Fidessa examines how algos bring efficiency and predictability to derivatives trading
New paper unravels algo myths and highlights the opportunities for FCMs
Chicago, November 5, 2014 - Fidessa group plc (LSE: FDSA) has today announced the release of a new paper entitled Defining algos in futures markets. Authored by Yuriy Shterk, Head of Derivatives Product Management for Fidessa in the Americas, it explores the growth of algos in derivatives markets. More specifically it discusses the types of algos currently in use, the importance of transparency and accountability, and the benefits of embedding algos into the trading workflow. The paper also highlights how FCMs are using algorithmic frameworks to meet regulatory best practices without impinging on creativity, and explores the rise of analytics.
As the derivatives market structure becomes more complex, algos will form a crucial part of buy-side and sell-side strategies. Global reach is now a necessity and the right algos can offer a huge competitive advantage to firms, but only if developed and used appropriately. The paper shows how forward-thinking FCMs are deploying a broad range of algos, completely distinct from those commonly associated with HFT and other controversial practices, to bring efficiency and predictability to derivatives trading. Traders can take advantage of these benefits to bring consistency and discipline to their own trading strategies.
"Derivatives markets are at an exciting inflexion point. Structural changes, primarily driven by new regulations, mean that the complexity of accessing liquidity will soon outstrip the human processing capacity of even the savviest traders," commented Yuriy Shterk. "Algorithmic trading, therefore, is becoming essential, but it needs to be done responsibly, measurably and in line with increased regulatory oversight. Those firms that can utilize algos as an integral part of their trading workflow will be able to achieve this and, at the same time, create a sustainable competitive advantage for themselves."PDF (0.06 MB)