Automated Trading
A common misconception is that Algo trading is equated with a complete handover of human control. The opposite is true. One step before the trade is getting executed, the human agent generally still has to determine the relevant parameters which define the trading algorithm. Algo trading has nothing to do with artificial intelligence. It is rather a complex model of if-rules.
The complete transfer of human control would only be possible within an automated trading system. Here, every process from internal and external data collection to trade execution is completely automated. As a rule, the trading algorithm has artificial intelligence and is thus constantly evolving. However, AI optimisers such as suena show that it is possible to develop such an AI autopilot in a way that human intervention is possible at any time via a user-friendly interface.
The advantages are manifold
One of the main advantages of Algo trading is its cost-effectiveness. With reduced human resources costs, even small plant operators and energy traders can benefit from optimised trading revenues. Algo trading is also well-suited for exploiting price fluctuations on the spot markets, particularly in combination with energy storage enabling storage operators to significantly increase their revenues. The benefits of Algo trading go beyond just optimised revenues on the intraday market. With AI-optimisation software, individual goals and tasks, such as balancing group management, can be customised. The algorithm continually decides on the best course of action to meet commitments while avoiding expensive balancing costs, all while optimising trading revenues and other possible tasks.
In addition to plant operators, who are on the producer side, Algo Trading is an equally attractive option for electricity consumers such as municipalities, public utility companies or commercial electricity consumers such as e-mobility providers. Especially in times of high electricity prices, Algo Trading offers immense potential for cost optimisation in combination with flexibility-assets such as battery storage.
The use of Algo trading is on the rise, and it is estimated that most transactions on EPEX Spot are no longer executed by human actors. This entails that a good data basis, both on the generation and the consumption side, is becoming increasingly important.
Automation is the future
With the advancements in machine learning technology, self-learning algorithms are continuously improving the accuracy of forecasts and increasing trading profits. For those looking to optimise their energy revenues or cost, Algo trading, or even better fully automated trading, is the way to go. At suena, we have developed an intelligent autopilot, suenAS, to help our clients optimise their trading revenues and costs through automated trading on multiple markets. This optimising software not only handles trading but can also manage all possible applications according to the specific needs of the operator.