fix(tradein/estimator): честная маркировка quarter-precision дат ДКП + insufficient_data флаг для sell-time-sensitivity (#1995)

"01.01.26 у всех сделок" — не ETL-баг, а честный предел источника:
rosreestr open dataset публикует только квартальную гранулярность
(period_start_date = первый день квартала), не точную дату сделки
(задокументировано в 01_schema_rosreestr_deals.sql ещё на моменте
создания схемы). Живой SQL подтвердил: 9 distinct дат на 9 кварталов,
5510 сделок все на 2026-01-01.

Фикс — честная маркировка, без фабрикации данных:
- AnalogLot.date_precision / SalesListingPair.deal_date_precision
  ("day"/"quarter") — фронт может показать "Q1 2026" вместо ложной
  точной даты.
- SellTimeBucket.insufficient_data (n_lots < sell_time_sensitivity_min_n_lots=10,
  паттерн 1-в-1 с существующим AvitoImvSummary.thin_market) —
  немонотонность на малых бакетах помечается, не сглаживается статистически.

tradein-mvp/backend/app/api/v1/trade_in.py

@@ -15,6 +15,7 @@ from fastapi import APIRouter, Depends, File, Header, HTTPException, Response, U
 from sqlalchemy import text
 from sqlalchemy.orm import Session
 
+from app.core.config import settings
 from app.core.db import get_db
 from app.schemas.trade_in import (
     AggregatedEstimate,
@@ -1414,6 +1415,7 @@ def get_estimate_sell_time_sensitivity(
     buckets: list[SellTimeBucket] = []
     for label, pct in bucket_definitions:
         r = bucket_map.get(label)
+        n_lots = r["n_lots"] if r else 0
         buckets.append(
             SellTimeBucket(
                 price_premium_label=label,
@@ -1421,7 +1423,11 @@ def get_estimate_sell_time_sensitivity(
                 median_exposure_days=r["median_exposure_days"] if r else None,
                 p25_days=r["p25_days"] if r else None,
                 p75_days=r["p75_days"] if r else None,
-                n_lots=r["n_lots"] if r else 0,
+                n_lots=n_lots,
+                # #1995: малая выборка → median/p25/p75 шумные (наблюдалась
+                # немонотонность между бакетами, напр. +10% быстрее +5%). Честный
+                # флаг вместо тихого шума — фронт решает, как показать.
+                insufficient_data=n_lots < settings.sell_time_sensitivity_min_n_lots,
             )
         )
 
@@ -1851,6 +1857,10 @@ def get_sales_vs_listings(
             ),
             days_listing_to_deal=r["days_listing_to_deal"],
             discount_pct=(float(r["discount_pct"]) if r["discount_pct"] is not None else None),
+            # #1995: street_sales_vs_listings() фильтрует ТОЛЬКО source='rosreestr'
+            # (067_v_street_sales_vs_listings.sql) → все pairs сейчас квартальной
+            # precision (deal_date = period_start_date). Честная маркировка, не баг.
+            deal_date_precision="quarter",
         )
         for r in rows
     ]

tradein-mvp/backend/app/core/config.py

@@ -633,5 +633,16 @@ class Settings(BaseSettings):
     # ENV: BROWSER_WAIT_MS.
     browser_wait_ms: int = 6000
 
+    # ── #1995: sell-time-sensitivity — честная маркировка малой выборки ────────
+    # /estimate/{id}/sell-time-sensitivity бьёт лоты на 4 price-бакета (cheap/
+    # median/plus5/plus10) и считает median/p25/p75 exposure_days по каждому.
+    # При n_lots ниже порога результат бакета шумный (замечена немонотонность:
+    # +10% продаётся быстрее +5% — артефакт малой выборки, не data-баг). Вместо
+    # тихого шума бакет с n_lots < порога помечается insufficient_data=True
+    # (SellTimeBucket) — тот же паттерн, что и AvitoImvSummary.thin_market
+    # (avito_imv_thin_market_threshold). НЕ сглаживаем/не выдумываем статистику —
+    # честная маркировка.
+    sell_time_sensitivity_min_n_lots: int = 10
+
 
 settings = Settings()

tradein-mvp/backend/app/schemas/trade_in.py

@@ -71,6 +71,17 @@ class AnalogLot(BaseModel):
     # kadastr_num — все ДКП-сделки сейчас T1. Поле зарезервировано на случай
     # будущего enrichment data feed (ЕГРН direct).
     tier: str | None = None
+    # ── Честность даты (#1995) ──
+    # Rosreestr open dataset публикует ДКП-сделки с точностью до КВАРТАЛА
+    # (listing_date = deals.deal_date = period_start_date, первый день квартала —
+    # см. data/sql/01_schema_rosreestr_deals.sql, комментарий "Excluded: ... exact
+    # deal date"). Поэтому ВСЕ rosreestr-сделки одного квартала несут ОДИНАКОВЫЙ
+    # listing_date — это честное отражение granularity источника, а НЕ баг/заглушка
+    # (подтверждено live-аудитом prod: 9 кварталов, ровно 1 distinct deal_date на
+    # квартал). listings (avito/cian/yandex/domklik) несут реальную day-level дату
+    # скрапинга/парсинга. None — источник не задан (напр. устаревшая persisted-запись
+    # до этого поля — rehydrate default).
+    date_precision: Literal["day", "quarter"] | None = None
 
 
 class CianChartPoint(BaseModel):
@@ -452,6 +463,11 @@ class SellTimeBucket(BaseModel):
     p25_days: int | None
     p75_days: int | None
     n_lots: int
+    # #1995: n_lots < settings.sell_time_sensitivity_min_n_lots → малая выборка,
+    # median/p25/p75 exposure_days шумные (немонотонность между бакетами — типичный
+    # артефакт, не data-баг). Фронт должен явно показать "недостаточно данных"
+    # вместо тихого шума. Не сглаживаем/не переоцениваем статистику — честный флаг.
+    insufficient_data: bool = False
 
 
 class SellTimeSensitivityResponse(BaseModel):
@@ -518,6 +534,16 @@ class SalesListingPair(BaseModel):
     # discount_pct = (deal_price - listing_price) / listing_price * 100.
     # Отрицательный = продали дешевле выставленного (торг).
     discount_pct: float | None = None
+    # #1995: честность precision deal_date. Rosreestr open dataset публикует ДКП с
+    # точностью до КВАРТАЛА (deal_date = period_start_date, 1-е число квартала —
+    # см. data/sql/01_schema_rosreestr_deals.sql), НЕ реальную дату регистрации.
+    # street_sales_vs_listings() фильтрует ТОЛЬКО source='rosreestr' → сейчас
+    # precision одинаковая ("quarter") для всех pairs этого endpoint'а. Live-аудит
+    # prod (2026-07): 9 загруженных кварталов — ровно 1 distinct deal_date на
+    # квартал, подтверждает НЕ баг, а granularity источника. Per-pair (не
+    # response-level) — задел на случай будущего source с exact-датой (etazhi/
+    # domklik_history, см. deals table comment).
+    deal_date_precision: Literal["day", "quarter"] = "quarter"
 
 
 class SalesVsListingsResponse(BaseModel):

tradein-mvp/backend/app/services/estimator.py

@@ -29,7 +29,7 @@ import time
 from collections.abc import Callable, Iterable
 from dataclasses import dataclass
 from datetime import UTC, date, datetime, timedelta
-from typing import Any
+from typing import Any, Literal
 from uuid import uuid4
 
 from scraper_kit.providers.avito.imv import (
@@ -5259,6 +5259,22 @@ def _compute_confidence(
     return base, explanation
 
 
+def _date_precision_for_source(source: str | None) -> Literal["day", "quarter"] | None:
+    """Честная маркировка precision listing_date/deal_date (#1995).
+
+    Rosreestr open dataset публикует ДКП-сделки с точностью до КВАРТАЛА
+    (deals.deal_date = period_start_date, первый день квартала — см. data/sql/
+    01_schema_rosreestr_deals.sql, комментарий "Excluded: ... exact deal date").
+    Live-аудит prod (2026-07): 9 загруженных кварталов, ровно 1 distinct deal_date
+    на квартал — подтверждает granularity источника, а НЕ баг/заглушку.
+    listings (avito/cian/yandex/domklik) несут реальную day-level дату
+    скрапинга/парсинга. source=None (неизвестен) → None (precision не заявляем).
+    """
+    if source is None:
+        return None
+    return "quarter" if source == "rosreestr" else "day"
+
+
 def _listing_to_analog(row: dict[str, Any]) -> AnalogLot:
     return AnalogLot(
         address=row.get("address") or "",
@@ -5270,6 +5286,7 @@ def _listing_to_analog(row: dict[str, Any]) -> AnalogLot:
         price_per_m2=int(row.get("price_per_m2") or 0),
         listing_date=row.get("listing_date"),
         days_on_market=row.get("days_on_market"),
+        date_precision=_date_precision_for_source(row.get("source")),
         photo_url=(row["photo_urls"] or [None])[0] if row.get("photo_urls") else None,
         source=row.get("source"),
         source_url=row.get("source_url"),
@@ -5310,6 +5327,7 @@ def _anchor_comp_to_analog(c: dict[str, Any]) -> AnalogLot:
         price_per_m2=ppm2,
         listing_date=c.get("listing_date"),
         days_on_market=c.get("days_on_market"),
+        date_precision=_date_precision_for_source(c.get("source")),
         photo_url=(photo_urls or [None])[0] if photo_urls else None,
         source=c.get("source"),
         source_url=c.get("source_url"),
@@ -5340,6 +5358,7 @@ def _deal_to_analog(row: dict[str, Any]) -> AnalogLot:
         price_per_m2=int(row.get("price_per_m2") or 0),
         listing_date=row.get("deal_date"),
         days_on_market=row.get("days_on_market"),
+        date_precision=_date_precision_for_source(row.get("source")),
         photo_url=None,
         source=row.get("source"),
         source_url=None,  # rosreestr сделки без публичной ссылки

tradein-mvp/backend/tests/test_estimator_enrich.py

@@ -83,6 +83,68 @@ def test_deal_to_analog_carries_lat_lon() -> None:
     assert lot.tier == "T0_per_house"  # kadastr with участок → per-house tier
 
 
+# --------------------------------------------------------------------------- #
+# date_precision honesty flag (#1995) — rosreestr deals are quarter-granular,
+# real listings are day-granular. See _date_precision_for_source docstring +
+# data/sql/01_schema_rosreestr_deals.sql for the underlying data-source reason.
+# --------------------------------------------------------------------------- #
+
+
+def test_deal_to_analog_marks_rosreestr_source_as_quarter_precision() -> None:
+    """#1995: deals.source='rosreestr' → date_precision='quarter' — deal_date is
+    period_start_date (первый день квартала), НЕ реальная дата регистрации.
+    Это честная маркировка данных, а не баг (live-аудит prod: 9 кварталов,
+    ровно 1 distinct deal_date на квартал)."""
+    row = {
+        "address": "ул. Тестовая, 2",
+        "area_m2": 60.0,
+        "rooms": 2,
+        "floor": 5,
+        "total_floors": 10,
+        "price_rub": 11_000_000,
+        "price_per_m2": 183_333,
+        "deal_date": None,
+        "days_on_market": None,
+        "cadastral_number": "66:41:0204016:10",
+        "source": "rosreestr",
+        "distance_m": 80.0,
+        "lat": 56.84,
+        "lon": 60.61,
+    }
+    lot = estimator._deal_to_analog(row)
+    assert lot.date_precision == "quarter"
+
+
+def test_listing_to_analog_marks_real_source_as_day_precision() -> None:
+    """Listings (avito/cian/yandex/domklik) carry a real scrape/parse date →
+    date_precision='day'."""
+    row = {
+        "address": "ул. Тестовая, 1",
+        "area_m2": 50.0,
+        "rooms": 2,
+        "floor": 3,
+        "total_floors": 9,
+        "price_rub": 10_000_000,
+        "price_per_m2": 200_000,
+        "listing_date": None,
+        "days_on_market": 14,
+        "photo_urls": None,
+        "source": "cian",
+        "source_url": "https://example.test/1",
+        "distance_m": 120.0,
+        "lat": 56.8389,
+        "lon": 60.6057,
+    }
+    lot = estimator._listing_to_analog(row)
+    assert lot.date_precision == "day"
+
+
+def test_date_precision_none_when_source_unknown() -> None:
+    """source отсутствует (устаревшая/неполная строка) → date_precision=None —
+    не заявляем precision, которую не можем подтвердить."""
+    assert estimator._date_precision_for_source(None) is None
+
+
 def test_analog_lat_lon_optional_none_when_missing() -> None:
     # Tier S address-only Avito lots can lack geom → lat/lon absent → None (graceful).
     row = {

tradein-mvp/backend/tests/test_sales_vs_listings.py

@@ -341,9 +341,7 @@ def test_sales_vs_listings_passes_proper_params(trade_in_app: FastAPI) -> None:
 def test_sales_vs_listings_response_shape(trade_in_app: FastAPI) -> None:
     """Все required fields присутствуют в JSON response."""
     fixture_rows = [
-        _make_pair_row(
+        _make_pair_row(deal_id=1, listing_id=11, discount_pct=-3.0, listing_source="cian"),
-            deal_id=1, listing_id=11, discount_pct=-3.0, listing_source="cian"
-        ),
     ]
     db_mock = _make_db_mock(fixture_rows)
     _override_db(trade_in_app, db_mock)
@@ -361,24 +359,81 @@ def test_sales_vs_listings_response_shape(trade_in_app: FastAPI) -> None:
     data = resp.json()
     # Top-level keys
     expected_keys = {
-        "street", "period_months", "window_days", "area_tolerance",
+        "street",
-        "total_deals", "deals_with_listings", "linkage_rate_pct",
+        "period_months",
-        "median_discount_pct", "pairs",
+        "window_days",
+        "area_tolerance",
+        "total_deals",
+        "deals_with_listings",
+        "linkage_rate_pct",
+        "median_discount_pct",
+        "pairs",
     }
     assert expected_keys.issubset(data.keys())
     # Pair keys
     pair = data["pairs"][0]
     pair_keys = {
-        "deal_id", "deal_date", "deal_price_rub", "deal_price_per_m2",
+        "deal_id",
-        "deal_area_m2", "deal_rooms", "deal_floor", "deal_address",
+        "deal_date",
-        "listing_id", "listing_source", "listing_source_url", "listing_date",
+        "deal_price_rub",
-        "listing_price_rub", "listing_price_per_m2", "listing_area_m2",
+        "deal_price_per_m2",
-        "days_listing_to_deal", "discount_pct",
+        "deal_area_m2",
+        "deal_rooms",
+        "deal_floor",
+        "deal_address",
+        "listing_id",
+        "listing_source",
+        "listing_source_url",
+        "listing_date",
+        "listing_price_rub",
+        "listing_price_per_m2",
+        "listing_area_m2",
+        "days_listing_to_deal",
+        "discount_pct",
     }
     assert pair_keys.issubset(pair.keys())
     assert pair["listing_source"] == "cian"
 
 
+# ── Test: honest deal_date_precision marking (#1995) ─────────────────────────
+
+
+def test_sales_vs_listings_marks_deal_date_precision_quarter(
+    trade_in_app: FastAPI,
+) -> None:
+    """#1995: street_sales_vs_listings() возвращает ТОЛЬКО rosreestr-сделки —
+    deal_date = period_start_date (первый день квартала), не реальная дата
+    регистрации (см. data/sql/01_schema_rosreestr_deals.sql). Live-аудит prod
+    подтвердил: 9 загруженных кварталов, ровно 1 distinct deal_date на квартал —
+    это НЕ баг/заглушка, а granularity источника. Каждая пара должна нести
+    честный deal_date_precision="quarter", даже когда (как в prod) МНОГО разных
+    сделок совпадают по deal_date."""
+    same_quarter_date = date(2026, 1, 1)
+    fixture_rows = [
+        _make_pair_row(deal_id=1, deal_date=same_quarter_date, listing_id=11),
+        _make_pair_row(deal_id=2, deal_date=same_quarter_date, listing_id=12),
+        _make_pair_row(deal_id=3, deal_date=same_quarter_date, listing_id=13),
+    ]
+    db_mock = _make_db_mock(fixture_rows)
+    _override_db(trade_in_app, db_mock)
+
+    client = TestClient(trade_in_app)
+    resp = client.get(
+        "/api/v1/trade-in/sales-vs-listings",
+        params={
+            "address": "г. Екатеринбург, ул. Космонавтов, 50",
+            "area_m2": 50.0,
+            "rooms": 2,
+        },
+    )
+    assert resp.status_code == 200
+    data = resp.json()
+    assert len(data["pairs"]) == 3
+    for pair in data["pairs"]:
+        assert pair["deal_date"] == "2026-01-01"
+        assert pair["deal_date_precision"] == "quarter"
+
+
 # ── Test: default param values ───────────────────────────────────────────────
 
 

tradein-mvp/backend/tests/test_sell_time_sensitivity.py

@@ -0,0 +1,240 @@
+"""Tests for GET /estimate/{id}/sell-time-sensitivity (#1995).
+
+Regression coverage for the "недостаточно данных" honesty flag: buckets built
+from a thin sample (n_lots below settings.sell_time_sensitivity_min_n_lots)
+must carry insufficient_data=True instead of silently exposing noisy
+median/p25/p75 exposure_days numbers. We deliberately do NOT try to smooth or
+"fix" a non-monotonic result (e.g. +10% selling faster than +5%) — see
+test_sell_time_sensitivity_does_not_smooth_non_monotonic_result below.
+"""
+
+from __future__ import annotations
+
+import os
+import sys
+from types import SimpleNamespace
+from unittest.mock import MagicMock
+
+# psycopg v3 driver required; stub DATABASE_URL before any app import
+os.environ.setdefault("DATABASE_URL", "postgresql+psycopg://test:test@localhost:5432/test")
+
+# WeasyPrint requires GTK — not present in CI/Windows. Stub before any app import.
+_wp_mock = MagicMock()
+sys.modules.setdefault("weasyprint", _wp_mock)
+sys.modules.setdefault("weasyprint.CSS", _wp_mock)
+sys.modules.setdefault("weasyprint.HTML", _wp_mock)
+
+import pytest  # noqa: E402
+from fastapi import FastAPI  # noqa: E402
+from fastapi.testclient import TestClient  # noqa: E402
+
+_ESTIMATE_ID = "22222222-2222-2222-2222-222222222222"
+
+
+@pytest.fixture(autouse=True)
+def _restore_get_role():
+    """Restore app.core.auth.get_role after each test (mirror test_estimate_idor)."""
+    from app.core import auth as auth_mod
+
+    original = auth_mod.get_role
+    yield
+    auth_mod.get_role = original
+
+
+def _make_app() -> FastAPI:
+    """Minimal FastAPI app mounting only the trade-in router."""
+    from app.api.v1 import trade_in as trade_in_module
+
+    application = FastAPI()
+    application.include_router(trade_in_module.router, prefix="/api/v1/trade-in")
+    return application
+
+
+def _exec_result(
+    *,
+    fetchone: object | None = None,
+    all_rows: list | None = None,
+    scalar: object | None = None,
+    mapping_rows: list | None = None,
+) -> MagicMock:
+    """Build a single db.execute(...) return value supporting whichever chain
+    the endpoint calls next (.fetchone() / .all() / .scalar() / .mappings().all())."""
+    result = MagicMock()
+    result.fetchone.return_value = fetchone
+    result.all.return_value = all_rows if all_rows is not None else []
+    result.scalar.return_value = scalar
+    mapping_mock = MagicMock()
+    mapping_mock.all.return_value = mapping_rows if mapping_rows is not None else []
+    result.mappings.return_value = mapping_mock
+    return result
+
+
+def _make_db_mock(
+    *,
+    created_by: str | None,
+    bucket_rows: list[dict],
+    target_median: int | None = 120_000,
+) -> MagicMock:
+    """Sequential db.execute() mock covering the endpoint's fixed 6-call path
+    (explicit radius_m, address resolves ≥1 house_id):
+      1. _assert_estimate_access_by_id → fetchone(created_by)
+      2. target lat/lon/address → fetchone
+      3. address-based house lookup → all()
+      4. radius_m-based house expansion → all()
+      5. target_median benchmark → scalar()
+      6. bucket_rows → mappings().all()
+    """
+    db = MagicMock()
+    db.execute.side_effect = [
+        _exec_result(fetchone=SimpleNamespace(created_by=created_by)),
+        _exec_result(
+            fetchone=SimpleNamespace(lat=56.8, lon=60.6, address="Екатеринбург, ул. Тестовая, 1")
+        ),
+        _exec_result(all_rows=[SimpleNamespace(id=1)]),
+        _exec_result(all_rows=[SimpleNamespace(id=2)]),
+        _exec_result(scalar=target_median),
+        _exec_result(mapping_rows=bucket_rows),
+    ]
+    return db
+
+
+def _client_with(app: FastAPI, db_mock: MagicMock, role: str) -> TestClient:
+    from app.core.db import get_db
+
+    def _override_db():
+        yield db_mock
+
+    app.dependency_overrides[get_db] = _override_db
+
+    auth_mod = sys.modules["app.core.auth"]
+    auth_mod.get_role = lambda _u: role  # type: ignore[assignment]
+    return TestClient(app)
+
+
+def _bucket_row(bucket: str, n_lots: int, median_exposure_days: int) -> dict:
+    return {
+        "bucket": bucket,
+        "n_lots": n_lots,
+        "median_exposure_days": median_exposure_days,
+        "p25_days": median_exposure_days - 5,
+        "p75_days": median_exposure_days + 5,
+    }
+
+
+def _get(client: TestClient, *, radius_m: int = 500) -> dict:
+    resp = client.get(
+        f"/api/v1/trade-in/estimate/{_ESTIMATE_ID}/sell-time-sensitivity",
+        params={"radius_m": radius_m},
+        headers={"X-Authenticated-User": "admin"},
+    )
+    assert resp.status_code == 200, resp.text
+    return resp.json()
+
+
+# ── Test: thin bucket (n_lots < threshold) flagged insufficient_data ────────
+
+
+def test_sell_time_sensitivity_flags_thin_bucket_insufficient() -> None:
+    """n_lots below settings.sell_time_sensitivity_min_n_lots (default 10) →
+    insufficient_data=True; buckets with plenty of lots stay False."""
+    from app.core.config import settings
+
+    bucket_rows = [
+        _bucket_row("cheap", 20, 30),
+        _bucket_row("median", 18, 35),
+        # plus5 — thin sample (3 lots).
+        _bucket_row("plus5", 3, 60),
+        _bucket_row("plus10", 15, 40),
+    ]
+    db_mock = _make_db_mock(created_by="kopylov", bucket_rows=bucket_rows)
+    app = _make_app()
+    client = _client_with(app, db_mock, role="admin")
+    data = _get(client)
+
+    by_label = {b["price_premium_label"]: b for b in data["buckets"]}
+    assert by_label["cheap"]["n_lots"] == 20
+    assert by_label["cheap"]["insufficient_data"] is False
+    assert by_label["plus5"]["n_lots"] == 3
+    assert by_label["plus5"]["insufficient_data"] is True
+    assert by_label["plus5"]["n_lots"] < settings.sell_time_sensitivity_min_n_lots
+
+
+# ── Test: bucket at exactly the threshold is NOT flagged (strict <) ─────────
+
+
+def test_sell_time_sensitivity_bucket_at_threshold_not_flagged() -> None:
+    """n_lots == threshold (10) is considered sufficient (strict less-than check)."""
+    from app.core.config import settings
+
+    bucket_rows = [
+        _bucket_row("cheap", settings.sell_time_sensitivity_min_n_lots, 25),
+        _bucket_row("median", 20, 30),
+        _bucket_row("plus5", 20, 35),
+        _bucket_row("plus10", 20, 40),
+    ]
+    db_mock = _make_db_mock(created_by="kopylov", bucket_rows=bucket_rows)
+    app = _make_app()
+    client = _client_with(app, db_mock, role="admin")
+    data = _get(client)
+
+    by_label = {b["price_premium_label"]: b for b in data["buckets"]}
+    assert by_label["cheap"]["n_lots"] == settings.sell_time_sensitivity_min_n_lots
+    assert by_label["cheap"]["insufficient_data"] is False
+
+
+# ── Test: missing bucket (no DB rows at all) defaults n_lots=0 → flagged ────
+
+
+def test_sell_time_sensitivity_missing_bucket_flagged() -> None:
+    """A price bucket absent from the DB result (no matching lots) still comes
+    back with n_lots=0 and MUST be flagged insufficient_data — never a bare 0
+    presented as if it were a real, confident median."""
+    bucket_rows = [
+        _bucket_row("cheap", 20, 30),
+        _bucket_row("median", 18, 35),
+        _bucket_row("plus10", 15, 40),
+        # 'plus5' entirely missing from bucket_rows.
+    ]
+    db_mock = _make_db_mock(created_by="kopylov", bucket_rows=bucket_rows)
+    app = _make_app()
+    client = _client_with(app, db_mock, role="admin")
+    data = _get(client)
+
+    by_label = {b["price_premium_label"]: b for b in data["buckets"]}
+    assert by_label["plus5"]["n_lots"] == 0
+    assert by_label["plus5"]["median_exposure_days"] is None
+    assert by_label["plus5"]["insufficient_data"] is True
+
+
+# ── Test: non-monotonic result is surfaced as-is, not smoothed ──────────────
+
+
+def test_sell_time_sensitivity_does_not_smooth_non_monotonic_result() -> None:
+    """#1995: +10% selling (median_exposure_days=20) FASTER than +5%
+    (median_exposure_days=60) is a real, honestly-reported small-sample
+    artifact — the endpoint must NOT invent smoothing/interpolation. Both
+    thin buckets are simply flagged insufficient_data so the frontend can
+    choose to de-emphasize them, and the raw (non-monotonic) numbers pass
+    through unchanged."""
+    bucket_rows = [
+        _bucket_row("cheap", 20, 30),
+        _bucket_row("median", 18, 35),
+        _bucket_row("plus5", 4, 60),  # thin — slower
+        _bucket_row("plus10", 3, 20),  # thin — faster (non-monotonic vs plus5)
+    ]
+    db_mock = _make_db_mock(created_by="kopylov", bucket_rows=bucket_rows)
+    app = _make_app()
+    client = _client_with(app, db_mock, role="admin")
+    data = _get(client)
+
+    by_label = {b["price_premium_label"]: b for b in data["buckets"]}
+    # Raw numbers unchanged — no smoothing/reordering applied.
+    assert by_label["plus5"]["median_exposure_days"] == 60
+    assert by_label["plus10"]["median_exposure_days"] == 20
+    # Both thin buckets honestly flagged instead of silently shown.
+    assert by_label["plus5"]["insufficient_data"] is True
+    assert by_label["plus10"]["insufficient_data"] is True
+
+
+if __name__ == "__main__":  # pragma: no cover
+    raise SystemExit(pytest.main([__file__, "-q"]))