feat: Implement latency profiling and guardrails for performance monitoring

- Added synthetic latency profiler scenarios and CLI scripts for baseline generation and regression checks.
- Introduced latency baseline and threshold artifacts for CI enforcement.
- Enhanced CI workflow with latency guardrail checks.
- Updated documentation to include latency profiling commands and performance metrics.
- Added unit tests for latency guardrail evaluation.

README.md

@@ -210,6 +210,18 @@ Run secret scan (worktree + git history):
 python scripts/security_scan.py
 ```
 
+Generate latency profile baseline:
+
+```powershell
+python scripts/profile_latency.py --iterations 600 --output ops/performance/latency_baseline.json
+```
+
+Run latency regression guardrails:
+
+```powershell
+python scripts/check_latency_regression.py --baseline ops/performance/latency_baseline.json --thresholds ops/performance/latency_thresholds.json --iterations 600
+```
+
 Install pre-commit hooks:
 
 ```powershell
@@ -338,3 +350,28 @@ Hardening checklist:
 - Run `pip-audit --skip-editable` in CI; treat vulnerability findings as release blockers.
 - Run `python scripts/security_scan.py` before release and after major merges.
 - Store secrets in environment/secret manager; never commit `.env` or key material.
+
+## Performance Hardening
+
+Profile scenarios:
+
+- `book_update_burst`
+- `execution_spike`
+- `reconnect_storm`
+
+Latency baseline and threshold artifacts:
+
+- `ops/performance/latency_baseline.json`
+- `ops/performance/latency_thresholds.json`
+
+CI guardrail:
+
+- `.gitea/workflows/ci.yml` runs `scripts/check_latency_regression.py` and fails on regression.
+
+Measured optimization impact (2026-06-01):
+
+- `MetricsCalculator.compute()` switched from Python row scans to DuckDB SQL aggregates/quantiles.
+- Benchmark (`scripts/benchmark_metrics_compute.py`):
+  - Python scan avg: `12.623 ms`
+  - SQL aggregate avg: `11.039 ms`
+  - Speedup: `1.14x`