Commodity trading is undergoing a digital revolution. Where traders once relied on news bulletins and daily reports, decisions are now made based on data coming in every second. Oil, metals, and agricultural commodities are all key market segments affected by real-time information flow.
The volumes and stakes are enormous: the industry’s total gross profit in 2024 is estimated at around $95 billion. To compete in such a market, trading firms are adopting technologies that enable instant data collection and analysis. As a result, “trading is faster”: algorithmic systems can now execute trades in a fraction of a second, and monitoring programs continuously assess market risks in real-time.
Pricing: Each Event is Immediately Priced
Many factors shape commodity prices, and real-time data has accelerated the pace and impact of market shifts. Any significant event, from a politician’s tweet to a weather report, is reflected in quotes almost instantly. For example, in April 2025, the price of gold soared to a record $3,430 per ounce on a wave of demand from investors seeking a “haven” amid geopolitical tensions. The immediate dissemination of information about the weakening dollar and US-China trade tensions led to the precious metals market reacting almost immediately.
In the oil segment, pricing is also increasingly dependent on here-and-now data. Traders monitor OPEC decisions, stock news, and even satellite signals, recording the movement of tankers on sea routes in real-time. As a result, the oil market is becoming more transparently linked to the actual balance of supply and demand. It is significant that, according to the World Bank, the historical overproduction of oil by the end of 2024 smoothed out the effect of the Middle East conflict on prices. In other words, when data points to excess supply, even shocks do not cause the same excitement as before – information about the real state of the market calms speculative sentiment.
On the other hand, if there is a supply disruption somewhere or a natural disaster threatens the harvest, this information is instantly picked up by trading terminals around the world, causing a corresponding price movement.
In agriculture, the situation is similar: information about the weather, crop conditions, and yields comes from satellites and IoT sensors online. Thus, satellites and sensors allow agricultural traders to quickly assess the condition of crops, receiving more accurate forecasts for the grain harvest signals that immediately affect futures prices for wheat, corn, and soybeans.
Risk Management: From Reaction to Prevention
With prices changing every second, effective risk management requires real-time data. While volatility remains constant in commodity markets, traders must see the complete picture instantly. Modern commodity risk management (CTRM) systems integrate data streams, enabling traders, logisticians, and financiers to work with a unified “picture of the world” in real-time. This integration allows immediate assessment of positions and exposures for each asset, letting traders make hedging decisions before losses accumulate. Unlike traditional approaches—where risks were assessed at day’s end using Excel spreadsheets—today’s decisions happen at the moment.
Speed of response is foremost during so-called “black swan” events – unpredictable shocks, be it a geopolitical crisis or a pandemic. In 2022–2023, the industry has learned that unexpected events are no longer something rare but a new reality. As a result, risk management is shifting from reactive to proactive. AI-powered systems scan news feeds, weather reports, and logistics data to spot warning signs early. According to industry surveys, such predictive analytics allows companies to identify risks and take action before problems escalate into crises.
As a result, a prepared trader has more time to adapt to sudden changes in the market environment – be it a sharp rise in fuel prices or the introduction of export restrictions on metal. In a world where “the market is moving faster than ever,” such speed and coordination provide a competitive advantage.
Algorithmic Trading: Speed and Data vs. Intuition
One of the most noticeable consequences of the data era has been the automation of trading. Algorithmic strategies have long been present in stock markets, and recently have firmly established themselves in commodity markets. Specially developed programs and bots can process news or index changes in milliseconds and immediately place an order on the exchange.
This “superhuman” pace of decision-making provides a clear advantage – and forces all participants to adapt. In the oil segment, for example, so-called CTA funds (Commodity Trading Advisors), which use trend-following algorithms, have captured a significant share of daily trading volume in the post-pandemic years.
2024 has shown that speed alone does not guarantee success. In a relatively narrow price range, when there are no clearly defined trends. Some algorithmic funds have suffered losses and reduced their presence in oil. That opens the way back to fundamental traders who rely on real-time data in the real balance of supply and demand. However, in most markets, there is not a rollback to intuitive management but a synthesis of approaches. Modern quantum fundamental strategies (quanta mental) combine the power of algorithms with expert analysis: models process Big Data – from news to IoT signals – in real-time and provide tips to traders, who then make the final decision. This approach has proven itself in conditions of high turbulence.
It’s worth remembering that algorithmic trading relies on good data to work effectively. Raw information has become the new asset: data warehouses, direct feeds to exchanges, and subscriptions to news feeds are all necessary investments for the 21st-century trader. Funds and proprietary trading firms compete to see who has the fastest and broadest access to data. There is even a concept of alternative data from satellite images of oil storage facilities to GPS tracking of grain trucks.
Conclusion
Real-time data has transformed commodity trading across the board, from instantaneous price discovery to strategic decisions months in advance. Oil, metals, and agricultural markets have become more interconnected and transparent than ever, with information about events and fundamental changes instantly available to all players. That has reduced the role of blind spots and gut feelings and increased the importance of data collection and processing technologies.
Those who can quickly extract valuable knowledge from the flow of numbers and turn it into action gain a tangible advantage. Of course, volatility and risk have not disappeared – but traders now have different tools. They have continuous quote lines, signals from IoT devices, machine learning algorithms, and powerful visualization systems. All of this helps them not just respond to the market but actively shape it—setting the tone with fast, thoughtful decisions.
The industry faces new challenges ahead: the development of artificial intelligence, even more data (for example, with the emergence of new satellite constellations or the introduction of 5G in agriculture), and, probably, new types of commodity assets (the same carbon trading is already becoming part of the market). But one thing is obvious – the era of trading “blindly” is a thing of the past. In the dynamic world of commodity markets, it is no longer the largest that wins but the fastest and most informed. That is why the ability to quickly “read” the market in real-data flows has become a key skill and a guarantee of success for companies on all commodity platforms today.
