Energy Trading: Gambling or Prediction? Can Chat GPT Beat the Bookies?

Risk is inherent in any form of trading but to what extent is trading energy commodities a matter of chance and to what extent does educated prediction contribute?

Moreover, with the advancements in artificial intelligence such as ChatGPT, is it conceivable for these models to out-predict seasoned traders and energy analytics tools? Read on to find out our verdict.

Trading in the Energy Market: Gambling or Prediction?

Supply and demand in the energy markets is complex and unpredictable, with many variables and many interactions between those variables. In addition, unexpected global events disrupt the energy industry significantly.

Does this mean there is no difference between trading and gambling? To the untrained, it may seem that way, especially considering the language used when it comes predictive markets, such as “betting”. However, some key differences between gambling and energy trading are discussed below.

Speculation vs. Calculation

At the heart of gambling is the notion of speculation. Unlike gambling, professional traders base decisions on a range of data and predictive models, making the process far more analytical than purely chance based – especially when using the right energy analytics tools.

In addition, with gambling, all players typically have the same odds. In energy trading, however, having more or better information (about political movements, weather changes, etc.) gives traders an edge.

Disciplined Trading Strategies

Gambling is typically about quick wins while successful trading is about long-term profit backed by strategy. Even with intraday or day ahead trading, strategy and data inform decisions.

In addition, professional trading is not about win or lose, black and white outcomes like with gambling. Instead, gains and losses are part of an interwoven tapestry of long-term strategy.

Energy Risk Management

Professional traders employ rigorous risk management operations to limit potential losses – something that’s often absent in pure gambling. Whether it’s stop-loss orders, diversification, or other means, risk management mechanisms provide protection and again, a strategic approach as opposed to relying on pure luck. Dedicated energy trading risk management software also provides robust tools for mitigating losses. 

Can ChatGPT Outsmart the Market?

ChatGPT has proven itself valuable for many tasks, from coding to data analysis. But how does it fare in the context of ETRM analytics? Can traders rely on it to support their decision making?

How is ChatGPT Used in Energy Trading?

We recently discussed the role of AI in energy trading software and its benefits. Dedicated AI-powered trading platforms are capable of processing information quickly, conducting predictive analytics and learning from past decisions. It also engages in emotionless trading, making calculated, objective, rational decisions free of the influence of emotion and cognitive biases.

Some traders have tested ChatGPT to see if it can measure up to specialized platforms. Among the tasks it may be used for include:

• Analyzing complex and vast datasets quickly, considering the implications of multiple variables such as economic indicators and weather changes.
• Developing trading strategies by considering market conditions, risk tolerance, trading goals and other parameters.
• Finding and analyzing valuable information on the markets.
• Analyzing news headlines and other qualitative data in order to discover trading signals.

Another benefit of using ChatGPT is that it can learn about an individual user’s preferences and requirements. As such, it can produce personalised recommendations, leading to better decision-making over time.

Limitations of Using ChatGPT for Energy Trading and Energy Risk Management

The accuracy and effectiveness of any application of ChatGPT for trading depends on a range of factors.

Data Quality, Quantity and Completeness

The quality, quantity, and completeness of its training data is vital. For example, if a model is going to analyze historical information in order to make predictions about future scenarios, if the information provided is incomplete or it does not have sufficient information regarding all the variables involved, the conclusion may be inaccurate.

In addition, ChatGPT needs to be up to date at all times when it comes to trading, especially in the short term. Anyone that was using it before GPT-4 was released was limited to information published up until 2021. Considering the global economic events that occurred during and after 2021, using GPT-3 for energy trading back then would have been limiting.

Also note that, even though ChatGPT can now access information published online after 2021, it is still only trained on data no later than September 2021. Any novel market events that have occurred since then will not be make up the basis of predictions.

Quality of Prompts

Another key element in using tools such as ChatGPT is the quality of the prompts provided. As prompts are created by humans, the advantage that technology brings regarding consistent, standardized methods becomes redundant.

Memory Constraints

It may be necessary to analyze a smaller amount of data at a given time than what you want to due to ChatGPT’s memory constraints. This reduces overall efficiency compared to using a dedicated analytics tool.

Overfitting and Other Inaccuracies

Using ChatGPT can lead to overfitting, which is the case when relying on any AI model. This is because, when queried about trading strategies or financial data, it will rely heavily on its training data, which is historical by nature.

While historical data is important, overreliance on it may lead to poor performance when the model is exposed to new data. As such, the model will not account for unexpected market conditions that have not occurred before or are rare, or conditions that were not included in the training data.

Users may also fall into the trap of not testing the model on enough new data during the training/feedback process, furthering the risk of overfitting.

Another potential cause of overfitting is creating a model that is too complex, which may happen unintentionally if the user’s prompts are too intricate. The result would be a model that only performs well in very specific scenarios as opposed to real trading situations.

Models expertly developed for the context of energy trading are more reliable than generic solutions such as ChatGPT.

Aside from overfitting, ChatGPT has been known to make up statistics and answers to questions, as well as make simple mistakes such as misidentifying the Prime Minister of Japan.

The Verdict: Can ChatGPT Beat Professional ETRM Software?

It’s clear that ChatGPT is not yet intelligent enough to make informed trading decisions and it’s best reserved for auxiliary tasks such as research. While it can certainly be helpful as an assistant, making suggestions about trading strategies and signals, it should not be relied on for more comprehensive purposes. 

Conclusion

While energy trading does involve a degree of speculation, calling it gambling oversimplifies the intricacies and the analytical depth that goes into making decisions. As for AI models like ChatGPT, while they offer promising capabilities in data processing and pattern recognition, they aren’t silver bullets.

For the foreseeable future, a combination of human analysis/experience, and robust analytics software is the best approach for streamlining trading and gaining insights that can inform decisions – decisions in which humans have the final say.

Our CTRM and ETRM software is equipped with advanced energy analytics and risk analysis tools. Traders can make data-driven decisions using what-if scenarios, portfolio analysis, Monte Carlo simulations, margin optimization and more – all with intuitive data visualisations. To book a demo, contact us today.