Day 62 — Market Friction Modeling: Complete ExecutionDelaySimulator Workspace

This workspace delivers a production-grade ExecutionDelaySimulator that measures real execution latency in Decimal, tracks P95/P99 percentiles without float contamination, and integrates directly with SlippageMonitor from Day 33 via a typed friction_report() contract. It makes the gap between backtest fill assumptions and live execution visible and alertable.

---

What You’re Building

ExecutionDelaySimulator — the measurement and simulation layer for network-induced execution delay. It sits between SignalCombiner (Day 3–5) and OrderBuilder (Day 31), intercepting the signal price and fill price to compute latency_ms and slippage_bps as exact Decimal values.

Supporting types: DelayRecord (frozen dataclass — immutable audit record), LatencyStats (Decimal accumulator for percentile tracking), DelayStatus (enum: PENDING / MEASURING / RECORDED / ANOMALY), DelayAnomalyError (exception with attached record for alerting pipelines).

This workspace extends the friction surface started in Day 33 (SlippageMonitor) and feeds directly into the reconciliation pass from Day 34 (FillAuditLog).

---

Complete Implementation

See day62/src/execution_delay.py in the GitHub workspace. Key design decisions:

capture_time_ms() uses Decimal(str(time.time() * 1000)) — never Decimal(time.time() * 1000). The str() path captures display precision; the direct path captures the full IEEE 754 binary encoding including sub-microsecond noise.

LatencyStats.record() rejects float input with a TypeError. This is a hard boundary. If you’re passing float latencies into stats, you have a bug upstream — the error should surface there, not silently contaminate your P95.

friction_report() returns dict[str, Decimal | int] — all values typed, no floats. Designed to drop directly into SlippageMonitor.ingest_delay_report() without transformation.