Light1YT
78b667b018
EPIC 8 advisory deliverables over the #980 demand/supply engine, deterministic, no LLM, no new SQL: - what_to_build.rank_segments: runs compute_demand_supply_forecast across a class×room_bucket[×price] grid (default 3×5×1=15) per district/cad_num/horizon, drops deficit_index=None cells, ranks deficit_index DESC (tie-break confidence DESC then stable segment key). +deficit=build-signal, −=затоварка. - affordability.compute_affordability: §7.9 MAI degraded payment-burden proxy via subsidized weighted rate (_current_mortgage_rate) + annuity on 50m²/240mo. payment_to_income always None (#946 no income), rate_kind always subsidized_weighted (market ~20% absent), confidence always low. - Pure _rank_key / _annuity (rate≤0 → principal/months guard) unit-tested. 62 new tests; 368 forecasting tests green. Advisory + un-wired.
120 lines
4.2 KiB
Python
120 lines
4.2 KiB
Python
"""Forecasting services — детерминированный форсайт-слой Site Finder v2.
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#951 (Site Finder v2 / «GG-форсайт», EPIC 7 «Чувствительность к ключевой ставке»).
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Этот пакет — фундамент data-independent логики прогноза: monthly макро-ряды,
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классификатор режима ставки, лаговые помощники. Всё ДЕТЕРМИНИРОВАННО, БЕЗ LLM.
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Слои (по PR):
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• macro_series (#951b) — monthly макро-ряд + классификатор режима ставки (X-ось §9.6).
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• sales_series (#951c) — monthly ряд продаж по сегменту (Y-ось §9.6).
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• rate_sensitivity (#951d) — §9.6 чувствительность продаж к key_rate (CORE, ADVISORY).
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• macro_coefficient (#951e) — §9.5 макро-коэффициент (композитный множитель, ADVISORY).
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• demand_normalization (#951f) — §9.4 нормализация спроса под смену режима ставки (ADVISORY).
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• demand_supply_forecast (#952a) — §9.8 центральный движок: спрос (§9.4ק9.5) vs
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предложение (§9.3) по горизонтам → баланс/индекс дефицита (СБОРКА, ADVISORY).
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• what_to_build (#981/952-B) — §9.7 ранкер сетки сегментов по deficit_index
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(прогон #980 per-cell → DESC «что строить»; СБОРКА, ADVISORY).
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• affordability (#981/952-B) — §7.9 MAI: ДЕГРАДИРОВАННЫЙ прокси платёжной
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нагрузки (субсид. ставка, дохода нет → low-confidence; СБОРКА, ADVISORY).
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Источники данных:
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• макро — таблица macro_indicator через reader site_finder/macro.py (reuse).
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• продажи — objective_corpus_room_month / objective_lots (см. sales_series).
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"""
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from __future__ import annotations
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from app.services.forecasting.affordability import (
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MortgageAffordabilityIndex,
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compute_affordability,
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)
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from app.services.forecasting.demand_normalization import (
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DemandNormalization,
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compute_demand_normalization,
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normalization_factor,
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)
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from app.services.forecasting.demand_supply_forecast import (
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DemandSupplyForecast,
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compute_demand_supply_forecast,
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hold_last_rate,
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)
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from app.services.forecasting.macro_coefficient import (
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MacroCoefficient,
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assemble_coefficient,
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compute_macro_coefficient,
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f_issuance,
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f_mortgage_rate,
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f_overdue,
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f_rate,
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renormalize_contributions,
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segment_steepness,
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)
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from app.services.forecasting.macro_series import (
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MonthlyMacro,
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classify_regime,
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get_monthly_macro,
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is_confounded_window,
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macro_at_lag,
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)
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from app.services.forecasting.rate_sensitivity import (
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RateSensitivity,
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best_lag,
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compute_rate_sensitivity,
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ols_slope_r2,
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shrink,
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)
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from app.services.forecasting.sales_series import (
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SalesSeries,
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SegmentSpec,
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build_sales_series,
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fill_month_grid,
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log_diff,
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price_bucket_of,
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room_area_bucket_of,
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)
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from app.services.forecasting.what_to_build import (
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RankedSegment,
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WhatToBuildRanking,
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rank_segments,
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)
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__all__ = [
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"DemandNormalization",
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"DemandSupplyForecast",
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"MacroCoefficient",
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"MonthlyMacro",
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"MortgageAffordabilityIndex",
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"RankedSegment",
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"RateSensitivity",
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"SalesSeries",
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"SegmentSpec",
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"WhatToBuildRanking",
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"assemble_coefficient",
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"best_lag",
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"build_sales_series",
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"classify_regime",
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"compute_affordability",
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"compute_demand_normalization",
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"compute_demand_supply_forecast",
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"compute_macro_coefficient",
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"compute_rate_sensitivity",
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"f_issuance",
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"f_mortgage_rate",
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"f_overdue",
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"f_rate",
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"fill_month_grid",
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"get_monthly_macro",
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"hold_last_rate",
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"is_confounded_window",
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"log_diff",
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"macro_at_lag",
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"normalization_factor",
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"ols_slope_r2",
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"price_bucket_of",
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"rank_segments",
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"renormalize_contributions",
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"room_area_bucket_of",
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"segment_steepness",
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"shrink",
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]
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