Financial Data Management

The Algorithmica History Server, AHS, offers a modern, highly scalable enterprise-wide financial data management platform, specifically built for the financial industry.

Financial Data Management

The financial data management platform AHS, Algorithmica History Server, provides you with a scalable solution to efficiently source, clean and distribute validated data to down-stream applications.

The impact on financial Data driven by regulatory requirements such as EMIR, MIFID II, FRTB, SFDR has over the years put an immense challenge to the financial industry. Consequently, many new market driven initiatives have been introduced, both regulatory driven such as FIRDS and ANNA-DSB but also from existing data vendors and niche providers.

These requirements and market initiatives have put an increasing challenge on financial institutions and their existing data management frameworks. As a consequence, financial data has become an increasingly expensive part of day-to-day operations.

A scalable financial data management platform

You more than ever need to think strategically on how you want to address this moving forward with Financial Data Management

  • New data vendors / sources
  • Different integration technologies
  • New data requirements
  • Increased need for quality & validation
  • Control of utilization and cost of data

Algorithmica’s enterprise-wide data management solution, AHS, is built with all this in mind.

AHS provides you with a scalable platform to meet existing and future requirements when sourcing market, instrument, reference, index and corporate action data to downstream applications. By consolidating all your financial data management requirements to the AHS, you achieve immediate cost savings and quality improvements.  

Key features of the Algorithmica History Server, AHS

  • Built-in feed handlers for 20+ data vendor feeds.
  • Unrivaled configurability to support complex automated validation, cleaning and enrichment requirements.
  • Comes with Quantlab built-in for calculation of derived data sets
  • Full transparency and control of incoming and normalized data.
  • Overview of data utilization to support internal and external fee schemes.
  • Dynamic data model to support existing/future data requirements for instruments, market, indices, corporate actions and reference data.
  • High flexibility to fit into any organization’s internal environments, supporting both push / pull technologies depending on requirements.

What you gain from AHS

  • Modern technology to streamline disparate data management processes in either real time or batch.
  • Avoid being dependent on any vendor specific data format or data delivery feed
  • Save money by using Quantlab to calculate expensive derived data
  • Coherent and quality-assured data across all your downstream systems.
  • Control of data utilization to control data costs

Find out more about the Algorithmica History Server

Explaining Financial Data Management: Financial Data Management refers to the systematic process of collecting, organizing, storing, and analyzing financial information within an organization. This encompasses a wide range of financial data, including income, expenses, assets, liabilities, and various other financial metrics. The primary goal of financial data management is to provide accurate, timely, and meaningful information that can support decision-making, financial planning, and overall business strategy.

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