Trading in a Digitalized World: How to Navigate Volatility With Everyday AI

Data Basics, Scaling AI Guilherme Castro

Over the years, traders have utilized complex models to follow price trends, predict the duration of commodities cycles, and analyze the impact of new policies in local markets. Data access and quality are major pieces to guaranteeing the operation of these models. But for those who think that digitalisation only benefited trading teams by improving their models' capabilities, the full picture is more complex. According to a CTW poll, traders identified "keeping up with technology and digitalisation" as their main challenge for the next five years.

The reality is that relying only on quantitative data or high-entry barriers for new players is not enough to develop a competitive advantage anymore. The economy is going through a period of rapid transformation: local markets have become global hubs, and many commodities now have strong price cross-correlation, as highlighted by McKinsey. Most importantly, unstructured data now contributes as much as numbers in defining optimal trading decisions.

Thus, trading teams are also going through deep transformations in their operations to keep up with the market evolution. And finding the right platform to navigate the coming tide of digitalization is essential for avoiding inefficiency traps — and it just might determine whether a company leads the market or struggles to adapt.   

Finding the Signal in the Noise

Suppose you are still waking up every day, opening a spreadsheet, and plugging in new information to update several models across different trading products and markets. In that case, there is a high chance you are already behind the competition, or else it’s just a matter of time.

The expansion of sensors, and the development of new technologies connecting equipment and shedding unprecedented visibility on their live status, has completely changed the game. Today, traders can track cargo shipments in real-time across the globe, embed more precise weather forecasts into their models, and at the same time live-track market sentiment across several communication channels (trade publications, social media, etc.). The individualized process of capturing insights by reading hundred-page reports daily is giving way to systematized data collection and preparation, and their conversion into powerful insights. 

Previously, the outstanding amount of data available made it almost impossible for humans to analyze all the relevant sources quickly enough to respond at the pace required by the market. But it is not only data volume and response time that traders are targeting with their evolving their strategies. Guaranteeing data quality and accuracy throughout the whole trading cycle is essential to preventing mistakes that can be costly to the company.

In combination with more accessible cloud computing, transferring the responsibility for complex data analyses to computer systems is modernizing how traders operate. Rather than relying on the relatively infrequent publication of new materials, traders have the capacity to follow the market in real time, capturing a more realistic snapshot of how the industry is behaving, and responding at the same pace.   

Companies deploying advanced analytics — including unstructured data cleaning and classification — are collecting positive returns with more reliable data reconciliation from diverse data sources. More robust market signals enhance the quality of insights supporting traders' decision-making processes, especially during times of extreme volatility, which are becoming more common nowadays. For those companies focused on energy markets, McKinsey found that traders utilizing advanced analytics can see reductions of 30% or more in costs when optimizing bidding strategies for renewable assets in short-term markets.   

Now, you might be thinking: if I simply improve my data collection and utilization throughout my trading process, am I sorted? Unfortunately, the answer is probably not. Implementing advanced analytics is a significant initial step to producing better insights. But if you aren’t empowering the teams responsible for markets, you won’t get an optimal return on your investment. That's why we need to think about how a platform like Dataiku, in supporting its customers, also helps them transform their teams.

Global Commodities, Global Teams

It hasn’t only been markets themselves — expanding from sites for local trading to interconnected global hubs — that have been affected by these changes. The teams operating in these markets have also been transformed.

Especially for large companies trading several commodities across the globe, having experts in different geographies who are able to specialize in a given market can make a strategic difference. The challenge with specialization, whether by market or by product, is that it produces a tendency within companies to form siloes within their trading teams, causing them to operate less efficiently. Siloed teams often suffer from duplicated solutions for singular problems, and the development of multiple tools that, given the lack of intercommunication, are not mutually compatible and often require idiosyncratic, manual maintenance. 

Like the price correlation one commodity might have with another, companies need to empower their teams to leverage their synergies while operating in the market. This is not the reality today. Repetitive tasks must give way to process automation, tools must allow traders to replicate common stages across different workflows rather than building each model independently, and data democratization must become a priority in order to increase productivity. 

One Platform, Infinite Potential

According to McKinsey, traders become up to 90% more productive when they utilize end-to-end platforms to automate processes using advanced analytics for intraday trading.

A centralized platform like Dataiku helps break the silos within teams and across the whole trading lifecycle. Analysts benefit from the automation of activities and shift their focus to developing more complex models that are now available across different teams. Traders can replicate successful strategies efficiently with recipes and plug-ins, building a shared best-practices platform. As a result, teams are more efficient and better able to work across disciplines. They’re also able to navigate market dynamics and are empowered to respond to real-time demands without depending on error-prone human interventions. Importantly, they are able to retain high-level visibility into every stage of each process they run, thereby ensuring a well-governed AI operation.

At the end of the day, companies investing in AI-powered trading teams will break the paradigm of how their processes have run for years. Going through the whole AI maturity journey takes time. Still, companies are collecting the benefits from day one of integrating teams and empowering them with self-service data strategies. 

And with the ability to respond faster to changes in trends or new opportunities in emerging markets, like those linked to the energy transition, companies are more likely to build competitive advantages while expanding their activities. New markets typically come with larger margins to compensate for higher risk. With full governance of their models and data transparency across all their teams, traders can better manage their risk, improving their capacity to target more profitable commodities. Thus, companies increase the value captured without over-exposing their position, all while keeping their data secure and their transactions traceable. These improvements are just the beginning of the Everyday AI journey.   

 

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