Most algorithmic edge decay isn't caused by alpha signal degradation—it's caused by infrastructure latency and execution

The silent killer of quantitative strategy performance is not alpha decay. It is the invisible rot of infrastructure latency and execution pathway inefficiencies compounding beneath the surface of otherwise sound signal research. Most firms are confidently diagnosing market regime change as the cause of their P&L erosion while the real culprit lives in their own engineering stack, unexamined and unaddressed.

I have seen this pattern repeat with striking consistency. Strategies showing 15 to 30 percent apparent alpha degradation over 18 months were restored to within 90 percent of original performance after infrastructure overhaul alone. Zero signal model changes. The regime change diagnosis was not merely imprecise. It was categorically wrong.

The prevailing belief in quantitative finance is that edge decay is primarily a function of signal crowding, alpha signal degradation, and shifting market microstructure regimes. This view is reinforced by academic literature, sell-side research, and the gravitational pull of most firms' R&D budgets toward signal discovery. It persists because signal research is intellectually legible, culturally prestigious, and easy to resource. Infrastructure inefficiency is diffuse, unglamorous, and notoriously difficult to attribute to specific P&L outcomes.

The numbers tell the story plainly. Roughly 70 to 80 percent of quant fund R&D budgets flow to signal and model research. Less than 10 percent goes to execution infrastructure optimization. Look at hiring patterns. Look at conference topic distributions at QuantCon, Trading Show, Battle of the Quants. The industry's revealed preference is overwhelming, and it is wrong.

This common view is dangerously incomplete because it treats the execution layer as a static constant. It is not. It is a dynamic, degrading system. Network paths shift. Co-location neighbors change load profiles. Serialization overhead creeps in through library updates. Order routing logic accumulates conditional branches that add microseconds per decision. None of these changes announce themselves. They accrete like plaque in an artery.

Firms running attribution models that isolate signal alpha from execution cost systematically undercount execution drag. The reason is structural. They benchmark against a static Transaction Cost Analysis model rather than measuring the compounding drift of their own infrastructure performance over time. Standard TCA asks: "What did this trade cost relative to a benchmark?" It does not ask: "Is our infrastructure performing the same way it performed six months ago on identical order types?" These are fundamentally different questions, and the second one almost never gets asked.

Consider the mechanism concretely. A single additional microsecond of jitter per order across 500,000 daily order events compounds into measurable queue position loss. Queue position loss drives adverse selection increase. Adverse selection increase drives fill rate degradation. Model this through and you find 3 to 7 basis points of annualized drag. But here is where the misattribution becomes systematic: conventional TCA frameworks categorize this drag as market impact rather than infrastructure decay. The engineering problem gets laundered into a market narrative. The PM sees "increased market impact costs" in the attribution report and concludes the signal is crowded. The real cause never surfaces.

This is not speculation. When firms conduct rigorous infrastructure audits measuring not just median latency but full distribution tail latency, serialization overhead drift, switch fabric congestion patterns, and order gateway pathway efficiency over rolling six month windows, they consistently discover 20 to 40 percent more execution drag than their TCA reports attribute to infrastructure.

One case makes the point sharply. A mid frequency equity stat arb desk discovered that a firmware update to their network switches 14 months prior had introduced an asymmetric latency profile on cancel replace messages specifically. Eight microseconds on average. This single issue was responsible for an estimated 4.2 basis points of annualized performance drag. Their PM had attributed it entirely to crowding in momentum reversion signals. Fourteen months of misdiagnosis. Fourteen months of the research team chasing signal modifications to solve an engineering problem.

Sophisticated practitioners treat infrastructure as a living alpha source rather than a fixed cost center. They run continuous latency regression testing against their own historical baselines. They maintain execution pathway auditing as a formal process with the same rigor as signal validation. They staff dedicated infrastructure alpha teams whose KPIs tie directly to P&L recovery, not uptime metrics. Firms like Optiver, Tower Research, and Jump Trading maintain infrastructure performance as a board level metric and run A/B execution pathway experiments continuously. Contrast this with the typical asset manager approach: outsource execution infrastructure to a prime broker's commodity stack, then spend millions trying to figure out why backtested alpha does not survive contact with live markets.

The actionable framework is straightforward. Institute a quarterly infrastructure alpha review that independently measures execution pathway efficiency drift and cross references it against strategy performance attribution. Make this review a prerequisite before any signal model changes are approved. If you cannot demonstrate that your infrastructure has not degraded, you have no basis for concluding that your signals have.

If you have not conducted a full stack infrastructure latency audit in the last six months while simultaneously greenlighting budget for new signal research, you are making an implicit bet that your execution layer has not degraded. You have not priced that bet. You cannot defend it.

Here is the diagnostic I would pose to anyone reading this: when was the last time your firm measured tail latency distribution drift on your execution pathway over a trailing 12 month window and compared it against strategy Sharpe ratio decay curves? I would predict that fewer than 5 percent of firms have ever performed that specific cross analysis. That gap is not a minor oversight. It is the thesis.