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Case Studies

Case Study #1:  Designing a Bespoke Post-Trade Analytics Framework

The Issue

 

A systematic trading firm wanted help designing a trading performance analytics framework to accommodate their unique investment and trading process. 

 

How we helped

We worked with the client to help structure the various levels of data, from the high-level “parent” order through to execution of child orders.  We provided a detailed spec outlining the data fields necessary at each level of the trade, from strategy IDs on the parent level all the way down to granular information on what FIX tags their brokers should populate.  The framework we designed gave the client the ability to assess all their relevant costs, including how to assess opportunity costs in the context of their unique investment process.  Lastly, we provided custom Python code to create data aggregations on the parent-, wave-, and child-level and to perform specific analyses with those data, such as algo performance across brokers, venue analysis, and net alpha capture. 

Case Study #2: Customizing Broker Algorithms to Maximize Performance

The Issue

 

A quantitative hedge fund sought help customizing their broker algorithms to improve performance.

 

How we helped

 

We analyzed the client’s data to determine how the broker algorithms they used were currently behaving suboptimally.  Using our deep knowledge of trading algorithm implementation, we were able to quickly diagnose certain anomalous behavior and provide specific, actionable recommendations that they could take directly to their brokers for implementation.  In the process, we uncovered a few ways in which the client could easily change their own internal processes to further enhance performance, changes that were completely independent of broker customizations.  We also provided advice on how to assess the performance of the proposed changes.  And because we want our analysis to be transparent and replicable, our deliverable included not only our final report, but also the complete Python code base we used to generate our recommendations.

Case Study #3: Custom Algorithm Design

The Issue

A cryptocurrency fintech firm was looking to create a suite of algorithmic trading products to offer its clients on an agency basis.  They wanted help in defining the most relevant algorithms for their initial offering – factoring in both client demand and time to market – and sought assistance from us in designing these algorithms.

 

How we helped

We provided a detailed set of recommendations on what algorithmic trading products would be most useful for the initial offering and why.  Specifically, we analyzed trading data to show where clients could benefit most via algorithms to motivate the initial product suite.  We then worked with the client to develop a spec for each algorithm.  This involved not simply drafting a spec that was dropped in the client’s lap at completion.  Rather, whiteboard Q&A sessions were held on demand, to walk through the details, discuss the pros and cons of different implementation approaches, the rationale for why certain approaches were favored over others, etc. 

Case Study #4:  Strategic and Tactical Consulting

The Issue

A global investment firm wanted to benchmark their existing agency electronic offering against the best-in-class algorithms offered by its competitors

 

How we helped

We performed a comprehensive, product-by-product gap analysis to determine how their products and functionality measured up against their competition.  This involved interviewing key stakeholders, such as business leaders, salespeople, product managers, and technologists as well as reviewing the business logic of the products and their historical performance.  We also provided in-depth recommendations on where the firm should concentrate its efforts to get the biggest bang for their (limited) buck.  As a follow-on to our gap analysis, we were asked to do an even deeper dive into the product areas with the most critical deficiencies.  In response, we delivered a detailed roadmap on how to improve these areas by leveraging existing technologies used elsewhere in the firm.

Cast Study #5: Ad Hoc and Retained Services

The Issue

A multi-asset investment firm was in the process of enhancing their trading process and had begun assessing the performance of those changes.  The client wanted an external perspective on the relative merits of the changes and on how to interpret some unexpected performance numbers. 

 

How we helped

We met face-to-face over the course of a few hours with the team’s portfolio managers and researchers in a group setting to discuss their experience to date.  We provided the client attendees with an overview of the experience other firms have had after making similar changes to their processes and how these changes affected performance.  We then reviewed the client’s internally-generated performance numbers with them and gave our feedback on where their experience was similar to other firms and where it differed.  We then brainstormed with the client on how to move forward with their planned changes.  Unlike other services where clients ask us to perform analysis and/or write reports, these engagements allow clients to leverage our industry knowledge and expertise in a cost-effective manner on an as-needed basis, either as a “one-off” or an ongoing basis.