PARTNER CONTENT
By Zach Adda, North America Head of Customer Solutions, Exasol
& Madeleine Corneli, Lead Product Manager AI/ML, Exasol
Data is more than a buzzword in alternative investments – it has become the lifeblood for countless hedge fund strategies, as quant and algorithmic trading take an ever more dominant position within the industry.
The opportunity to use data to find an edge and deliver new sources of alpha – the uncorrelated returns so prized by investors – has never been greater.
At the same time, the data challenge facing hedge funds is vast. Data sources range from market pricing and news feeds to technical indicators and risk metrics, some live and some pre-aggregated. Finding the correct data – the raw material – is one thing. Utilizing it efficiently is another challenge altogether.
How can hedge funds capitalize on this opportunity most effectively? How can a firm efficiently manage the datasets available and process them through their models in a timely manner?
Firms rely on a central data store as their ‘source of truth’ to collect and unify all these various data streams. This data store also needs to offer hyper-fast performance that scales with the volume of relevant data. This central database powers advanced calculations, statistical analysis, model simulations and the like, which can be further enhanced with machine learning models.
From data ingestion and storage to model development and advanced analytics, Exasol offers a unified and optimized end-to-end data solution, providing that single source of truth. Quants can develop and deploy machine learning models on Exasol, which also acts as a historical portfolio and transaction data store for evaluation and auditing purposes.
Analysis and model development are perpetual partners; analysis helps drive new model development and evaluation of existing models. This constant innovation is key to delivering returns. And the faster the data platform, the more effective this cycle becomes, which is why Exasol’s optimized architecture is a powerful match.
The core of Exasol technology is its massive parallel processing (MPP). This allows for rapid query processing even at high concurrency rates. This unique architecture provides a supreme edge to customers dependent on real-time insights into data or a very frequent re-computation of large amounts of data.
Quant trading clients have started building their tech stack around Exasol, both in the development of trading algorithms, and feeding of the trading algorithm.
What does this look like in practice? One Exasol hedge fund client has over 100 terabytes of raw data that they’re storing within their Exasol data warehouse. The team runs an average of 10 million select queries per day, with an average query execution time of half a second. In the jargon of big data, that’s an extremely performant, high velocity system. And that’s what they rely on. Some Exasol customers are realizing performance gains of several orders of magnitude, reducing 10+ minute queries to seconds and cutting down hour-long ETL/ELT processes to just minutes, vastly improving efficiency on data processing tasks.
Efficient computing at high volume is critical for faster development cycles when testing models and doing research – where the value is being added.
Another critical consideration for hedge funds is ensuring the security and privacy of their data.
Exasol’s solution can be deployed on a hedge fund’s own hardware. This ensures the data sits behind the firm’s firewall and existing data protection systems. Exasol can also be deployed on cloud-based infrastructure (AWS, Azure, GCP) or as a Saas solution for organizations which want to lower their administrative requirements.
AI functionality and AI support is embedded throughout the Exasol platform. AI and machine-learning can be used to improve data pre-processing; getting data cleaned and ready for the models. Sentiment analysis, for instance, or pulling out keywords from masses of text – those kinds of traditional machine-learning models are easy to implement on Exasol and easy to scale.
Generative AI in particular has interesting applications in the alternative investment space. Although the risk of hallucinations makes it difficult to implement Gen AI directly in trading models, it can be used effectively for data pre-processing and gathering signals. For example, extracting keywords, and themes from investor statements and leveraging these as market signals.
That’s where Exasol’s velocity really gives you an edge. And edge, as everyone in the industry knows, means everything to hedge funds – especially when it comes to quant hedge funds and data deployment.
Zach Adda, North America Head of Customer Solutions, Exasol –
Zach helps prospective customers and partners understand the technical value of Exasol, where he guides Proof of Concepts (PoCs) wherever the data environment; on-premises, in the cloud or SaaS. To maximize value, PoCs are built with the prospect’s own data, use cases and BI and analytics tools. Zach also works with existing customers and partners to optimize and extend their use of Exasol.
Before joining Exasol, Zach acted as a Technical Consultant at Blackboard where he helped customers develop and implement custom data solutions. And before that, he worked as a Solution Architect at Teradata Applications working with marketing and data teams to implement and leverage omnichannel marketing applications from the Teradata Marketing Applications suite of technologies.
Madeleine Corneli, Lead Product Manager AI/ML, Exasol –
Madeleine is an enthusiastic data and analytics nerd. She has a decade of experience helping build and implement business intelligence platforms. Madeleine currently leads AI product development at Exasol – focused on bringing impactful and performant solutions to customers. Madeleine has worked for various software companies including Tableau and Salesforce – to create innovative tools that help users solve challenges and tell stories with data. When she’s not digging into data, Madeleine can be found hiking and biking around the mountains of the US West coast.
