How to compete in a complex landscape: Unleashing the power of big data for start-up hedge funds

Article by Dr Elliot Banks, Chief Product Officer, BMLL

In order to develop effective investment and trading strategies, quantitative or systematic hedge funds require robust infrastructure to carry out extensive research and run complex computation and continuous model calibrations. Our CPO Elliot Banks looks at how hedge funds can level the playing field and overcome data challenges ranging from quant resource to licensing and connectivity.

Data is constantly evolving, so the problem is always evolving,” pronounced Gary Collier, CTO of Man Alpha Technology in a recent report on new technologies that are helping hedge funds evolve. The report predicts that few hedge funds will be able to resist the advantages of big data, namely because of the edge it provides for an increasing number of their competitors. Benefits of data analysis can unlock patterns in market behaviour, improve signal generation and optimise algorithm performance.

Collier goes on to advise that, “you need technology that can help your [sic] process that data and extract value as efficiently as possible.”

Mind the upkeep

Yet in order to develop effective investment and trading strategies, quantitative or systematic hedge funds require robust infrastructure to carry out extensive research and run complex computation and continuous model calibrations.

Gershie Vann, CEO and Founder of Aleto (formerly Magma Capital Funds) says: “The equipment and hardware necessary for receiving data, building, training, and testing models, as well as computing power, can easily exceed a million dollars. This type of investment poses a significant hurdle for start-ups in the systematic hedge fund industry.”

There are a further number of concerns, however, on top of the substantial financial resources needed to acquire necessary hardware, software and associated technical support:

  • Market data licensing fees and data security: The costs associated with this can be extremely high, especially if the fund relies heavily on traditional market data directly from exchanges, other trading venues or incumbent vendors. For example, direct data licences may need to be put in place, usually having to cover both real-time and historical use cases from the outset. Furthermore, additional licence fees may be incurred when accessing the full-depth venue order book, plus cloud compute fees will increase the costs and preparation of setting up a fund for operation.

  • Connectivity to reliable data feeds: Integration of data into the internal infrastructure to make it usable by the team can typically take weeks, adding to time constraints.

  • High cost of building a team of quant researchers: Substantial resources are needed to properly curate, cleanse and manage the various data feeds. The time of Quantitative Research teams is better spent focussing on research and development rather than data procurement and formatting, and Operations teams don’t always have the expertise in the data aspect of the business.

Introducing Data Science Infrastructure as a Service

Start-up hedge funds, however, can access harmonised, cross-venue Level 3 historical data sets to support their fund’s specific operation and growth from the outset, without the high upfront costs and lengthy timelines normally associated with launching a quantitative or systematic fund.

A ready-to-use, quant-built data science platform capability can provide quants with the highest quality derived data source available, solving the time-to-market issues associated with building infrastructure in-house. By using a pre-built cloud-based platform from a specialist provider, early-stage hedge funds gain rapid access to engineered, high-quality, granular market tick data in a secure dedicated sandbox environment for carrying out research, signal detection and back testing. And they can do so without the need to set up direct data licences themselves, nor incur additional licensing fees to access the full depth venue order book (Level 3) data.

Leveraging Data Science Infrastructure as a Service solutions ultimately provides cost efficiency, accelerates time-to-market, ensures consistent data quality and completeness, offers scalability and flexibility, and provides access to expert support and collaboration.

Start-up hedge funds can level the playing field

By outsourcing a portion of a fund’s data engineering to a trusted specialist provider, start-up funds can focus on refining their investment strategies and driving growth whilst removing costs and complexity from their day-to-day business. New hedge funds can level the playing field, enhance their competitiveness, and increase their opportunity for long-term success in the fast-paced world of quantitative finance.

To read more about how systematic or quantitative hedge funds can improve predictability, quality and speed of alpha through a Data Science Infrastructure as a Service solution, read the “Data engineering for early-stage hedge funds” case study available here.

The article first appeared on TabbFORUM.