Research Pillar
Constraints before optimisation. This pillar tracks production-grade boundaries: feed hygiene, monitoring, and auditability that prevent drift, override, and silent failure. Integrity is enforced by infrastructure, not intention.
Infra
Program · Feed hygiene & gaps
Handling incomplete feeds, errors, corporate actions, exchange changes, and structural gaps without silent leakage. Focus: cut-offs, snapshotting, and reproducibility under real conditions.
Monitoring
Program · Behaviour drift alerts
Early-warning systems for slippage, latency, broken assumptions, and behavioural drift. Focus: what we monitor, what triggers alarms, and how alerts map to execution permission.
Audit
Program · Explain-every-trade
Making sure every trade can be reconstructed after the fact: inputs, regime state, gates, risk permission, and execution conditions. Focus: traceability over narratives.
How Latency and Queue Risk Reshape Execution Reality Latency and queue risk rarely appear in performance reports. Nevertheless, they shape every execution outcome the engine produces. By the time slippage shows up in the post-trade record, the underlying conditions that caused it have already moved on. This article examines latency and queue risk as structural...
How Slippage Attribution Reveals What Actually Changed Slippage attribution describes how a systematic engine separates the total cost of a fill into its underlying causes. A single slippage number says the trade cost more than expected. Slippage attribution, by contrast, says why. Furthermore, it tells the engine whether the cause was structural, behavioural, or accidental....
Why Monitoring Pipeline Behaviour Drift Detection Comes Before System Failure Systematic trading infrastructure relies on one assumption above all others: the engine will behave the same tomorrow as it does today. However, behaviour does not hold constant by itself. Monitoring pipeline behaviour drift detection exists precisely because engines change in subtle ways before they fail...
Data Trust: Preventing Future Leakage in Systematic Trading Most discussions about data leakage focus on detection. Teams build logs, audits, validations, reconciliations—mechanisms designed to discover errors after they occur. However, data trust systematic trading approaches this differently. In live systems, data trust represents an architectural boundary problem rather than a monitoring challenge that teams must engineer upstream...