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  • Paul Smith and Tatiana Lyubimova

AI applications to Commodity Trading

Updated: Apr 18



Today's financial markets generate substantial amounts of valuable data. This is especially the case with global markets that see billions of dollars in transactions on a daily basis. The challenge facing commodities traders is how to harness and analyze this data with accuracy and speed to increase profitability. By deploying artificial intelligence systems, financial firms can more efficiently process this large influx of data and use it to their advantage.

Understanding and predicting the supply and demand for certain commodities can influence trading profits significantly. Artificial intelligence can be used to collect and process supply and demand data to establish market conditions and identify factors that are of the greatest impact.

Let's take a look at how Particle.One artificial intelligence can increase the efficiency of information in the commodity markets.


  • Price Forecasting Algorithms are already in wide use by many financial companies to forecast pricing. But with today's technological advances, artificial intelligence can be used to more efficiently aggregate research, market sentiment, and historical data to arrive at more accurate predictions in commodity prices.

  • Risk Management Commodity risk calculations can be a time-consuming job, especially on portfolios comprised of thousands of positions. By using machine learning to supplement this operation, risk managers will have more time to focus on additional critical projects in the firm.

  • Supply Chain Modeling Efficient movement of commodities through the supply chain is crucial to profitability. The supply chain component of an artificial intelligence system can be a tremendous asset for the optimization of real-time commodity data. Greater precision in this data can greatly assist in making critical trading decisions.


By using artificial intelligence, investors can make more informed trading decisions without being inundated with overwhelming amounts of data. Our company has collected 150M of time series, covering over 65 commodities in over 150 countries. Our Knowledge Graph technology uses metadata to highlight casual relationships between the time series.


Contact us for more information on how our technology can assist you in the construction of the supply and demand matrix for your commodity strategies.

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