What we set out to build
To stop evaluating market-making performance by watching it, and start evaluating it from the fill data it produces.
What was built
- Automated ingestion and analysis of strategy runs and fill logs
- Inventory, spread and adverse-selection breakdowns per run
- Comparison across configurations rather than one run at a time
What it achieves
- Feeds the same maker-economics work that produced the studio's first measured positive edge: adverse selection analysed across 1.8 million real fills
- Turned maker viability into an asset-selection question with a measurable answer, rather than a general belief that market making works
The stack
Python, Hummingbot, pandas.
Common questions
How was this built?
This was built through Colabs, our sister company. A founder brought the idea and the domain knowledge, Colabs brought the team, the founder funded the build with a monthly subscription, and they hold equity in the company that resulted. We label every case study, because a studio that publishes its prices and argues the other side of its own comparisons has to be equally precise about its own portfolio.
What state is it in?
Internal tooling. We state that plainly rather than describing everything as production: several things here are deliberately gated, and the reason is usually evidence rather than a missing feature.
Can you build something like this for us?
That is the point of publishing it. The same patterns are in the catalogue at published prices, and you can scope your own version on the plain-English page without contacting us first.
Where to next
If something here is close to what you need, the same patterns are in the catalogue at published prices, or you can describe your version in plain English and get it scoped and priced without talking to anyone.