Case Study:

Finance: Equities Portfolio Research Platform

Services Rendered: Systems Design | Cloud Architecture | Data Analysis



An asset manager with nearly $10 billion in assets maintained a large equities portfolio constructed using fundamentals-based research. Three characteristics of their research process presented operational challenges:

  1. The research was thorough, involving signals generated using market and reference data over multiple dimensions
  2. The research was exhaustive, with analysis was performed over tens of thousands of potential securities across global equities markets
  3. The research was periodic, meaning it had to be performed on a regular interval

It was quite time-consuming and laborious for the firm’s analysts to perform this analysis manually, which introduced a very real risk of missing investment opportunities. 

While this research process presents as a good candidate for automation, out-of-the-box solutions from large financial technology vendors such as Bloomberg and FactSet didn’t support the integration of custom screening logic and were prohibitively expensive. The asset manager asked APrime to design a cost-effective, low-maintenance solution that would leverage their proprietary research criteria and process.



With deep experience in the quantitative investment management space, APrime was able to quickly identify the key components a solution would require and stitch them together: 

  1. Data feed: We evaluated several market-leading data providers and selected the vendor providing the most value and greatest coverage for price, volume and other reference data. 
  2. Storage: We leveraged TimescaleDB, a relational database optimized for time-series analysis, to power our analysis. TimescaleDB–built on top of PostgreSQL–preserved the ease of use of a popular SQL database and provided convenient out-of-the-box features such as time-series slicing and aggregation. 
  3. Analysis: We built a containerized analysis engine in Python using popular Python-based tools for time series analysis (e.g. pandas, NumPy, and many more). 
  4. Reporting: We used the popular Streamlit framework to set up standardized analytics reports along with client-facing interactive dashboards that enabled the CIO to screen potential investment opportunities on the fly based on proprietary criteria and custom metrics. 

The above components were all packaged and deployed into a Microsoft Azure app environment, forming a self-contained, unified and easy to manage software solution. 



APrime delivered a functional prototype in a single quarter, allowing our client to monitor 10,000 US-based securities in a fully automated manner. In addition to eliminating the need for their team to hire additional analysts, our solution expanded the firm’s capability to identify investment opportunities which otherwise would have gone unnoticed. 

Over the following three months, we further expanded the client’s securities universe to include over 50,000+ global securities of interest, built custom dashboards for intuitive analysis of aggregated data, and integrated an additional enrichment dataset to provide a more targeted subset of investment opportunities for evaluation. 

Armed with these powerful new capabilities, the asset manager rapidly identified and capitalized on several multi-million dollar opportunities, and continues to leverage our tool to fuel the ongoing expansion of their portfolio.

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