How Institutions Use algoseek

Getting raw exchange feeds into AWS sounds straightforward until you learn that AWS blocks multicast traffic at the network level. A large hedge fund needed the full equity SIP and OPRA multicast feeds inside their cloud environment, with strict separation between production, testing, and development, and no requirement for algoseek to see anything inside the fund’s AWS infrastructure.

algoseek used Mercury to convert the raw multicast into TCP, pushed it over dedicated dark fiber to algoseek-managed AWS accounts, and made the feeds available via private link. The fund picks up any channel from A or B feeds, sourced from New Jersey or Chicago, in US-East-1 or US-East-2. Everything is fully redundant across both locations.

The hardest part of this project was not the concept but the execution. AWS networking at this scale behaves differently from what the documentation describes, particularly around jumbo frame handling between VPCs. algoseek had already solved those problems, which is why the infrastructure handles volume spikes and rapid market data growth without latency degradation.

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For a proprietary trading firm, every hour spent on infrastructure is an hour not spent on alpha. This firm was managing its own hardware in Equinix NY5, handling exchange connectivity paperwork, troubleshooting networking issues, and maintaining its own feed handlers. The engineering team that should have been building strategies was running an infrastructure operation instead.

algoseek took over the full stack: colocation, networking hardware, direct exchange connectivity, Mercury for real-time feed normalization, monitoring, and escalation support. The firm’s strategies now run on the same Mercury ticker plant that processes data for US regulators and bulge bracket banks. When exchange specifications change or volume spikes hit, algoseek handles it. The firm’s engineers don’t get paged.

Historical data for backtesting runs on the same schema and normalization as the live feed. Strategies that pass validation on historical data behave identically in production because the data underneath never changes shape between environments.

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For a proprietary trading firm, every hour spent on infrastructure is an hour not spent on alpha. This firm was managing its own hardware in Equinix NY5, handling exchange connectivity paperwork, troubleshooting networking issues, and maintaining its own feed handlers. The engineering team that should have been building strategies was running an infrastructure operation instead.

algoseek took over the full stack: colocation, networking hardware, direct exchange connectivity, Mercury for real-time feed normalization, monitoring, and escalation support. The firm’s strategies now run on the same Mercury ticker plant that processes data for US regulators and bulge bracket banks. When exchange specifications change or volume spikes hit, algoseek handles it. The firm’s engineers don’t get paged.

Historical data for backtesting runs on the same schema and normalization as the live feed. Strategies that pass validation on historical data behave identically in production because the data underneath never changes shape between environments.

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The most common way strategies break in production is that the research data and the production data don’t match. Different vendors, different normalization, different schemas. A signal that worked in backtesting fails live because the data underneath changed.

algoseek eliminates this problem. A quant starts on the Console exploring historical data, validates a signal against Extended Minute Bars with up to 90 quantitative fields per bar, runs out-of-sample tests against the full archive via ArdaDB, paper trades against the same normalized feed, and goes live on the same data. Same schema, same normalization, same security masters at every stage. The architecture scales from a single researcher to a full production operation without re-engineering the pipeline.

Firms that start on the Console with the free historical data sample regularly grow into full package subscriptions as their strategies move through the lifecycle.

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Fintechs that want to include market data in their product offering face a build-or-buy decision with hidden complexity. Exchange licensing is arcane, data normalization is ongoing work, and infrastructure reliability directly affects their customers’ experience. Building all of that from scratch means running a data business inside a fintech business.

algoseek’s Data Supplier Solutions programme handles the upstream entirely. The fintech receives normalized data via RESTful API and redistributes it under its own brand. algoseek manages the exchange licensing, data quality, pipeline infrastructure, and first-level support. The fintech’s customers consume the data without knowing the underlying source.

This lets fintechs ship data products to their customers without building data infrastructure, managing exchange relationships, or staffing a data operations team.

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