The key traits of resilient E/CTRM systems

With shifting market dynamics and digital innovation increasing energy trading volumes and complexity, an Energy Risk Europe panel explored whether energy and commodities trading and risk management (E/CTRM) systems are meeting the evolving demands of traders. Experts also considered the key components of resilient trading systems in today’s dynamic markets. This article presents the key takeaways from the discussion

The panel

• Gaurav Garg, Managing partner, capSpire
• Rhodel D’Souza, Executive director, JP Morgan
• André Jäger, Senior vice-president, product management, ION Commodities
• Moderator: Philip Harding, Commercial editor, Risk.net

The changing needs of energy and commodities traders

The energy transition, alongside market decarbonisation and digital innovation, is transforming the nature of energy and commodities trading. Firms are tackling increasing trading volumes, heightened volatility and escalating complexity, along with a shift to real-time and intraday trading. These market forces are, in turn, reshaping E/CTRM system requirements.

Gaurav Garg, capSpire

Gaurav Garg, managing partner at capSpire, explained some of the additional impacts of the energy transition. “Traders need technology solutions that support portfolio diversification and expansion,” he said. “Traceability is also becoming an increasingly important component of solutions, as companies seek to ensure their green power commitments translate into the procurement of renewable energy in the wholesale market.”

“Market disruptions and increased volatility are driving client needs for more sophisticated risk management capabilities,” added Andre Jäger, senior vice-president of product management at ION Commodities. “Clients are looking to better understand risks, as they seek to mitigate potential future disruption and understand the impact of volatility on their growing portfolios.”

Data challenges are also increasing. Changing market dynamics and the rapid growth in data volumes and inputs means firms are increasingly under pressure to extract, combine, process and deliver data in real time to feed analytics, pricing and risk metrics. There is growing demand for real-time data streaming, as well as integration with external platforms and advanced application programming interfaces (APIs).

Rhodel D’Souza, executive director at JP Morgan, outlined how its clients are becoming “increasingly sophisticated” in this regard.

“Landscapes are diversifying, and demands are increasing on the data platform side,” continued Garg. “Larger clients that have multi-commodity landscapes and systems want to consolidate and integrate everything into single reporting, with risk analytics executed across their portfolios.”

All panellists agreed that there is a huge drive for automation across the market. “Firms are looking at delivery models, exploring AI [artificial intelligence] and machine learning, and seeking ways to simplify and standardise processes,” said Jäger. Manual trading is giving way to algorithmic and automated decision-making.

“Clients are building up predictable algorithmic execution, especially in the day-ahead and intraday markets,” added Garg.

New analytics and tools

As the energy trading landscape grows more complex, firms are focusing increasingly on enhancing resilience in their trading strategies. This includes leveraging advanced analytics, structured data and cutting-edge technologies.

Energy trading clients are becoming increasingly ‘data hungry’, D’Souza explained. “Clients want structured data and inputs, such as net flows, as well as buy and sell volumes in particular markets and advanced visualisations, such as volume heat maps. These can help determine the best time to trade title-transfer facility or the national balancing point, and how to hedge with foreign exchange instruments.”

Predictive analytics are also playing an increasingly large role in energy trading resilience. Garg highlighted the significant impact of weather patterns on energy supply and demand as an example: “By combining historical data with weather data, firms are looking at whether they can anticipate price movements more effectively and make decisions accordingly.” Advanced natural language models are particularly gaining traction in short-term markets, scraping and aggregating data from diverse sources.

Increasing expectations of E/CTRM systems

These evolving needs are placing increasing demands on E/CTRM systems. Solutions must cater to the needs of a growing array of different sizes of firms with varying business models, while accommodating the complexities of modern energy generation and consumption.

In some cases, traders can have overambitious expectations. Tension exists between traders’ need for quick, revenue-generating actions and a system’s ability to handle detailed long-term contract complexities.

“Capturing a 20-year power purchase agreement contract in just three clicks is not realistic. There’s just too much variation and complexity,” said Jäger. “It is therefore important to have the right support model in place for traders when it comes to entering contracts. In addition to meeting trader needs, CTRMs cater to other business functions in the organisation – such as risk management, operations, accounting and finance – which depend on the data being captured completely and accurately.”

Many traders expect coverage for diverse portfolios spanning multiple commodities, regions and timeframes. “Every trader wants every single product and instrument configured in the trading landscape, in case they trade it one day,” said Garg. “Sometimes a line needs to be drawn as to what is realistic. Systems have limitations. They are not all configured for trading everything end-to-end.”

Determining E/CTRM strategies and prioritisation

When it comes to setting up systems or developing existing landscapes, panellists underlined the importance of future-proofing systems, but also stressed that companies need to carefully consider their business models and strategies.

“In principle, there are three common priorities for firms when it comes to their E/CTRM systems: scalability, systems interoperability and cyber security. However, different sizes of firms have different needs. There is no ‘one size fits all’. Companies must look at their portfolios and strategies over the medium term to define their target architecture landscapes and solutions,” said Garg.

“Different business models require very different capabilities and support from a solutions perspective,” added Jäger. “Standardised, high-volume business models with a focus on automation require scalability, while a highly bespoke customer-specific model focused on long-term delivery contracts needs flexibility.”

“No single E/CTRM can address all of the market’s needs equally,” he continued. “There are specific functionalities that certain E/CTRMs are good at, and others are not. It is very important for clients to understand what will create value for their organisation and identify the most appropriate solution to fulfil their needs. For example, ION has very flexible solutions with functional depths that support global multi-commodity businesses, but they don’t come at the same price as a very standardised SaaS [software-as-a-service] point solution focused on a particular capability.”

Ensuring data quality, security and integrity

Ensuring the quality, security and integrity of data within E/CTRMs is critical. “This means defining a data framework,” noted Garg. “The most important step is to establish data ownership and naming standards. Standard data structures for transporting data between systems mean it will be easier to bring a new E/CTRM system onto your data platform and ensure interoperability. Centralised and consistent reference data management also needs to be established and maintained so that different systems can talk to each other.”

Jäger also stressed the importance of automation in tackling data quality challenges. “Automated checks on data quality can provide additional visibility. It’s about supporting the right rules with the right level of automation,” he said.

To tackle the data challenges facing the energy sector, firms should learn from other sectors. “With the increasing volumes of data in energy markets, we need to look at markets where some of these high-volume data problems have already been solved, especially on the banking side,” concluded Jäger. “That means harnessing the capabilities that are being utilised to manage volume and offer dynamic scaling, including streaming technologies such as the Apache Kafka data-streaming platform and cloud utilisation.”

In summary

E/CTRM systems are evolving to meet the demands of traders in today’s complex energy markets. Firms are increasingly focusing on automation, real-time data and advanced analytics to enhance their trading and risk management capabilities. Dynamic scalability, data management, real-time risk and flexible reporting capabilities are just a few of the ways these systems are enabling energy firms to navigate an ever-changing landscape with greater confidence and resilience.

The panellists were speaking in a personal capacity. The views expressed by the panel do not necessarily reflect or represent the views of their respective institutions.

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