Why Deephaven?

You have a data problem…


Information determines winners…and losers. Leveraging advanced technology is required to be competitive. Bigger data and faster iterations drive innovation, grow revenue and mitigate risk. But harnessing and exploiting big data in a meaningful way is expensive and time-consuming. Can you afford inaction?

What are your options?

The volume, velocity, and variety of today’s data overwhelms traditional architectures.  Data is stored among a series of siloed applications, while latent data and complicated workflows bring insight to a crawl. Software, infrastructure, and personnel costs mount as tech resources become more scarce. Instead of researching alpha, quants self-support their platform. Non-revenue generating producers consume too much development bandwidth.

– Non-programmers lose time waiting on intermediaries.  
– Custom monitoring and reporting takes too long.  
– Research cycles drag.
– Inaction drives down revenue.
– Wasted resources.
– No competitive edge.
Commercial (legacy) players may have served capital markets well in the past.  However, those ticker plant options no longer have the agility to drive alpha in today’s environment. Analysts want (and the market demands) answers fast. Instead, they wait endlessly for highly priced techies to make the DB useful. Their proprietary programming languages are complex and difficult to learn. More than one platform is required to complete common tasks. Incorporating data presents too many challenges.  

– Lack innovation.
– Ad hoc platforms limit productivity.
– Accessible only to data scientist unicorns.
– Established, but expensive.
– Antiquated pricing structure.
In today’s capital markets, we’ve encountered no serious players using Open Source as the backbone of their data pipeline. These building blocks require support from outside consultants, and you still rely on your development teams to build a tremendous amount of infrastructure. Important, but relying exclusively isn’t an answer for capital markets player.

– Varying quality.
– Forfeit independence.
– Too narrowly capable.
– Free is never free.
– Get ready to build.
Exceptional expertise in data system design and technology is hard to come by. Building your own solution requires qualified – and expensive – programmers and developers. Solutions take years to develop, and even if a system gets up and running, maintenance is a serious commitment.

– Huge risk.
– Massive upkeep.
– Could wait years before seeing ROI.
– Perpetual maintenance.
– No guarantee of success.

Deephaven: One Solution

  • Data is centrally available via a single, easy-to-use platform.
  • Handles real-time, historical, and alt-data seamlessly.
  • Efficiently sources, logs, validates, and cleans data.  
  • Empowers any user to directly develop, test, and adjust their own strategies.
  • Blazing fast research cycles occur in days/weeks instead of weeks/months.
  • Easily configurable access control.
  • Multiple language interfaces: e.g., Python, Java, R.
  • Proprietary APIs and third-party integration.
  • Installed or cloud-based containerized configurations.

We’ve done the hard work for you. Start building alpha now.

Features

The Deephaven Advantage

Comprehensive Data Backbone
Serious capital market players have a lot of data: market ticks, position states, OMS records, customer messages, back-office files, reference data and machine metrics. Deephaven services it all. A data pipeline that provides all knowledge workers with actionable, relevant information immediately. Easily configurable access control enables fine-tuned sharing and data protection.
Universal Access and Ease of Use
Access thousands of tables, each with billions of records, independent of technical capabilities. Simple operations for getting, filtering, sorting, joining, aggregating, and manipulating data. From on-boarding new sources, configuring validation jobs, writing queries, integrating your legacy custom models and code, spinning up new visualizations and widgets, Deephaven is designed to be easy.
Development Platform for Devs and Quants
Use Deephaven to build large-scale simulations, farm algos, process signals, optimize parameters, and employ machine learning. The organics of the platform allow both modular construction using parts from Python, Java, and R, as well as easy migration from development to production.
Application Server
Data is about action, which means automation. All Deephaven customers use the platform’s battle-tested APIs to serve source and derivative data real-time to dependent apps across the organization. Algos built in Deephaven can communicate with OMS infrastructure, minimizing the path from research to alpha.
Ultra High-Performance DB
Finance is all about timestamps. Anyone who has tried using SQL to do market surveillance or develop an algo simulation knows it is the wrong weapon for the fight. Alpha deteriorates. Customers become impatient. Deephaven empowers traders and quants to develop, test, and adjust their own strategies in near-real time. Blazing fast research cycles occur in days instead of months. Quick iteration and evolution is a competitive edge.
Broad Framework
Customers use Deephaven to develop and deliver high-feature order management systems, vol-surface fitters, real-time surveillance systems, risk control scenario analyses, greybox trading platforms, stat arb strategies, slippage and execution quality analytics. The platform serves a range of applications and industries.
Cost-Effective
Missed opportunities are expensive. Bad data is expensive. Slow turnarounds are expensive. Bloated development and DBA teams are expensive. Deephaven customers increase revenues and upgrade productivity. The opposite of expensive.