In most financial markets, algorithmic trading already accounts for the majority of the trade volume. In the United States, 80% of all stock trades are machine-driven. Thanks to its obvious benefits, algorithmic trading is also finding more and more acceptance in Energy markets, especially in short-term markets. Algorithmic trading strategies are fast becoming a source of competitive advantage in the increasingly digitalized and volatile intraday power markets.
The reduced reliance on coal and the increasing growth of renewables in the grid causes the balance of demand and supply to constantly shift, resulting in more volatile energy markets. This is especially true during the last few weeks when the COVID-19 pandemic caused energy demands and prices to decline sharply. In volatile markets, the market signals occur in intervals that are too short for human traders to keep up and respond effectively to.
Algorithms are just more efficient and faster at identifying patterns even while they are just developing and can operate at speeds and frequencies that are impossible for human traders to achieve.
Human traders are prone to manual errors, emotional and psychological influence on trading decisions, no matter their level of experience and expertise. Algo trading eliminates all of these.
The difference between automated trading and algo-trading is crucial. Automated trading is just automation of the trades that humans were already executing, based on a clear set of trading rules. Algo-trading involves creating and executing trades using trading strategies that are constantly being improved by the algorithm. Most modern Electronic Trading and Risk Management (ETRM) systems just automate the execution of trades that are a result of human decision making, without necessarily having complex enough algorithms that can handle the decision-making process.
The obvious candidates for automated trading are trades that are frequently executed and repetitive with few exceptions.
These automated trades account for most of the machine-driven trading activity. The next step is to use enable algorithms to make trading decisions, historically made by human traders. The application of Artificial Intelligence and Robotic Process Automation is widely expected to change the way energy markets operate.
However, increased efficiency and speed is not without risks. Algorithmic trading could be prone to errors in training data, system failures, and bugs that can have a cascading effect on the trade outcomes. The benefits of algorithmic trading do not mean that the entire job can be done without any human supervision, algorithms can benefit from human expertise and judgement to become better, more efficient, and effective while taking away the drawbacks typical to human traders. The role of energy trading experts would be to act as safety valves to make sure that the programs do not make mistakes and improve the algorithms and adapt them to volatile market conditions. While the bulk of the trading decisions can be made by algorithms, the human touch will still be important to judge opportunities and watch edge-cases, where machines could falter.
Human judgement can hardly be replicated well or replaced.
The biggest roadblock in front of energy companies would be their legacy systems, most of which cannot be scaled to offer the capabilities required by advanced artificial intelligence and robotic process automation systems. Application of AI for trading might require a complete overhaul of existing systems and establishing new systems and processes; activities that are not just expensive but also complicated and time-intensive. However, the advantages outweigh the efforts.
The virtual forum on AI & Machine Learning: Data-driven strategies for Power & Utilities feature data science and energy market experts who will share their expertise on Artificial Intelligence strategies that help energy trading become more secure, effective and efficient. Konstantin Wiegandt, Head of Algorithmic Trading and Analysis at Statkraft, Germany will share insights into how algorithmic trading is used in Statkraft: balancing renewable portfolios, propriety trading, steering flexible production assets. Connect with us to join the discussion.
- January 26, 2021