How professionals use algoseek market data

An independent quant backtesting a systematic strategy hits the wall every solo researcher hits. The prices are the straightforward part; everything around them is not. Splits and dividends applied wrongly, index membership taken as it stands today rather than as it stood then, a security master that quietly merges two tickers into one history. The backtest runs clean and the result is fiction.

algoseek ships the reference layer alongside the prices: point-in-time index components, adjustment factors recalculated nightly, and a security master that holds identifiers stable across renames, splits and delistings. It is the same reference data the regulator and bank work runs on.

The quant validates against the archive in the Sandbox before paying for anything, then licenses only the datasets the strategy actually needs. One person, working from the same foundation as a desk of twenty.

Not every firm needs infrastructure built for it. A quant team validating signals needs the data itself, in the environment it already works in, without commissioning a project to get it there. Most start with historical data, well before anything goes live.

algoseek delivers a standard feed over the internet into their cloud, or cross-connects it directly into their own infrastructure. No custom build and no bespoke calculation, on the same normalized schemas and security masters that sit behind every other engagement algoseek runs.

As strategies firm up, the same feed moves with them: historical first, then delayed, then real time. The schema does not change, so nothing has to be rebuilt when the data starts moving.

A prop trading team runs equity strategies out of New Jersey, with futures alongside them. It needs raw trades and quotes data across both, with a centre of gravity in New Jersey as it is primarily focused on equity trading.

The firm subscribes to algoseek’s feeds with a hybrid solution: real-time data co-located in Equinix New Jersey, beside the Mercury ticker plant, and market data also sent up to their AWS cloud infrastructure.

The firm’s strategies now receive market data from 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.

Getting raw exchange feeds into AWS sounds straightforward until you learn that AWS blocks multicast traffic at the network level. A large hedge fund needs the full equity SIP and OPRA multicast feeds inside its 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 encapsulates the raw multicast into TCP, pushes it over dedicated dark fiber to algoseek-managed AWS accounts, and makes the feeds available via AWS PrivateLink. The fund picks up any channel from A or B feeds, sourced from Equinix New Jersey or Chicago, in us-east-1 or us-east-2. Everything is fully redundant across both locations.

The hardest part is 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 has already solved those problems, which is why the infrastructure handles volume spikes and rapid market data growth without latency degradation.

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.

Index pricing at a bulge bracket bank is not a research exercise. The bank’s global index structuring team needs custom one-minute TWAP bars for US equities, computed in real time and historically, to feed the pricing of indexes that a large segment of the US capital markets depends on daily. Both the bank’s internal team and the third-party Calculation Agents who publish official index prices consume this data.

The constraints are absolute: latency SLAs that cannot slip, concurrent access for the bank and every Calculation Agent at scale, and zero tolerance for downtime. algoseek builds the feed handler, computes the full historical archive, and deploys a regionally redundant delivery layer backed by GCS storage. Calculation Agents pull any contract or ticker over HTTP in parallel, thousands at a time, without standing up dedicated infrastructure on their side.

When a bank moves its index calculations from end-of-day to intraday, the infrastructure algoseek builds becomes part of the pricing chain for some of the most widely referenced indexes in the US.

Standard NBBO calculations exist for a reason, but a US regulator needs something different: a custom NBBO derived from the full OPRA options feed, built to the regulator’s own specification and delivered to the cloud without compromising on latency. The engineering challenge is that OPRA generates roughly 30 terabytes of uncompressed data per day, and the calculation has to stay accurate when volume spikes to five times normal levels.

algoseek pairs Mercury’s ticker plant and builds a real-time compute grid with regionally redundant infrastructure. Four independent feeds from the raw OPRA multicast are arbitraged against each other so that no message is ever lost, even under the heaviest load the market has produced. The system has never missed an SLA.

This infrastructure now underpins a critical function in US market regulation. The engineering standard behind it is the same one algoseek applies to every client engagement.