Client profile
Mid-sized digital asset trading firm scaling from discretionary decision loops to systematic, production-grade execution.
The problem
The firm’s core challenge wasn’t research quality — their strategies were sound. The problem was that research velocity had outpaced production discipline. Strategies were being promoted to live trading based on informal agreement rather than documented criteria. Rollback decisions, when they happened, were reactive. Post-trade attribution was manual and inconsistent, making it difficult to distinguish genuine edge deterioration from execution noise. Running 24/7 in volatile markets without reliable incident protocols was compounding operational risk at the same rate as trading risk.
The hardest part of this kind of engagement is that the gaps are invisible until something goes wrong. The systems appear to be working. The real issue is that no one knows why they’re working — or what exactly breaks when they don’t.
Scope
- Research-to-production strategy pipeline with deterministic promotion gates
- Post-trade analytics and attribution stack for desk-level decision support
- 24/7 operational reliability model for execution and reconciliation systems
Approach
Embedded directly with research, engineering, and desk leadership to understand the full decision chain — from how strategies were developed to how they were monitored in production.
The promotion criteria were defined from first principles: data quality standards, latency budgets by strategy type, and risk constraints calibrated to the desk’s capital allocation model. This required understanding both the statistical properties of the strategies and the operational realities of running them continuously across market regimes.
Attribution and slippage decomposition were built to answer the specific questions the desk was asking — not generic trading analytics. Drawdown diagnostics were designed around the specific failure modes relevant to the asset class and execution style.
Incident runbooks and alert thresholds were designed with market context in mind: what matters in a market-hours incident is different from what matters at 3am during low-liquidity hours.
Results
- Improved risk-adjusted performance consistency over the monitored deployment window
- Faster promotion cycles with fewer rollback events
- Higher confidence in intraday risk posture and execution quality