Reality is hazy and there is a high potential for misreads. In a globally interconnected market, change catalysts act quickly There is a lack of predictability and not an absolute cause-and-effect chain. Price fluctuations are aplenty and, consequently, the global supply chain is at greater risk than ever before. The supply of key raw materials is one announcement away from being crippled and the market constantly operates in a state of fear. From US sanctions on Russia and China to President Trump announcing his decision to pull out of the Iran deal and the indirect consequences of these actions, to the shutdown of lead smelters in China, for environmental reasons, without notice as well as anything in between has created ripples in the commodity markets. Firms should be conducting fundamental and technical analysis of new forms of commodity product trading, and considering ways to supplement their existing trading strategies with new technology, and developing the talent to stay ahead of the competition.2018 has been a year of tectonic shifts. There is no doubt that quantitative and algorithmic trading is here to stay, and has the potential to transform commodity trading. And technological advances are making commodity and financial institutions reconsider their trading strategies. Many commodity businesses are looking to capitalise on volatile energy prices with a high frequency and algorithmic trading perspective. In the new age of trading, commodity traders are looking for more liquidity and price discovery in the oil & products and power and gas markets.Īnd while commodity trading is usually associated with longer-term trading strategies, compared to the more active trading strategies seen in equities and interest rates, with the changing landscape across commodity markets and global political uncertainty, there is a trend towards higher frequency trading in commodity markets. The principles of automated trading are being applied to highly volatile and active commodity products, such as oil and refined products, gas and power, as well as across energy, metal, agriculture and soft products. That, in turn, is driving an increased flow of institutional funds, including ETFs, into commodity products, and making traditional commodity merchants rethink how they trade in the commodity futures markets. We see macro global strategy funds developing and adopting automated trading strategies that they currently use in equity markets and transferring them into commodities.
Traders are always looking for an edge, and automated trading systems that require no human intervention have proven to be a competitive tool helping some traders advance their positions.Īs the willingness to use algorithmic trading strategies and systems expands across the commodity markets, concepts such as machine learning are being used to advance the development of automated trading systems to work even more efficiently. The demand from merchants and funds for automated trading continues to grow, as the velocity of trading in commodity markets accelerates. The Commodity Futures Trading Commission estimates that algorithmic trading accounted for 74% of orders in 2015, and 68% in 2016. We already see commodity futures markets being transformed by the rise of algorithmic and electronic trading, with a market historically driven by supply and demand frameworks to now being overtaken by algorithms. That means commodity traders making use of algorithmic input can formulate superior trades than algorithms alone, and suggests all commodity trading firms should be identifying opportunities, rather than threats, from technological advances. But quantitative trading gives commodity traders the ability to conduct superior research and analysis around supply and demand dynamics and other key fundamentals.
Humans, of course, remain very important when it comes to commodity trading, considering the physical logistics and nature of commodities. Quantitative trading and analysis has been shown to improve the risk-adjusted returns across complex financial derivative products, by increasing the speed at which trades can be made using an automated, systematic trading approach.
Much has been written about the ways in which proprietary quantitative and algorithmic trading strategies have the potential to increase trading performance across commodity, currency, debt and equity markets. Part 1: The rise of the machines: quantitative and algorithmic trading in commodities
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