"""Forecasting services — детерминированный форсайт-слой Site Finder v2.

#951 (Site Finder v2 / «GG-форсайт», EPIC 7 «Чувствительность к ключевой ставке»).

Этот пакет — фундамент data-independent логики прогноза: monthly макро-ряды,
классификатор режима ставки, лаговые помощники. Всё ДЕТЕРМИНИРОВАННО, БЕЗ LLM.

Слои (по PR):
  • macro_series  (#951b)          — monthly макро-ряд + классификатор режима ставки (X-ось §9.6).
  • sales_series  (#951c)          — monthly ряд продаж по сегменту (Y-ось §9.6).
  • rate_sensitivity (#951d)       — §9.6 чувствительность продаж к key_rate (CORE, ADVISORY).
  • macro_coefficient (#951e)      — §9.5 макро-коэффициент (композитный множитель, ADVISORY).
  • demand_normalization (#951f)   — §9.4 нормализация спроса под смену режима ставки (ADVISORY).
  • demand_supply_forecast (#952a) — §9.8 центральный движок: спрос (§9.4ק9.5) vs
    предложение (§9.3) по горизонтам → баланс/индекс дефицита (СБОРКА, ADVISORY).
  • what_to_build (#981/952-B)     — §9.7 ранкер сетки сегментов по deficit_index
    (прогон #980 per-cell → DESC «что строить»; СБОРКА, ADVISORY).
  • affordability (#981/952-B)     — §7.9 MAI: ДЕГРАДИРОВАННЫЙ прокси платёжной
    нагрузки (субсид. ставка, дохода нет → low-confidence; СБОРКА, ADVISORY).
  • scenarios (#984/954-A)         — §11 три макро-сценария (conservative/base/
    aggressive) прогоном #952 под тремя конвертами ставки (СБОРКА, ADVISORY).
  • product_scoring (#985/954-B)   — §14.2 десять продуктовых скоров ∈ [0,1] (выше=
    лучше) + взвешенный overall (renorm над доступными) + §16 причина на скор;
    сводит #950…#984 + live-стек, graceful-None, ADVISORY.
  • special_indices (#986/954-C)   — §25 шесть специальных индексов (Launch Window,
    Product Void, Cannibalization, Competitor Strength, Artificial Demand, Cost-of-
    Error); сборка над #980/#981/§9.1/§9.2/§7.9, per-index graceful-None, ADVISORY.

Источники данных:
  • макро — таблица macro_indicator через reader site_finder/macro.py (reuse).
  • продажи — objective_corpus_room_month / objective_lots (см. sales_series).
"""

from __future__ import annotations

from app.services.forecasting.affordability import (
    MortgageAffordabilityIndex,
    compute_affordability,
)
from app.services.forecasting.confidence_engine import (
    ConfidenceFactor,
    ReportConfidenceResult,
    compute_report_confidence,
)
from app.services.forecasting.demand_normalization import (
    DemandNormalization,
    compute_demand_normalization,
    normalization_factor,
)
from app.services.forecasting.demand_supply_forecast import (
    DemandSupplyForecast,
    compute_demand_supply_forecast,
    hold_last_rate,
)
from app.services.forecasting.macro_coefficient import (
    MacroCoefficient,
    assemble_coefficient,
    compute_macro_coefficient,
    f_issuance,
    f_mortgage_rate,
    f_overdue,
    f_rate,
    renormalize_contributions,
    segment_steepness,
)
from app.services.forecasting.macro_series import (
    MonthlyMacro,
    classify_regime,
    get_monthly_macro,
    is_confounded_window,
    macro_at_lag,
)
from app.services.forecasting.product_scoring import (
    ProductScore,
    ProductScoreCard,
    compute_score_card,
)
from app.services.forecasting.rate_sensitivity import (
    RateSensitivity,
    best_lag,
    compute_rate_sensitivity,
    ols_slope_r2,
    shrink,
)
from app.services.forecasting.report import (
    ReportConfidence,
    ReportExecSummary,
    ReportFutureMarket,
    ReportMarketNow,
    ReportMeta,
    ReportProductTz,
    ReportScenarios,
    ReportScoring,
    SiteFinderReport,
)
from app.services.forecasting.sales_series import (
    SalesSeries,
    SegmentSpec,
    build_sales_series,
    fill_month_grid,
    log_diff,
    price_bucket_of,
    room_area_bucket_of,
)
from app.services.forecasting.scenarios import (
    ScenarioForecast,
    build_rate_envelopes,
    compute_scenarios,
)
from app.services.forecasting.special_indices import (
    SpecialIndex,
    SpecialIndices,
    compute_special_indices,
)
from app.services.forecasting.what_to_build import (
    RankedSegment,
    WhatToBuildRanking,
    rank_segments,
)

__all__ = [
    "ConfidenceFactor",
    "DemandNormalization",
    "DemandSupplyForecast",
    "MacroCoefficient",
    "MonthlyMacro",
    "MortgageAffordabilityIndex",
    "ProductScore",
    "ProductScoreCard",
    "RankedSegment",
    "RateSensitivity",
    "ReportConfidence",
    "ReportConfidenceResult",
    "ReportExecSummary",
    "ReportFutureMarket",
    "ReportMarketNow",
    "ReportMeta",
    "ReportProductTz",
    "ReportScenarios",
    "ReportScoring",
    "SalesSeries",
    "ScenarioForecast",
    "SegmentSpec",
    "SiteFinderReport",
    "SpecialIndex",
    "SpecialIndices",
    "WhatToBuildRanking",
    "assemble_coefficient",
    "best_lag",
    "build_rate_envelopes",
    "build_sales_series",
    "classify_regime",
    "compute_affordability",
    "compute_demand_normalization",
    "compute_demand_supply_forecast",
    "compute_macro_coefficient",
    "compute_rate_sensitivity",
    "compute_report_confidence",
    "compute_scenarios",
    "compute_score_card",
    "compute_special_indices",
    "f_issuance",
    "f_mortgage_rate",
    "f_overdue",
    "f_rate",
    "fill_month_grid",
    "get_monthly_macro",
    "hold_last_rate",
    "is_confounded_window",
    "log_diff",
    "macro_at_lag",
    "normalization_factor",
    "ols_slope_r2",
    "price_bucket_of",
    "rank_segments",
    "renormalize_contributions",
    "room_area_bucket_of",
    "segment_steepness",
    "shrink",
]