We’ve been hearing for years about the rise of algorithmic trading in energy markets, but what does that actually mean in practice? Not all algorithms are created equal. As algorithmic technology has matured, it has diversified into various types, each with a unique level of sophistication and a different relationship to human decision-making. 

According to a recent study by the Netherlands Authority for Consumers and Markets (ACM), algorithms have become a core component of energy trading across both gas and power markets. This shift is especially pronounced in power markets, where high volatility and intense competition drive the need for advanced tools. The ACM study identifies three primary types of algorithms shaping energy trading today: execution algorithms, signal generators, and trading algorithms. Each of these plays a distinct role, tailored to the specific needs and characteristics of the market in which they’re applied.

But despite the benefits automation algorithms bring, trader interaction remains key. Traders aren’t just monitoring algorithms - they’re guiding them, setting parameters, and intervening when conditions call for human oversight. Figure 1 of this study provides a schematic overview of the three types to clarify the varying degrees of automation and trader involvement associated with each algorithms:

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2. Signal Generators to cover for market volatility

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Signal generators act as the “eyes and ears” for traders, continuously scanning market data to identify potential opportunities. These algorithms analyze inputs such as historical data, weather forecasts, market prices, and grid conditions to generate signals that guide trading decisions. In volatile markets like power trading - where renewable energy sources introduce fluctuations - signal generators help traders manage uncertainty by flagging actionable insights.

Interaction between trader and algorithm is high with signal generators. Traders rely on these algorithms to sift through vast datasets but remain responsible for interpreting the signals and deciding when and how to act on them. For instance, if a signal generator detects a spike in power demand based on an approaching weather front, it might prompt the trader to buy electricity in advance. While the algorithm provides the raw intelligence, the trader applies judgment and experience to formulate the strategy.

Signal generators are particularly valuable in the power market’s spot trading, where fast decision-making is essential due to market volatility. Although these algorithms function autonomously in analyzing data, they operate as advisory tools rather than executors. This gives traders the flexibility to adjust based on real-time conditions, making signal generators indispensable in high-variability environments.

2. Execution Algorithms remain the backbone of gas trading

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Execution algorithms automate the process of placing orders based on pre-set criteria like price and volume. They are designed for stable, predictable environments and are widely used in the gas market, where trades are typically fewer, larger, and less volatile. Here, the interaction between trader and algorithm is focused on defining execution parameters to minimize market impact, with the trader deciding factors like the timing and size of orders.

In gas markets, which usually see larger transaction volumes over longer timeframes, execution algorithms allow traders to optimize order placement. For example, to avoid moving the market price against their interests, traders can use execution algorithms to break large orders into smaller, timed trades. 

The role of the trader here is key: they analyze the broader market environment and set the parameters to ensure the algorithm operates smoothly within its framework. In this sense, execution algorithms act as the backbone of gas trading, where the market’s relatively stable nature enables traders to use algorithms for efficient order handling while still exercising strategic control.

3. Trading Algorithms for managing risk in the power market

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Trading algorithms take automation further by not only analyzing data and generating signals but also independently deciding on trade execution. These algorithms are vital in the power market, where high competition and volatility demand rapid responses to shifting conditions. The interaction here is less frequent but still significant: traders configure the algorithm’s parameters, monitor its performance, and intervene when market anomalies require a human intervention.

In high-frequency trading environments, trading algorithms can operate independently, adjusting to real-time data for optimal results. The ACM study points out that trading algorithms often use machine learning to adapt to new market behaviors, identifying patterns from historical data and modifying strategies accordingly. For example, in response to a sudden drop in supply from a wind farm, a trading algorithm can adjust its parameters to manage this shift autonomously, providing the agility required in the fast-paced power market.

However, traders remain essential for overseeing these algorithms, particularly in times of extreme volatility. While trading algorithms can operate within a broad range of conditions, unexpected events - like a sudden regulatory change or a geopolitical event - may require traders to pause or override the algorithm to protect against unforeseen risks. This balance between autonomy and oversight allows traders to leverage the speed and precision of trading algorithms while maintaining strategic control.

Comparing gas and power market algorithm use

But how are these algorithms actually used in practice across gas and power markets? The market study also features a survey of industry players, which provides a real-world snapshot of algorithm adoption:

The results highlight a clear divergence: In gas trading, the relatively stable market environment favors execution algorithms. These algorithms allow traders to manage large orders with less concern for rapid price changes. Conversely, in power trading, where volatility is a significant factor, there is a greater reliance on trading algorithms and signal generators to cope with frequent fluctuations in supply and demand.

The trend towards more sophisticated algorithmic trading is largely driven by the energy transition. As renewable energy sources become more prevalent, the need for data-driven trading strategies that can handle unpredictability increases. This shift is particularly evident in power markets, where the integration of wind and solar power has made real-time adaptability crucial.

e*star’s approach to algorithmic trading

At e*star, we have long recognized the growing importance of algorithms in energy trading. This is why our e*star Trading Pilot blends the strengths of algorithmic trading with the insights and adaptability of manual trading. It functions like an aviation autopilot system: while the autopilot handles routine tasks, the pilot remains in control for crucial maneuvers. Similarly, the Trading Pilot automates repetitive and time-sensitive tasks, enabling traders to retain authority over strategic decisions.

This solution caters to all trading styles, from manual to semi-automated and fully automated approaches. Whether it’s executing large gas trades using stable execution algorithms or managing the fast-paced power markets with trading algorithms and machine learning, our tools are designed to keep traders competitive. We understand that each market requires a tailored approach, and our platform supports a variety of algorithmic strategies to match the specific needs of gas and power traders.

The Trading Pilot maximizes the benefits of algorithmic trading by executing trades at millisecond speeds, processing large datasets, and operating based on predefined rules. This speed and precision are particularly valuable in the energy sector, where market conditions can change rapidly due to factors like weather events or geopolitical shifts. For example, the Trading Pilot can automatically engage in arbitrage by detecting price discrepancies across multiple energy exchanges and executing trades to take advantage of these differences.

Traders still have the final say

However, there are times when the judgment of an expert trader is essential. Market events that can’t be quantified, such as political developments or unexpected economic changes, can render algorithmic approaches less effective. In these situations, the Trading Pilot allows traders to override the system, making manual adjustments to strategies based on experience and intuition. It’s like taking over the controls during turbulence, using human expertise to navigate unpredictable conditions.

For example, during the 2018 “Beast from the East” cold snap in Europe, manual trading was crucial for meeting surging energy demands as traders adjusted strategies in real-time. The Trading Pilot provides similar flexibility, enabling traders to step in when automated systems fall short and respond to emerging risks or market surprises.

The future of energy trading is undeniably algorithm-driven. As the ACM study illustrates, the use of different algorithm types is essential for adapting to the distinct characteristics of gas and power markets. Execution algorithms provide stability in gas trading, signal generators offer guidance in navigating market volatility, and trading algorithms deliver the speed and adaptability needed for power markets. As the energy landscape continues to evolve, staying ahead will require a balanced approach that leverages the strengths of each algorithm type.