Spikeet
A financial market data application built end to end, on top of an existing database covering 25 years of US stock data.
- 25 yrsof US stock data
- GBsscanned per screen
- No-codefor non-technical traders
The engagement
Spikeet came to us with a substantial asset already in place: a database of 25 years of historical US stock data. What they needed was the application on top of it — the layer that would let traders actually use that data. We built that application end to end during a 2021–2022 engagement.
What we built
The core engineering problem was market-wide scanning: running complex, multi-condition filters and calculations across gigabytes of historical data, and returning results a trader could act on.
- Market-wide scanning across the full 25-year dataset, not a sampled subset.
- Multi-condition filters with calculations evaluated across gigabytes per scan.
- A no-code interface, so traders could build and run screens without writing a line of SQL.
That last point defined the product. The audience was traders, not engineers — the power of the dataset only mattered if someone without a query language could reach it.
Scope, precisely
We built the application; the underlying database pre-existed our engagement. Our involvement ran through 2021–2022 and has since concluded, so this page describes the system as we delivered it — not the product as it stands today.
- ClientSpikeet
- EngagementApplication build, end to end
- Period2021–2022 (concluded)
- DomainFinancial market data
- DatabasePre-existing, 25 years of US stock data
Sitting on data your users can't reach?
The application layer is what turns a dataset into a product.