One layer's caught DB error left the shared §22 report Session in Postgres's
"aborted transaction" state with no rollback, silently emptying every later
layer on the same report. Wrap each failure point in db.begin_nested()
(SAVEPOINT): orchestrator._safe_call, macro_series _query_key_rate_monthly /
_query_inflation_monthly / _query_mortgage_monthly (per indicator),
special_indices._run (per index), and sales_series _query_source_a /
_query_source_b — so a sibling builder's failure no longer cascades.
Also close the RELEASE-SAVEPOINT trap: inner helpers that swallowed a
db.execute failure without their own savepoint left the tx aborted, so the
outer builder's savepoint failed at RELEASE (illegal in an aborted tx) and
poisoning still cascaded. Add savepoints at the inner catch sites —
special_indices._query_parcel_centroid and district_resolver._admin_names /
resolve_objective_districts.
Refs #2464 (cluster A session-poisoning).
Корень «−1.00 везде» (эпик #1953): compute_demand_supply_forecast брал
district-wide unit_velocity (847.5/мес, ВСЕ классы/комнаты) как спрос и
весь district-сток (~63k доступных) как предложение для КАЖДОЙ ячейки
what_to_build → один и тот же ratio во всех ячейках → все deficit_index
прижаты к −1.0. Плюс objective_lots — append-per-snapshot (~2.9× инфляция
строк), что симметрично раздувало обе базы → даже сегментация без дедупа
осталась бы вырожденной.
Фикс (blast radius — ТОЛЬКО forecast/deficit calc; platform-wide dedup = #1964):
- market_metrics.compute_market_metrics: +obj_class/+room_bucket (+cache key).
_STOCK_SQL и _SALES_WINDOW_SQL дедуплят до ПОСЛЕДНЕГО снапшота на физлот
(DISTINCT ON project_name,corpus_name,section,floor,lot_number ORDER BY …
snapshot_date DESC,id DESC), затем агрегируют. Class-фильтр (LOWER=LOWER,
class lowercase) + room-bucket (Source-B room_area-вокабуляр, зеркало
sales_series.room_area_bucket_of → what_to_build фильтрует без перевода).
ROLLUP/GROUPING сохранён; confidence считается на дедуплицированных counts.
- demand_supply_forecast: base_pace и open-сток теперь ПОСЕГМЕНТНЫЕ
(market_metrics(obj_class,room_bucket)). При заданном сегменте L2/L3
(hidden/future) ИСКЛЮЧЕНЫ из баланса — они класс/формат-агностичны, иначе
двоились бы по всем ячейкам. +_market_room_bucket VOCAB-мост (валидирующий
pass-through Source-B меток; неизвестное → None = без фильтра, не тихий 0-rows).
- what_to_build/_DEFAULT_CLASSES и recommendation Economy-маппинг: «эконом»→
«стандарт» (в objective_lots эконома НЕТ, стандарт=483k → раньше ячейка
матчила 0 строк и молча выпадала).
- report_assembler honesty-guard: если ВСЯ сетка прижата к ±1.0
(degenerate-fallback) — не эмитим «строить»/«избегать», показываем
«недостаточно гранулярных данных для посегментного вывода».
- data/sql/173_objective_lots_physflat_idx.sql: partial index под DISTINCT ON
(Index Only Scan + Unique, без Sort на 1.75M строк; idempotent, BEGIN/COMMIT).
Prod-verify (parcel 66:41:0205010:287, Железнодорожный, h=24): ячейки
ДИФФЕРЕНЦИРУЮТ (12 measured, 7 distinct) вместо all −1.0; MOI комфорт/студия
38.5 vs стандарт/студия 244.3 (точное совпадение с ожидаемым).
Тесты: регрессия «ячейки различаются (не all −1.0)» + vocab-translation +
honesty-guard + посегментное предложение. ruff clean; no :name::type.
Replace dev-jargon with plain RU across the §22 forecast report (6.2/6.3/6.5
+ deficit/затоварка legend). Source of truth = backend reason strings + the
frontend RU maps; text/render-only, no scoring/forecast math touched.
Backend (source of truth):
- scenarios.py _COLLAPSE_REASON_LOW_BETA: «β rate-sensitivity не прошёл gate …»
→ «чувствительность к ставке не оценена на коротком ряде ЕКБ → один базовый
сценарий вместо трёх».
- confidence_engine.py _coverage_factor: drop «domrf↔objective» jargon, say it
affects будущее предложение/конкуренцию. New _history_factor: «глубина истории
N мес» + на что влияет + связь с 6.2 (короткий ряд → один сценарий).
Frontend (both Section-6 families — live analysis page + ptica cockpit):
- Deficit legend: −1 затоварка / 0 баланс / +1 острый дефицит + actionable
трактовка; MOI tied to «сколько месяцев район распродаёт предложение».
- 6.2 heading «Почему один сценарий, а не три» over the collapse reason.
- 6.3 render confidence.rationale + weakest-link rule («скорее завысим
недоверие, чем недооценим риск»); FACTOR_RU gains confounded_window/
advisory_cap; factor notes shown.
- 6.5 «Вес»→«Оценка»; overall verdict vs 0.5; «Риск избытка предложения»→
«Запас по предложению»; §-refs moved from reason into tooltip; 6 special-
index 1-line «что это + куда лучше» descriptions; 0.00-score reasons shown.
Tests: confidence_engine (history/coverage notes), stripSectionRefs vitest.
Refs #1963
#1954 — площадь «—»: COALESCE(land_record_area, specified_area, declared_area)
в EGRN-блоке analyze (land_record_area NULL у 12809/42233 участков, но
specified_area заполнена). area_m2 для 66:41:0205010:287 теперь 106378 (был NULL).
#1954 — «Обновлено» сломано: cost_registration_date — мёртвая колонка (0/42233);
репойнт на updated_at (42233/42233 заполнено). Ключ ответа last_egrn_update_date
не меняется (additive value fix).
#1958 — confidence-фактор «Прогноз спрос/предложение» дублировался ×4
(по фактору на горизонт). Сворачиваем в один weakest-link'ом (MIN ранга
по горизонтам) в _component_confidences до confidence-движка (#990).
#1957 — ЗОУИТ backend:
- _get_cad_zouit_overlaps: DISTINCT ON (reg_numb_border) — дедуп дубль-строк
(одна физ. зона 2× с разным category_name). group_key cad_zouit→protected.
- _get_zouit_overlaps (dump path): subcategory→RU-тип карта (26→СЗЗ и др.,
коды сверены кросс-джойном dump↔cad_zouit на проде); type_zone из карты,
reg из props.options.reg_numb_border. Раньше отдавал blank-строки.
- унификация group_key (protected/engineering/okn/natural/other) + top-level
reg_numb_border в обоих путях.
UP038-модернизация isinstance в report_assembler (pre-commit ruff 0.7.4).
Frontend note (#1957): nspd_zouit_overlaps теперь всегда group_key из набора
{protected,engineering,okn,natural,other} — сырой 'cad_zouit' больше не отдаётся;
оба пути несут type_zone + reg_numb_border.
Tests: +4 _component_confidences collapse, +6 ЗОУИТ (subcategory map, dump
typing, DISTINCT ON), schema-test обновлён на protected. 451 passed targeted.
Когда demand_normalization honesty-gate отбивает β rate-sensitivity
(`sensitivity.confidence=='low'`, ЕКБ-регрессия не проходит n≥30/R²≥0.1/slope<0),
все 393 прод-отчёта получают coefficient=1.0 для всех трёх сценариев
conservative/base/aggressive. По дизайну backend честно деградирует, но фронт
рисует 3 разноцветные карточки/линии одинаковых чисел без caveat.
Backend (scenarios.py, report.py, report_assembler.py, orchestrator.py):
- compute_scenarios теперь возвращает (list, collapsed: bool, reason: str|None)
- _detect_collapsed(): math.isclose(rel_tol=1e-9, abs_tol=1e-6) сравнивает
projected_demand_units И deficit_index на всех горизонтах между
conservative и aggressive — расхождение в любой метрике на любом h → False
- _COLLAPSE_REASON_LOW_BETA — единственный источник истины каноничного
русского предложения
- ReportScenarios получает поля scenarios_collapsed + scenarios_collapse_reason
- demand_normalization.py НЕ ТРОНУТ — поведение корректно по контракту
Backend экспортёры (report_pdf.py, excel.py):
- PDF: при collapse — тёмная плашка-параграф вместо таблицы трёх столбцов
- Excel: ОДНА строка «base» + caveat-ячейка вместо трёх идентичных столбцов
Frontend (forecast.ts, ScenariosBlock, ScenarioCards, ScenarioCompareTable,
ForecastChart, Section6Forecast, ptica.module.css):
- Тип ReportScenarios расширен двумя optional-полями
- ScenariosBlock (light): headline-bar + одна grid-карточка base + caveat
- ScenarioCards (ptica dark): sub-component ScenarioBaseCard, одна карточка
+ collapse-note, ScenarioCompareTable return null
- ForecastChart: effectiveScenarios filter — одна линия viz-1 «Базовый»
вместо трёх перекрывающихся
- Section6Forecast: caveat выше графика, читается ТОЛЬКО из
scenarios_collapse_reason (источник истины — backend constant)
Тесты: +18 новых (TestMetricsEqual×6, TestDetectCollapsed×7,
TestComputeScenariosCollapseDetection×4 + 1 проверка graceful + corner).
102 forecasting passed, 88 cross-module passed.
Refs #1871
Add `velocity_by_room: dict[str, float] | None` to `MarketMetrics` — per-bucket
unit velocity (ед./мес) derived from the existing `sold_by_room` ROLLUP data that
`_query_sales_window` already returns. No new SQL required.
Thread per-bucket velocity through `_demand_only_overlay` via the new
`_FORECAST_TO_METRIC_BUCKETS` constant that maps each forecast bucket to its
market_metrics room-bucket keys. "80+ м²" sums "4" + "5+" keys. Fallback to
aggregate `unit_velocity` when `velocity_by_room` is None (thin-data path).
Previously `base_pace` was identical for all 5 room-buckets, so §9.4 norm and §9.2
base_pace cancelled out in pace/max_pace and ranking was driven purely by §9.5
macro_coef (segment steepness proxy). Now §9.2 reflects real per-bucket observed
demand from objective_lots.contract_date data.
Callers of `compute_market_metrics` that don't use `velocity_by_room` are unaffected
(the new field is additive to the frozen dataclass). All existing callers verified —
none construct `MarketMetrics` directly except the one production site.
_GEO_WEIGHT_UNKNOWN was 0.1, which equals exp(−6.9/3)≈0.10 (weight of a
confirmed-far project at ~6.9 km). Projects beyond that distance got a
weight *below* 0.1, meaning unknown-coordinate projects outweighed
confirmed-far ones — an inversion of the documented intent.
Lowered to 0.05 (≈ exp(−3) = exp(−9 km / scale)), restoring the correct
hierarchy: confirmed-close > confirmed-far > unknown. Updated TestGeoWeight
(hardcoded 0.1 expectation) and TestCannibalizationTrueMode (overlap ×
floor comment/value) accordingly. Added two new assertions in TestGeoWeight
that enforce the hierarchy monotonically and verify unknown < exp(−6.9/3).
_count_full_years treated units=0 as a valid observation, so a series
where fill_month_grid zero-filled every month still accumulated 3 full
years and passed the _MIN_FULL_YEARS guard. Zero-filled months carry no
seasonal signal, so they must be skipped in the year counter — the same
way None values already were.
Fix: skip v==0 alongside v is None in _count_full_years.
Add four tests: zero-filled 36-month series → n_full_years=0/applied=False;
partial-coverage years (only 6 non-zero months/year) → not counted as full;
real non-zero series still passes guard; normalize_demand on zero-filled
SalesSeries returns series unchanged.
Add `deal_count_months: int | None = None` to `compute_report_confidence`.
When provided, threads it as suffix into `_factor_from_count` so the
deal_count ConfidenceFactor note reads «7 сделок за 6 мес — мало» instead
of the windowless «7 сделок — мало». Existing callers unaffected (default None).
Tests: two new cases in TestComputeReportConfidence — with/without window.
Внутренний recommendation→product_scoring контракт-ключ был мислейблом: величина —
темп распродажи нежилого (sell-through, прокси ликвидности/спроса), а НЕ доля нежилого
в объёме застройки. Переименован ключ + исправлены reason/docstring/комментарии у
потребителя _score_commercial. Числовая логика не изменена. Ключ внутренний (нет
frontend/schema/openapi-потребителей) → rename контракт-безопасен. pytest 171 passed.
Closes#1635
API отвергал ?horizon=24 (422), хотя ТЗ §12.1 называет 6/12/18/24, а движок
УЖЕ считает 24 на каждом ране: _DEFAULT_HORIZONS=(6,12,18,24) во всех 6 точках
стека (orchestrator/forecast-task/demand_supply_forecast/scenarios/
special_indices/report_assembler), PIPELINE_HORIZON_MONTHS=24.
_hidden_release_fraction клампит h/18→1.0 на 24 (без переполнения),
future_supply._horizon_weight расширяет окно чисто — скрытых ≤18 потолков нет.
Чистое расширение валидатора-enum, не новая математика.
Backend: _ALLOWED_FORECAST_HORIZONS → {6,12,18,24}, Query/docstring/error-msg.
Frontend: HorizonSelector HORIZONS=[6,12,18,24] (тип horizon=number, union не нужен;
прочие потребители data-driven через meta.horizons/forecasts_by_horizon).
Тесты: API принимает 24/отвергает 30; движок-тесты доказывают h=24 осмыслен
(поля посчитаны, demand(24)>demand(18), hidden созрел, индексы в диапазонах).
Closes#944 (Q1 горизонт 24)
DemandSupplyForecast.as_dict() не эмитил 'confounded'/'is_confounded_window',
report_assembler._confounded() всегда возвращал False и §15 confounded_window
factor в compute_report_confidence был мёртв: 48-мес окна, пересекающие
2024-07-01 шок никогда не тянули report confidence к 'low' и шок не назывался
в rationale.
Patch: добавлено confounded: bool в DemandSupplyForecast (от §9.5 macro_coef
OR §9.6 rate_sensitivity), exposed в as_dict(). _confounded() уже использовал
.get() defensively — блокер был в producer'е.
+3 теста: contract на real DemandSupplyForecast.as_dict(), end-to-end
assemble_report → confounded_window factor surfaces at level=low, weakest-link
тянет overall к 'low'. 61 report_assembler + 1034 forecasting тестов зелёные.
Closes#1222
Degenerate price band (own_min==own_max или c_lo==c_hi, оба разрешены
CHECK 148 и Pydantic) внутри другой вилки возвращали 0.0 вместо 1.0:
фильтр w>0 выкидывал нулевую ширину → 0/positive=0.0. Это рвало
докстринг 'полное накрытие узкого = 1.0' и давало разрыв:
[148k,152k]→1.0 vs [150k,150k]→0.0, занижая среднее каннибализации.
Patch: вырожденные ширины обрабатываются ДО нормирования.
lo<=hi → точка внутри другой вилки → 1.0, вне → 0.0. +inf-обе-премиум
ветка перенесена в начало (избежать inf-inf=nan). +7 новых тестов в
TestPriceOverlap. 220 special_indices тестов зелёные.
Closes#1224
objective_lots.district хранит МИКРО-вокабуляр ('Уралмаш', 'ЖБИ', ...).
_query_artificial_demand фильтровал сырым АДМИН-именем ('Кировский' с
forecast.py:123) → ol.district='Кировский' = 0 строк → n_sold=0 → §25.5
Artificial Demand 'unavailable' с ложной причиной «нет проданных лотов»
в каждом district-scoped отчёте. Тот же класс бага, что #1211 в
_price_sensitivity.
Patch: импорт resolve_objective_districts + замена сырого
`ol.district = CAST(:district AS text)` на зеркальный паттерн
sales_series._SOURCE_B_SQL / market_metrics._SALES_WINDOW_SQL:
(CAST(:has_district AS boolean) IS FALSE
OR ol.district = ANY(CAST(:districts AS text[])))
Сигнатура _query_artificial_demand / _build_artificial_demand НЕ меняется
— caller остаётся admin-aware на входе.
+5 новых тестов (TestArtificialDemandDistrictResolution: резолвер вызван,
микро в bind, n_sold>0 после фикса), 6 обновлённых SQL-тестов. 21 passed
artificial_demand + 1030 forecasting тестов зелёные. ruff clean.
Closes#1205
`analyze["district"]` в этой кодовой базе — dict вида
{"district_name": "Верх-Исетский", "dist_to_center": 0.0, "median_price_per_m2": ...}.
Штатный caller (`workers/tasks/forecast.py:123`) явно извлекает `district_name`:
`district = row.district or analyze["district"]["district_name"]`. Но новые callers
(тесты, расширения чата, ad-hoc эндпоинты) легко передают сырой dict без знания этой
конвенции — тогда внутри §9.x-слоёв compute_market_metrics(district=<dict>) падает
с TypeError: unhashable type: 'dict' в forecast_request_cache.wrapper,
`_safe_call` это проглатывает → секции future_market.forecasts_by_horizon=[] и
scenarios.by_scenario={} тихо остаются пустыми (silent degrade, не 500).
Добавлен `_normalize_district(district)` — pure-нормализация на входе оркестратора:
- str → как есть;
- None → None;
- dict с district_name (непустая строка) → извлекаем;
- dict без district_name / с пустым / неподдерживаемый тип → None + logger.warning.
7 unit-тестов в test_orchestrator.py::TestNormalizeDistrict (все варианты входов).
Не меняет поведение штатного caller'а (str → str), только защищает от случайных
dict-callers.
Discovered through: #1130 Phase A (мой первый тестовый скрипт со скормленным
сырым `analyze["district"]` dict выдал forecasts.n_horizons=0 + 15 TypeError'ов
в _safe_call). Закрывает чип task_4a4aa3bb.
Refs #1130
Feed candidate_unit_mix into _build_cannibalization (mirrors how #1169 fed
candidate_release_month from the launch window), completing §25.3 to all 4 axes:
class + price + timing + unit-mix, plus geo weight.
- candidate mix from recommend_mix "buckets[].share_pct" (same rule-based
квартирография as §22 product_tz), extracted + normalized to {bucket: share}.
- _canonical_room_bucket folds recommend_mix RU labels ("1-к 30-45", "80+ м²")
and manual own_planned_project Latin keys ("1k") into one room-count space —
without it the L1 similarity would silently be 0 (disjoint keys).
- recommend_mix is HEAVY, so it's GATED: derived only when the own-portfolio has
>=1 project with a non-empty unit_mix; get_own_portfolio fetched once in
compute_special_indices and threaded into _build_cannibalization (no double
fetch). With OWN_DEVELOPER_IDS unset (portfolio empty) → zero added cost on the
hot §22 report path.
- Graceful (recommend_mix None/empty/raises → axis excluded, None-not-0),
deterministic. Unit-mix only fires for manual-future own-projects with a mix
(domrf-current carry unit_mix=None) — expected narrowness, documented.
205 tests; ruff + mypy clean. Scope: special_indices.py + test only; no deps.
Refs #1169
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.
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
