Add demand_normalization.py: norm = clamp(exp(beta·(rate_future −
rate_window_avg)), 0.5, 1.2). Discounts a sales pace observed under one rate
regime when projecting into a different (higher-rate) future — ТЗ §9.4: don't
carry a low-rate boom pace forward into high rates.
- beta reused from §9.6 rate_sensitivity (PR3, shrunk slope on Δln, gated <0);
rate_window_avg = mean key_rate over the §9.6 window (PR2 macro_series).
- Honesty gate: β None / §9.6 confidence='low' / no rate window → norm=1.0,
applied=False, confidence='low' (no naive boom, no fabricated discount).
applied=False is distinct from a trustworthy coefficient==1.0 (regimes match).
confidence never exceeds §9.6's.
- Pure normalization_factor() + frozen DemandNormalization + as_dict().
- 26 unit tests, no live DB (mocked PR2/PR3). ADVISORY: not wired into endpoints.
Completes the deterministic §9.4/§9.5/§9.6 forecasting engine (PR2/PR1/PR3/PR4/PR5).
PR6 follow-up: OverflowError guard on exported normalization_factor (unreachable
via orchestrator today, β bounded).
Light1YT
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