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
