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In the energy and commodity trading industry, traders negotiate transactions with brokers and counterparties directly. These negotiated transactions are called over-the-counter (OTC) deals. Price discovery for OTC deals is unlike price discovery for common equity, options, and other financial instruments, where pricing information is generally market-efficient and available in near real time. In OTC trades, the specific terms of traders’ agreements drive the price discovery process. For example, energy and commodity traders use the Interconnectional Exchange (ICE) trading solution’s chat program to discuss transactions and discover fair commodity prices. In this blog, we describe how global integrated energy company bp, an AWS for Energy trading customer, uses AWS machine learning (ML) and serverless technologies to enhance its price capture process. We share its AWS solution architecture and explain why it’s important to bp’s energy and commodity trading business.
In the OTC deal negotiation process, price quotes from traders’ chat messages are the source of intraday transactional pricing information. Once transaction terms are agreed upon, traders submit instructions in bp’s trading systems to execute a trade. At that point, pricing information gets recorded. The rest of the negotiation details and prospective transaction prices are not recorded. However, that information is still valuable because other personas in a trading organization—such as analysts, strategists, and risk managers—can use it to develop future trade strategies, marketing strategies, risk management, and additional market data sources.