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Investment Bank

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Algorithm wheels are gaining significant adoption, with investment firms rapidly implementing new technologies to improve performance and support best execution efforts. However, algo wheels are not suitable for everyone. To help assess best-in-class algo wheel configurations, we’ve developed an evaluation framework.

What is an algo wheel?

An algo wheel is an automated routing process which assigns a broker algo to orders from a pre-configured list of algo solutions. ‘Algo wheel’ is a broad term, encompassing fully automated solutions to mostly trader-directed flow. Investment firms typically use algo wheels for two reasons: first, to achieve performance gains from improved execution quality; second, to gain workflow efficiency from automating small order flow or normalizing broker algos into standardized naming conventions.

Algo wheels are on the rise as the buy-side increasingly grapples with the complexity of achieving best execution and the challenge of sourcing liquidity. The multitude of algo offerings, with non-standard names and unique characteristics, can be difficult to navigate and manage, so a handful of firms have leveraged algo wheels to help normalize offerings and manage commissions.

In parallel, the Markets in Financial Instruments Directive II (MiFID II)’s best execution requirement has prompted investment firms to justify broker selection more transparently. As a result, technology vendors rushed in to sell broker-selection wheels and transaction cost analysis (TCA) to help address this requirement. As technology products helped to lower the cost of implementing an automated trading wheel, buy-side use of algo wheels skyrocketed and suddenly the term “algo wheel” became ubiquitous.

To help assess best-in-class algo wheel configurations, we’ve developed the following evaluation framework that outlines steps for your consideration, based on our observations, educated opinions, and experience, having assisted clients during the vendor selection and implementation processes.

1. Set flow segmentation rules

Consider whether to route orders into a rules-based, automated algo wheel or to a trader for manual execution.

The expected cost, order size and alpha (proxied by Portfolio Manager/Fund) are among the most popular inputs to manage automated flow segmentation rules in an algo wheel. Additionally, it is important to monitor in-progress orders for algo wheel applicability. Large price movements, poor realized/unrealized performance or deteriorating market conditions may be reasons to remove an order from an algo wheel and actively manage it by a trading desk.

2. Form a broker list

Select the right composition of brokers to participate on the algo wheel.

Consider brokers’ algo platform flexibility and commitment to customization in initial broker selection. It may be nest to remove brokers that both consistently and significantly underperform peers, but only after a grace period to allow for experimentation. Adding new brokers on a trial basis allows you to explore new approaches.

3. Calibrate to trading objective and style

Define a strategy, benchmark and algo parameters at the outset with each broker.

Codify a limited number of normalized trading strategies and define each strategy’s goals, constraints and flow characteristics. Then communicate the details to each broker on the algo wheel and rely upon brokers to optimize their own strategies to best meet individual execution requirements.

4. Set order allocation methods

Use randomization to remove trader bias in broker selection.

While many firms use a simple coin flip, a modeled approach aims to randomize broker selection while simultaneously increasing the probability that brokers’ sample sets will be similar to each other. This type of approach can reduce the number of orders (sample size) needed to gain insights into algo rank, and therefore be quite valuable to the algo wheel process.

5. Rank broker performance

Leverage models to get the most out of your data for broker ranking and actionable insights.

A good performance decomposition model is critical to both remove noise from the broker ranking and to assist in identifying areas of improvement in broker algo behavior.

6. Adjust broker’s allocation weighting

Optimize allocation weightings.

Most firms currently eyeball how much to increase the allocation weighting for better-ranked brokers, and how much to decrease allocation for poorer-ranked brokers. However, arguably a more systematic and data-driven approach towards allocation adjustments could serve as one of the quickest and least expensive ways to improve the algo wheel experience. A formal, mathematical optimization model is typically quick to write and provides optimized allocation weightings each quarter to inform the trading desk’s decision making.

For institutional investors only. Not for retail customer use.

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About the authors

Daniel Nehren is Managing Director and Head of Statistical Modelling and Development, Equities at Barclays. In this role, Mr. Nehren is responsible for the development of algorithmic trading product and model-based business logic for the Equities division globally. Mr. Nehren joined Barclays from Citadel LLC, where he was Head of Equity Execution, responsible for all aspects of equity execution for asset management.

Previously Mr. Nehren spent over five years at J.P. Morgan where he was the Global Head of Linear Quantitative Research. Mr. Nehren started his career in finance at Goldman Sachs in 2001 leading a team of senior architects in designing and implementing a next-generation distributed trading platform for the Equities Division.


Shannon Koenig is a Vice President on the Equities Electronic Product team at Barclays and is responsible for partnering with clients to deliver product solutions. Ms. Koenig joined Barclays from ITG where she began as product manager of the Triton EMS platform. She later expanded to cover additional trading software products, working hands-on with trading desks to design and deliver workflow solutions. Before joining Barclays, Ms. Koenig most recently served as an analytics consultant for traders and portfolio managers to produce trading strategy recommendations and broker rankings for ITG’s Algo Wheel product.


Ameya Moghe is a Vice President at Barclays working in the Statistical Modelling and Development, Equities team and is responsible for working with clients to customize their usage of Barclays’ algorithmic trading suite to enhance client outcomes. 

Previously Mr. Moghe worked at ITG where he was the product manager for ITG’s suite of portfolio and tradelist optimization products and services.  In this capacity, Mr. Moghe worked closely with institutional portfolio managers to utilize modern optimization techniques in conjunction with risk and transaction cost forecasting models to generate optimal portfolio allocations while honoring complex real-world constraints. 

Prior to joining ITG, Mr. Moghe received an M.S. in Mathematical Finance from Boston University.  Mr. Moghe is also a CFA charterholder.


Contact the Electronic Trading team

If you are interested in learning more, or would like to receive a full report about this trend (available to clients of Barclays Investment Bank), please contact


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