LAB | Quant & Trading | ongoing
Quant R&D Platform & Rust-backed Services
Research-to-production trading workflows with performance and risk constraints as first-class features.
RustPythonBacktestingTime-seriesRisk constraints
- Backtesting and signal evaluation discipline designed to resist overfitting.
- Performance-oriented services where latency and throughput matter.
- Architecture that keeps audit trails and constraints explicit.
What we build in the lab
- Data ingestion, feature pipelines, and repeatable research harnesses.
- Backtesting engines with constraint-aware simulation primitives.
- Rust services where performance or safety is non-negotiable.
Operating principles
- Measure robustness, not just returns.
- Design for observability (trace every decision path).
- Prefer composable primitives over giant one-off systems.
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