Custom financial index investment service

Our client provides custom investing indexes for the financial and insurance industries. We are honored to become one of the reasons for the client's success story. Geniusee helped to tailor one of the most important parts of their direct indexing service. We strengthened their team with our profound expertise in data engineering.

Case image

Business context

Our expertise in data engineering appealed to the client. The Geniusee team tailored a custom data-collecting pipeline that helps create financial indexes empowering end customer’s investment strategy. We also ensured data quality and preparation for further analysis. We created a custom solution for custom financial index investing to convert and extract data from subscriptions provided by financial institutions.

Key challenges:
  • Assure incredibly fast data processing speeds
  • Build a secure and trustworthy data transfer process
  • Ensure system stability and periodical platform updates
Business context

Work approach

Services

Our client needed specific services in the data engineering field to provide data collection and quality assurance of information from CME*, Bloomberg, Reuters, and others. We also had to ensure fast processing of up to a few seconds, as speed means billions in the trading world. As a team, we created a secured and stable service for direct indexing strategy, working with third parties and data vendors with a lot of trust from the client.


*CME Group stands for Chicago Mercantile Exchange, Chicago Board of Trade, New York Mercantile Exchange, and The Commodity Exchange.

At this stage, the Geniusee team defined the elements of a system to build — architecture, infrastructure, components, and data. All aspects of a system are based on the requirements specified by our client. We created a workflow for data as it came to us from CME Group databases, and then we stored, processed, and analyzed it. We chose Google Cloud to guarantee security because most data came through Google Pub/Sub.
Our team developed the pipeline to process data and create custom indexes for our client. The most important part here was to create a trustworthy backup to ensure a working recovery plan in case of shutdowns or system failures. We used Python, as this language fits perfectly for data engineering projects due to its simple syntax. It makes it relatively easy to develop a prototype quickly, and speed is crucial for our client.
Data quality was our focus during this stage. It was vital to check before data ingestion. We based it on robotic comparisons of information from different sources during specific periods. To process historical data, we used batch processing. Eventually, we came up with automated checks on a daily basis. The Geniusee team also created unit tests for the delivered pipeline to ensure correct work, smooth flow, and untroubled functionality.
At this stage, our team was working on the DevOps of the pipeline. We described its architecture and the process of architecture diagram creation. We decided to rely on infrastructure as a code (IaC). By using machine-readable definition files instead of hardware configuration or interactive configuration tools, infrastructure as a code enables computer data centers to be configured and managed efficiently.

Project tech stack

As our client works with traders as end customers, we had to choose only fast and secure technologies for custom financial index investing. To satisfy those criteria and ensure a high-performance rate and asset allocation, we decided to go with cloud services.

Tech stack

Geniusee team

As the project centered on data engineering and required a significant amount of expertise, we decided to go with only senior specialists and our Head of Data Science and Data Engineering as a lead.

Product team
  • PM photo
    Oles

    Tech lead

    The client’s company consists of people you want to grow with. Many of them hold a Ph.D. in computer science. Being a part of such an outstanding team is an honor and motivates you to evolve constantly. Some of our colleagues on this project are heads of hedge funds, so we were sure the product we were building was tailored under perfect guidance and the requirements of the market, industry stakeholders, and our end customers eventually. Another part that drove and encouraged us was the tasks — optimization and development of algorithms that work in a fraction of a second.

  • Tech Lead photo
    Daria

    Project manager

    Working with a completely different project that profoundly focuses on data science was enjoyable for me. We tailored regular reports and deep communication with our client. This project seems highly successful to me due to a combination of two facts: our profound expertise and trust from the client's side.

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Features

One

High speed of data processing

We developed a solution that is 7.5 times more effective than existing services available on the market. This allows to create custom indexes based on individual stocks and huge funds that are suitable for asset management, traders’ financial goals, and cost effective.

Two

System
scalability

To ensure scalability, we based our platform on cloud services, ensuring their stability with custom-tailored backups. The system has copies in two regions to support Google Maintenance and provide 24/7 availability.

Three

Flexibility of the solution

Trading is a highly challenging and rapidly changing industry. To guarantee our system runs along with it, we ensure its flexibility based on module structure of custom indexing platform’s pipeline.

Four

Daily-based data quality check

We ensure data quality with automated tests based on batch processing and historical data comparisons from external vendors. We compared data from different sources to ensure that the data was correct.

Five

Security
measures

We use in-built solutions from cloud providers together with VPN services to ensure that our system is protected and leaks are prevented.

Results

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