Algorithmic Trading Adds Complexity to Derivatives
Order execution is increasingly becoming an exception handling process as global derivatives markets become more dominated by algorithmic trading. But without the ability to monitor how orders are performing in real time, and to automatically identify deviations, potential gains from algo trading can be thwarted.
“The main challenge is the global nature of the F&O market and the large number of underlying asset classes involved, each of which has its own idiosyncratic behaviors,” Yuriy Shterk, head of derivatives product management at Fidessa, told Markets Media. “On top of this, increasingly overlapping regulations are adding to the complexity.”
Fidessa has launched an order analytics service for its derivatives community that enables users to monitor all orders in real time and adjust algo parameters accordingly. It also provides a post-trade capability that shows execution performance, which is an essential component of demonstrating best execution as required under MiFID II, the company said in a release.
“It’s all about providing transparency and visibility in terms of how the algo models are performing,” Shterk said. “The trader can see not only when the algo made a decision to send an order to the market, but also the reasons behind that decision.”
The order analytics platform, which is integrated with Fidessa’s order and execution management systems, enables traders to measure the success or otherwise of a specific order – its performance against benchmarks, its market impact, slippage, etc., – and adjust execution parameters ‘in flight’ where an order looks set to underperform, said Shterk.
“Post-trade analytics provide a feedback loop for the continuous improvement of execution performance,” he said. “With regulators around the world increasingly articulating the need to provide evidence of best execution, the ability to analyze algo performance and capture the information to demonstrate the decision-to making process that went into execution of the order is a powerful differentiator.”
Different traders focusing on different product sets will have their own requirements in terms of how they want to analyze execution performance. “The solution needs to be comprehensive enough to account for the nuances of specific instruments and markets and flexible enough to meet the needs of different segments of the trading community,” said Shterk.
Another challenge is ensuring that the vast amount of data needed for meaningful analysis is readily accessible. “Whether its order level information or market data, it needs to be optimized to simplify the workflow; this can only be achieved effectively by integrating the analytics into the OMS or EMS that traders use in their execution or monitoring activities,” Shterk said.
Adoption of algo execution in the exchange-traded derivatives markets is continuing to grow. There is more algorithmic execution taking place in the AsiaPac markets and in some of the underlying asset classes that were traditionally mostly manual.
“At the same time, there is continuing pressure from the regulators to ensure all of the algorithmic models being used are fully understood and tested to avoid unexpected surprises,” said Shterk. “As markets continue to evolve and as volatility increases we expect to see greater use of algorithmic models.”
Most FCMs are providing access to algorithmic models as part of their execution toolkit. These models are generally well adapted to the F&O markets globally and take into account the specific nuances of the instruments available for trading. “The next frontier is to provide greater transparency of algo execution, as well as tighter integration into trading workflows for sell-side traders and their buy-side clients,” said Shterk.
Featured image via iStock
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