BestEx Research Launches IS Execution Algo Model

BestEx Research has launched a no-code framework for building and optimising implementation shortfall (IS) execution algorithms.

The BestEx Research Adaptive Optimal (IS) Framework allows clients to create tailored implementation shortfall algorithms and compare performance through experimentation for optimised, custom design for their unique order flow and preferences.

The firm adds the new service allows users to develop completely custom IS algorithms and understand the impact of design decisions on resulting execution costs. The framework features customisation of common IS preferences, such as whether to pace trading or favour an opportunistic approach, whether orders must complete, shifts in execution strategy based on market conditions, and more. The platform’s built-in A/B testing tools allow for experimentation across strategies for fair performance comparison, resolving traders’ long-standing questions about how to optimise performance, the firm adds.

In addition, BestEx says the new framework allows sell-side firms to provide and support “virtually any” IS algorithm, tailoring offerings to each individual firm, trader, and order with no coding required. The platform’s point-and-click algo development tools mean sell-side firms can recreate their existing offering in BestEx Research’s Algorithm Management System (AMS) while continuing to experiment for improved performance and partner with their clients to build optimised trading solutions.

“This is groundbreaking for our industry,” claims Hitesh Mittal, founder and CEO of BestEx Research. “For the last two decades, buy-side firms have used opaque, black-box implementation shortfall algorithms with just a handful of urgency settings, but such limited algorithms simply cannot work well for all portfolio managers for all types of orders.

“True optimisation of trading costs requires careful examination of orders’ alpha, the execution risk preferences of the managers, and the intraday liquidity in each product being traded, and Adaptive offers the tools to help our clients achieve that goal–something that’s been out of reach for decades,” he continues. “It’s not only that existing IS algorithms are not tailored to each investment manager; they also tend to suffer from a number of design issues that inflate trading costs unnecessarily; a misunderstanding of the trade-off between market impact and alpha decay, overly aggressive order completion behaviour, and long-term adverse selection are a few examples. Our new Adaptive Optimal (IS) Framework addresses these challenges and allows us to create customised solutions for each of our clients.”