JP Morgan Chase Applies Machine-Learning to Currency Market Trades

JPMorgan Chase, a top player in the $5.1 trillion-a-day global currency market has started applying a new technology that enables machine-trading programs to learn from previous trades and search for the most profitable way to execute them, according to a story in Reuters news. Algorithmic trading is a rapidly growing trend in currency markets where banks have cut back on trading teams, relying more on automated computing strategies to trade efficiently. Algos are used by market makers to search for platforms with deeper liquidity pools or to break up large orders to reduce market impact. JPMorgan’s new algorithm, named DNA or Deep Neural Network for Algo Execution, effectively combines what a multitude of algos currently do into a single strategy, and allows the framework to judge how a client order should be executed. For example, a typical time-weighted average price order executed by an algorithm may aim to buy a particular amount of currency over a few minutes or hours. But if the order is not executed within that time frame, the machine will trade aggressively toward the end to buy the required amount. The new algo aims to take that decision-making process one step further by determining how best to execute the transaction based on results of past trades. The algo has already been deployed for trading G7 currencies such as the euro, dollar and sterling, where it has access to data from thousands of past trades. JP Morgan’s trading platform has also seen an algorithmic turnover surge. Order volumes of over $5 million rose 50 percent on an annual basis, with 80 percent of orders being driven by algos, the bank said.