The competition for algo trading talent is reaching new peaks as the popularity of algorithmic and systematic trading has surged considerably in European energy markets over the past couple of years. The growing involvement of hedge funds in commodities has been a major catalyst. Traditional energy and commodity players are now following in their footsteps.
They are ramping up investments in systematic trading technologies which, historically, have been the sole domain of banking institutions and more prevalent in fields such as foreign exchange and equities trading. HC Insider delves into this pressing issue, shedding light on the strategies at play.
Reforming their Model
There are some obvious macro drivers behind the growth of algorithmic trading in European natural gas and power markets: the growth of renewables, the decentralization of energy systems, and increased liquidity in short-term markets.
The HC Insider Podcast
For companies, algorithmic trading has been instrumental to accelerate the development of their trading portfolios against these underlying market transformations. As explained in this HC Insider Podcast with Stephen Roseme, founder and CEO of the Bridgeton Group, algorithmic trading has enabled an acceleration of markets penetration across a wider and deeper array of products thanks to the highly advanced and sophisticated processes that they come with.
Algorithms have become indispensable tools, executing a high volume of trades on an hourly and intra-day basis—a feat that surpasses human capacity. Advanced analytical algorithms leverage vast amounts of data to predict future trends and generate price forecasts, even in other commodities such as agri-food.
Additionally, algorithms play a crucial role in balancing renewable energy trade and limiting price spikes. They offer valuable support in managing volatility, optimizing risk management, and incorporating self-correcting functionalities, performance metrics, and monitoring options to handle operational risks, profit, and losses.
The imperative for commodity traders to retain their competitive edge has placed a new emphasis on the role of their algorithmic trading team within their entire portfolio of activities. Many have been reforming their model by making algorithmic trading their primary source of revenue generation. Several firms within the gas and power space have also reduced the use of discretionary trading and strategies. These rely more on traders’ intuition and expertise in identifying and acting on fundamental and macroeconomic trends and knowledge.
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This trend has naturally increased the necessity to recruit and retain algorithmic developers. Notably, over the past 18 months, HC Group has seen more and more private trading houses acquiring and establishing their own short-term algo trading teams. Several funds significantly stepped up their quantitative recruitment during the second half of 2022 to build out algorithmic energy trading teams from scratch in the last six months.
The ideal candidates must have distinct attributes. Those with Python skills combined with a strong understanding of at least one other coding language and full-stack coding experience are in high demand. A strong academic background in a quantitative field is required to work with the data essential to the algorithmic trading field. Experience with advanced coding and mathematical principles – such as machine-learning and artificial intelligence, as well as to capture value from Big Data, is equally important.
Front-office experience in a commodities’ environment, ideally with trading experience, is a big plus too, especially given the growing importance of the role of algo analysts and traders in revenue generation business. Historically, trading firms have placed their teams of developers in second-line roles where their sole responsibility was focused on validating and back-test trading strategies.
With talent being so scarce, employers are having to yield to the demands of candidates to be able to attract them. Increasingly, individuals are showing a preference for moving into first-line roles where they would be developing and implementing their algorithmic strategies and models themselves.
This, obviously, comes with a reward and a share of the profits generated by their own strategies. In positions where this is absent, for some of the main private trading houses, a discretionary bonus tied to performance is essential to ensure candidates remain engaged in their role (typically between 50-100% depending on their personal performance).
As observed by HC Group, candidates are showing less interest in roles with fixed bonus structures of below 30% on an annual basis. These packages are rarely competitive with other roles in the sector with equivalent base salaries.
With algo talent demand only increasing, the repercussions on compensation levels and structure for these roles have been significant and employers are having to be more innovative. Bonus packages are still tied to individual performance, but some businesses also now incorporate a separate bonus for generating PnL. As such, the percentage can vary significantly depending on the profile of the companies and the packages they offer (including allowances and retention terms).
Yet, the search for apt talent with essential skills to navigate complex, volatile markets remains challenging. In addition to technical skills and trading experience, these roles, even if supported by advanced systems and technologies, remain highly skilled and strategic for the entire front-office performance. Candidates must come with soft skills and the personal abilities to deal with a high level of stress, in addition to business acumen and market knowledge. Such skills or experience are not easy to find in pure developers’ profiles.
In fact, those with strong knowledge of fundamental analysis, beyond quantitative skills are still in high demand. This is especially true for some commodities for which trading activities and performance remain very much dictated by the physical challenges on the ground, like shipping delays that are difficult to incorporate in algo-based trading strategies.
Algorithmic trading has attracted increased scrutiny, as volatility in commodity markets reached unprecedented levels in 2022. One of the virtues of systematic trading has been to improve liquidity in markets. But in times of increased markets risks, the convergence of varying systematic trading strategies simultaneously implemented by different participants has had the potential to bring more diverging signals in the markets, and create more ambiguity, as explained in another recent HC Insider Podcast on volatility.
Discretionary trading remains a constant for many businesses and is largely seen as a complement algo trading. The integration of technology and human expertise is paving the way for the emergence of a hybrid of talent pool and a fusion of competencies in profiles such as ‘quantamental’ analyst.
The best of both discretionary and algorithmic trading will merge into a unified approach which is transforming trading practices and unlocking new opportunities. In this rapidly evolving landscape, commodities participate must constantly optimise their capacity to attract, develop, and leverage tech talent to remain ahead of the pack. – Fatima Sadouki