Upgrade the §25.3 cannibalization index from a same-class-competitor proxy to
true own-portfolio overlap: score the recommended candidate segment against the
developer's own portfolio (get_own_portfolio, #1169 PR1) across axes —
audience/class (ordinal distance), price ₽/м² (interval overlap), unit-mix
(L1 similarity), timing (half-life decay) — geo-weighted by haversine proximity
to the parcel, aggregated by geo-weighted soft-max (the strongest nearby
cannibalizer dominates, not a diluting mean). Empty portfolio -> labelled proxy
fallback, confidence forced low, never presented as the true index.
Pure scoring fns unit-tested without DB; None-not-0 on missing axes; thin/
only-current portfolio -> low confidence + §26 note; deterministic (sorted
tie-breaks, no RNG). class+price+geo active now; unit-mix+timing plumbed via
optional params for a follow-up that wires them from the orchestrator horizon.
ruff + mypy clean; 151 special-index tests pass (964 forecasting dir, no regr).
Refs #1169
fedstat ИПЦ is reCAPTCHA-blocked; CBR publishes inflation openly. Add
fetch_inflation + parse_inflation_xlsx (CBR UniDbQuery DownloadExcel/132934,
monthly % г/г, region=rf, source=cbr) to cbr_macro.py; upsert
indicator_type=inflation_yoy via the existing cbr_macro_sync task (per-series
guard, SAVEPOINT-per-row, CAST not ::, ON CONFLICT on the PK).
Surface inflation_yoy in MonthlyMacro (frozen, carry-forward) and ACTIVATE the
reserved §9.5 inflation channel (macro_coefficient f_inflation: level-vs-4%-target
nudge, non-positive to avoid double-counting f_rate, excluded from
_RATE_DRIVEN_FACTORS). Channel was DEGRADED (no data) -> now BACKED + consumed;
_CONF_HIGH_MIN_BACKED 4->5. Deterministic (§16/§26); renorm claims the reserved
0.08 slice as designed. Live-verified (2026-04 5.58%); 194 macro + 902 forecasting
tests green. No migration, no new deps.
Refs #946.
Cold §22 forecast measured ~215-233s on prod: §9.x layers re-execute the same
horizon/segment-invariant DB loads with identical args hundreds of times per
report (profiled: get_competitors x69, market_metrics x124, get_monthly_macro
x290). Add a per-report ContextVar cache (forecast_cache(), opened once in the
orchestrator) + @cached(key_builder) on the expensive §9.x loaders so each
unique load runs ONCE and reuses the same frozen, read-only instance.
Output is byte-identical (memoized producers are frozen dataclasses / read-only
Pydantic, callers never mutate; cache is per-report, discarded on exit; no-op
outside the report build). No concurrency, no signature changes.
- forecast_request_cache.py: ContextVar cache + cached() decorator (no-op
outside context, reentrant, _MISS sentinel for cached None)
- @cached on competitors/future_supply/market_metrics/macro_series/
sales_series/macro_coefficient/demand_normalization/regression loaders
- orchestrator: wrap build_site_finder_report in forecast_cache()
- 58 tests: key discrimination (call-counting regression guard), no-op-outside,
per-report isolation, reentrancy, frozen-producer canary, amplification proof
(real get_monthly_macro xN->1)
code-reviewer APPROVE (keys correct, mutation-safe, output identical). 1265
forecast/cache tests green. No new deps. Refs #1129.
deficit_index pins to -1.0 for every ЕКБ segment (12mo demand flow vs multi-year
supply stock → log-ratio clamps) → zero discriminating power, though the oversupply
is partly real. Add MOI (gross competing supply / demand_per_mo), the real-estate
absorption standard, as an additive non-saturating companion that DISCRIMINATES
(Уралмаш 42mo … Чермет 109mo) where deficit cannot. deficit_index math kept exactly
as-is (honest absolute: -1 = genuinely oversupplied); docstrings clarify -1 is common
and MOI is the discriminating companion (no recalibration). _gross_supply extract-method
(single source of truth; _project_supply behavior byte-identical, code-review-verified).
Surface MOI in §22 future_market (passthrough) + exec_summary key_numbers/verdict.
Guards: no demand → None, no supply → 0. Prod: MOI varies 42→109mo, deficit stays -1.
Discrimination test pins MOI separating two segments both at deficit -1. Refs #952.
/analyze passes the official ЕКБ admin district (ekb_districts polygon, e.g.
'Кировский'), but objective_lots/corpus_room_month store informal micro-districts
('Втузгородок','ЖБИ') -> admin name matched 0 rows -> silent empty forecast.
Add resolve_objective_districts() (site_finder/district_resolver.py) mapping an
admin name to its clean micros via ekb_district_alias (note IS NULL), with
None -> EKB-wide fallback and raw-micro pass-through. Wire into the objective_lots
district filters of market_metrics (§9.2 stock+sales), supply_layers L1 (§9.3),
and sales_series Sources A+B (crm shares the micro vocab, prod-verified),
switching the scalar filter to psycopg3-safe = ANY(CAST(:districts AS text[])).
supply_layers L2/L3 keep the admin name (domrf_kn_objects.district_name is admin vocab).
Prod: Кировский/Ленинский/Орджоникидзевский obj_count 0 -> 32/64/31.
Tests mutation-verified non-vacuous. 192 module tests pass; ruff clean. Refs #969#949.
_KEY_RATE_MARKET_SPREAD_PP was a 0.0 placeholder, so §7.9 affordability payments
used the bare CBR key_rate (~14.5%), understating borrower cost and OVERSTATING
affordability. Calibrate to 4.5pp from the prod anchor (macro_indicator
mortgage_rate_primary_domrf 19.125% @ 2026-04-19 vs key_rate ~14.5% -> observed
~4.6pp, rounded conservatively; inside the typical RF 3-5pp band), so
rate_used = key_rate + 4.5 ~= 19% matches the directly-observed market primary
mortgage rate. Makes affordability LESS optimistic / more accurate. Docs + tests
updated to the symbolic spread (new regression anchor pins spread==4.5 and
key_rate 14.5 -> ~19.0); rate_kind/graceful-fallback semantics unchanged.
Forecasting suite 841 passed; ruff clean.
Backtest (OOS directional hit-rate): single-best-lag compute_rate_sensitivity
is directionally noise (0.148 Source B EKB-wide, lag-unstable); the Almon
distributed-lag estimator (compute_district_rate_regression) is strictly less
noisy on every tier (0.407 Source B / 0.60 survivorship-free Source A,
lag-stable). Add a thin adapter compute_rate_regime_sensitivity mapping
DistributedLagFit onto the existing RateSensitivity contract (beta=long-run
sum-beta, confidence regression->medium / fallback->low, district=None->low and
no call) and repoint the three consumers (demand_normalization, product_scoring,
demand_supply_forecast). Magnitude bounded by the existing [0.5,1.2] clamp.
Reversible; compute_rate_sensitivity kept for the backtest. Consumer tests
repointed to the real Almon path (mutation-verified genuine) + adapter unit
tests + end-to-end fallback degradation. Forecasting suite 840 passed; ruff clean.
Found by read-only services audit.
- recommendation._usp_from_deficits: skip di<=0 so «стройте его» is never emitted
for OVERSUPPLIED formats; all-surplus top-K → [] (no white-space niches).
Aligns with product_scoring._count_positive_usp (di>0). Was: «Дефицит формата
X — стройте его» for a surplus format, reaching PDF/Excel USP-ниши.
- report_assembler._domrf_coverage: drop ambiguous >1.0 percent-guess; normalize
per-branch (analyze pct /100, supply_layers fraction as-is). Sub-1% coverage
(0.8%) no longer read as 80% → no inflated confidence in the near-zero-coverage
case §15 flags. tests for both + end-to-end no-inflation. 241+148 pass.
REOPENED 951-B §9.6.
PART A: fix look-ahead leakage in backtest_rate_sensitivity --detrend. The
ln(units) trend was fit over train+test then split, so test data shaped the
detrend and inflated the OOS hit-rate. _detrend_log now takes fit_n; backtest_tier
fits the trend on TRAIN months only (same split evaluate_oos uses) and projects
(a,b) point-in-time onto test. Default fit_n=None preserves prior behaviour.
PART B (DoD): new app/services/forecasting/regression.py — Almon polynomial
distributed-lag (deg 2) of Δln(district demand) on Δkey_rate lags 0..6 via
OLS-on-Almon-regressors (numpy lstsq) + per-lag reconstruction + manual
Newey-West HAC SEs (NO statsmodels). Output {best_lag_months, coef=long-run
multiplier, x_pct, r2, n, per_lag_coef, hac_se,...}; gate mirrors _elasticity_coef
(n<30 OR R²<0.1 OR Σβ≥0 → fallback); §9.6 phrase from the lag shape. ADVISORY,
shipped standalone (integration point documented), NOT wired — protects the live
compute_rate_sensitivity consumers.
125+31 tests (synthetic known-lag recovery, HAC computed/differs-from-OLS,
fallback gating, no-leakage detrend). ruff clean. Refs #978
REOPENED — normalize.py was never created; only rate-regime discount existed.
New backend/app/services/forecasting/normalize.py with normalize_demand(series):
multiplicative month-of-year deseasonalization of the raw monthly demand
SalesSeries (§9.4). Pure/deterministic; min-data guard (<2 full years / empty
month / overall_mean<=0 → factor 1.0, no divide-by-zero, no thin-data noise).
Exposes seasonal factors for explainability. Synthetic unit test: seasonality
removed (month means equalised), flat unchanged, thin/empty/all-zero safe.
DoD (module + doc + test) MET. Production wiring into
rate_sensitivity._align_sales_deltas DEFERRED (documented TODO): deseasonalizing
the short rate-driven series perturbs the recovered β/lag on current data —
needs a points-per-month gate / joint seasonal+rate estimation + backtest before
wiring. Forecast stack is advisory regardless. Refs #979
REOPENED#980: when effective competing supply is exhausted under positive demand
(projected_supply<=0, demand>0), deficit_index now caps to +1.0 (peak of [-1,+1])
instead of None. balance_ratio stays None (demand/0 undefined), but the strongest
build signal no longer reads downstream as thin data (market_fit fell to 0.5,
what_to_build dropped the cell). No-signal (supply<=0 AND demand<=0) stays None.
REOPENED#981: MAI now uses CBR key rate (macro_indicator key_rate/rf via
get_monthly_macro) as the market borrowing-cost proxy (~16-21%) instead of the
subsidized weighted rate (~7.83%), per §7.9 DoD. rate_kind='key_rate_proxy'.
If key_rate absent → rate_kind='market_unavailable' (no silent subsidy fallback).
Income (#946) still missing → payment_to_income None, confidence low.
778 forecasting tests green. Refs #980#981
