At Iteralabs we believe in one core principle: To achieve consequential engineering results, science goes before hype.
“Most people use statistics like a drunk man uses a lamppost; more for support than illumination” ― Andrew Lang
And thus, we focus on statistical soundness and parametric stability for the models we use, with this hierarchical sourcing of knowledge: statistical learning > machine > large heuristics learning (Generative AI, which we could use, even daily, but as an optional tool not as a protagonistically, for-its-own-sake goal).
- Classical ML OnChain Computation.
- DeFi Market Making, Order Routing and Risk Modeling.
- Synthetic Data Generation (OffChain, and, OnChain).
- Classical ML and Quantitative Finance.
- Distributed Convex Optimization Models.
- Financial timeseries inner-pattern recognition (subsequential clustering).
- atelier-sdk : SDK For the Rust Engine for High Frequency, Synthetic and Historical, Market Microstructure Modeling.
