upd recommend sys

backend/app/services/analytics_queries.py

@@ -1095,6 +1095,186 @@ def _velocity_baseline(
     }
 
 
+def _district_market_saturation(db: Session, *, district_name: str) -> tuple[float | None, int]:
+    """Median sold% активных строящихся ЖК в районе. >50% = зрелый рынок
+    (конкуренты много продали, новый проект имеет место). <20% = свежий
+    (много инвентаря на продажу, сложнее пробиться).
+
+    Возвращает (median_pct, n_objects). None если <5 ЖК с perc.
+    """
+    row = (
+        db.execute(
+            text(
+                """
+                SELECT PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY a.perc) AS sold_median,
+                       COUNT(*) AS n
+                  FROM domrf_kn_sales_agg a
+                  JOIN domrf_kn_objects o
+                    ON o.obj_id = a.obj_id
+                   AND o.snapshot_date = a.snapshot_date
+                 WHERE a.type = 'apartments'
+                   AND a.perc IS NOT NULL
+                   AND o.district_name = :dn
+                   AND o.site_status = 'Строящиеся'
+                """
+            ),
+            {"dn": district_name},
+        )
+        .mappings()
+        .first()
+    )
+    if not row or (row["n"] or 0) < 5:
+        return None, int(row["n"] or 0) if row else 0
+    return _f(row["sold_median"]), int(row["n"])
+
+
+def _district_velocity_trend(db: Session, *, district_name: str) -> tuple[float | None, int, int]:
+    """Ratio realised: recent_6mo / prior_6mo. >1.5 — рынок горит, <0.7 —
+    остывает. Считаем за окно 12 мес: H1 2025 vs H2 2025+.
+
+    Возвращает (ratio, recent_units, prior_units). None если данных мало.
+    """
+    row = (
+        db.execute(
+            text(
+                """
+                SELECT
+                    SUM(sg.realised) FILTER (WHERE sg.report_month >= DATE '2025-07-01')
+                        AS recent,
+                    SUM(sg.realised) FILTER (WHERE sg.report_month BETWEEN DATE '2025-01-01'
+                                              AND DATE '2025-06-30')
+                        AS prior
+                  FROM domrf_kn_sale_graph sg
+                  JOIN domrf_kn_objects o
+                    ON o.obj_id = sg.obj_id
+                   AND o.snapshot_date = sg.snapshot_date
+                 WHERE sg.type = 'apartments'
+                   AND o.district_name = :dn
+                """
+            ),
+            {"dn": district_name},
+        )
+        .mappings()
+        .first()
+    )
+    recent = int(row["recent"] or 0) if row else 0
+    prior = int(row["prior"] or 0) if row else 0
+    if prior > 0 and recent > 0:
+        return recent / prior, recent, prior
+    return None, recent, prior
+
+
+_POI_WEIGHTS = {
+    "Транспорт": 1.5,
+    "Метро": 2.0,
+    "Образование": 1.2,
+    "Медицина": 1.3,
+    "Спорт": 1.0,
+    "Продукты": 0.8,
+    "Развлечения": 0.7,
+    "Новостройки": 0.0,  # сами ЖК — не используем как amenity
+}
+
+
+def _district_poi_score(db: Session, *, district_name: str) -> float | None:
+    """Среднее по ЖК района: weighted POI count в радиусе 1000м.
+    Используем категории-веса (метро/медицина важнее, новостройки игнор).
+
+    Возвращает None если в районе <3 ЖК с POI.
+    """
+    weights_sql = " ".join(
+        [f"WHEN i.poi_category = '{cat}' THEN {w:.2f}" for cat, w in _POI_WEIGHTS.items()]
+    )
+    row = (
+        db.execute(
+            text(
+                f"""
+                WITH per_obj AS (
+                    SELECT i.obj_id,
+                           SUM(CASE {weights_sql} ELSE 0.5 END)
+                               FILTER (WHERE i.distance_m IS NULL OR i.distance_m <= 1000)
+                               AS weighted_poi
+                      FROM domrf_kn_infrastructure i
+                      JOIN domrf_kn_objects o
+                        ON o.obj_id = i.obj_id
+                       AND o.snapshot_date = i.snapshot_date
+                     WHERE o.district_name = :dn
+                     GROUP BY i.obj_id
+                )
+                SELECT AVG(weighted_poi) AS avg_score, COUNT(*) AS n
+                  FROM per_obj
+                 WHERE weighted_poi > 0
+                """
+            ),
+            {"dn": district_name},
+        )
+        .mappings()
+        .first()
+    )
+    if not row or (row["n"] or 0) < 3:
+        return None
+    return _f(row["avg_score"])
+
+
+def _city_avg_poi_score(db: Session, *, region_code: int = 66) -> float | None:
+    """Средний POI score по всему ЕКБ — для нормировки district_poi_score."""
+    weights_sql = " ".join(
+        [f"WHEN i.poi_category = '{cat}' THEN {w:.2f}" for cat, w in _POI_WEIGHTS.items()]
+    )
+    row = (
+        db.execute(
+            text(
+                f"""
+                WITH per_obj AS (
+                    SELECT i.obj_id,
+                           SUM(CASE {weights_sql} ELSE 0.5 END)
+                               FILTER (WHERE i.distance_m IS NULL OR i.distance_m <= 1000)
+                               AS weighted_poi
+                      FROM domrf_kn_infrastructure i
+                      JOIN domrf_kn_objects o
+                        ON o.obj_id = i.obj_id
+                       AND o.snapshot_date = i.snapshot_date
+                     WHERE o.region_cd = :rc
+                       AND o.district_name IS NOT NULL
+                     GROUP BY i.obj_id
+                )
+                SELECT AVG(weighted_poi) AS avg_score
+                  FROM per_obj
+                 WHERE weighted_poi > 0
+                """
+            ),
+            {"rc": region_code},
+        )
+        .mappings()
+        .first()
+    )
+    return _f(row["avg_score"]) if row else None
+
+
+def _current_mortgage_rate(db: Session) -> tuple[float | None, str | None]:
+    """Последняя средневзвешенная ипотечная ставка из cbr_mortgage_series.
+    Возвращает (rate_pct, period_label)."""
+    row = (
+        db.execute(
+            text(
+                """
+                SELECT value, period
+                  FROM cbr_mortgage_series
+                 WHERE title ILIKE '%ипотечн%жилищн%'
+                   AND value IS NOT NULL
+                 ORDER BY period DESC
+                 LIMIT 1
+                """
+            )
+        )
+        .mappings()
+        .first()
+    )
+    if not row:
+        return None, None
+    return _f(row["value"]), row["period"]
+
+
 def _active_competitors_count(
     db: Session,
     *,
@@ -1289,34 +1469,52 @@ def recommend_mix(
         else 1.0
     )
 
-    # 3) Class multiplier from yandex_realty_zk price ranges (price_from)
+    # 3) Class multiplier через yandex_realty_zk + Comfort как BASELINE (×1.0).
+    # Раньше делили class_avg/overall_avg где overall = смесь по 12 rows
+    # → числа абсурдные (Elite ×1.22, Comfort+ ×0.66 < Comfort).
+    # Теперь: ratio(class) = class_price_avg / comfort_price_avg.
+    # Реалистичные индустриальные значения: Comfort=1.0, Comfort+=1.02,
+    # Business=1.86, Elite=4.27 (на основе текущих 12 rows yandex_realty_zk).
+    # yandex_realty_class_prices игнорируем — midpoint бессмыслен (ширина
+    # диапазонов класса искажает result).
+    # UI шлёт 'Comfort'/'Comfort+'/'Business'/'Elite' → realty_zk: 'COMFORT'/
+    # 'COMFORT_PLUS'/'BUSINESS'/'ELITE'.
     class_multiplier = 1.0
+    class_multiplier_source: str | None = None
     if target_class:
-        cls_row = (
+        zk_norm = {
-            db.execute(
+            "Comfort": "COMFORT",
-                text(
+            "Comfort+": "COMFORT_PLUS",
-                    """
+            "Business": "BUSINESS",
+            "Elite": "ELITE",
+        }.get(target_class)
+        if zk_norm:
+            r = (
+                db.execute(
+                    text(
+                        """
                     SELECT
-                        AVG(price_from) FILTER (WHERE obj_class = :cls) AS class_avg,
+                        AVG(price_from) FILTER (WHERE obj_class = :cls)     AS class_avg,
-                        AVG(price_from)                                 AS overall_avg
+                        AVG(price_from) FILTER (WHERE obj_class = 'COMFORT') AS comfort_avg
                       FROM yandex_realty_zk
                      WHERE price_from IS NOT NULL AND price_from > 0
                     """
-                ),
+                    ),
-                {"cls": target_class},
+                    {"cls": zk_norm},
-            )
+                )
-            .mappings()
+                .mappings()
-            .first()
+                .first()
-        )
-        cavg = _f(cls_row["class_avg"]) if cls_row else None
-        oavg = _f(cls_row["overall_avg"]) if cls_row else None
-        if cavg and oavg and oavg > 0:
-            class_multiplier = cavg / oavg
-        else:
-            warnings.append(
-                f"Нет ценовых данных yandex_realty_zk для класса '{target_class}',"
-                " коэффициент класса = 1.0"
             )
+            cavg = _f(r["class_avg"]) if r else None
+            comfort_avg = _f(r["comfort_avg"]) if r else None
+            if cavg and comfort_avg and comfort_avg > 0:
+                class_multiplier = cavg / comfort_avg
+                class_multiplier_source = "realty_zk_vs_comfort"
+            else:
+                warnings.append(
+                    f"Нет ценовых данных yandex_realty_zk для класса '{target_class}'"
+                    " — коэффициент класса = 1.0"
+                )
 
     # 4) Bucket distribution from rosreestr_deals — city-wide, last N months.
     # Если в каком-либо бакете <30 сделок и окно < 24 мес, расширяем до 24 мес
@@ -1484,26 +1682,57 @@ def recommend_mix(
         for r in bucket_rows
     }
 
+    # 5b-2.5) Дополнительные district-specific signals (Tier 2):
+    #   sat_factor   — насколько зрелый рынок района (median sold% активных
+    #                  ЖК). >50% = зрелый, новый проект имеет место, +bonus.
+    #                  <20% = свежий, много инвентаря, -penalty.
+    #   trend_factor — recent_6mo / prior_6mo realised. Clamp 0.7..2.0 чтобы
+    #                  экстремум не разрушал расчёты.
+    #   poi_factor   — weighted POI density района / city avg. ±5% на цены.
+    sat_median, sat_n = _district_market_saturation(db, district_name=district_row["district_name"])
+    sat_factor = 1 + (sat_median - 50) / 100 * 0.3 if sat_median is not None else 1.0
+
+    trend_ratio, trend_recent, trend_prior = _district_velocity_trend(
+        db, district_name=district_row["district_name"]
+    )
+    trend_factor = max(0.7, min(2.0, trend_ratio)) if trend_ratio else 1.0
+
+    poi_score = _district_poi_score(db, district_name=district_row["district_name"])
+    city_avg_poi = _city_avg_poi_score(db, region_code=region_code)
+    poi_factor = (
+        1 + (poi_score - city_avg_poi) / max(city_avg_poi, 1) * 0.05
+        if (poi_score is not None and city_avg_poi is not None and city_avg_poi > 0)
+        else 1.0
+    )
+
+    mortgage_rate, mortgage_period = _current_mortgage_rate(db)
+
     # 5b-3) Per-bucket project velocity at price_factor=1.0:
     #   bucket_market_v   = темп РЫНКА для bucket'а (deals/mo по всему региону)
-    #   normalisation     = sqrt(N_competitors) — компромисс между
+    #   normalisation     = sqrt(N_competitors) — power-law эффективные
-    #                       «монополист» (÷1) и «равный pie-split» (÷N).
+    #                       конкуренты (sqrt компромисс между ÷1 и ÷N).
-    #                       Реально продажи распределены по power-law: top-20%
+    #   project_velocity  = bucket_market_v / sqrt(N) × sat_factor × trend_factor
-    #                       ЖК делают 80% сделок, поэтому linear ÷N даёт
+    #                       sat — зрелый рынок ускоряет; trend — текущая
-    #                       абсурдные сроки в десятилетия. sqrt — стандартный
+    #                       динамика (горит/остывает).
-    #                       proxy для "effective competitors" в подобных
-    #                       зашумлённых market-share моделях.
-    #   project_velocity  = bucket_market_v / sqrt(N_competitors)
     #   adjusted          = project_velocity × price_factor^elasticity
     #   months_to_sellout = units_planned / adjusted
+    # Цена тоже корректируется на poi_factor (развитость района = премиум).
     pf_pow = price_factor**elasticity if price_factor > 0 else 1.0
     competitors_norm = math.sqrt(max(competitors, 1))
+    macro_velocity_mult = sat_factor * trend_factor
     total_units = 0
     for b in buckets:
         bucket_market_v = bucket_market_velocities.get(b["bucket"], 0.0)
-        bucket_velocity = round(bucket_market_v / competitors_norm, 4)
+        bucket_velocity = round(bucket_market_v / competitors_norm * macro_velocity_mult, 4)
         b["velocity_per_month"] = bucket_velocity
+        # POI-корректировка на цену (на ВСЕ p25/median/p75)
+        b["price_median_per_m2"] = round(b["price_median_per_m2"] * poi_factor, 2)
+        b["price_p25_per_m2"] = round(b["price_p25_per_m2"] * poi_factor, 2)
+        b["price_p75_per_m2"] = round(b["price_p75_per_m2"] * poi_factor, 2)
         if b["units_planned"] and bucket_velocity > 0:
+            # Revenue тоже пересчитываем после POI-correction (linear scale).
+            if b["revenue_planned_rub"] is not None:
+                b["revenue_planned_rub"] = round(b["revenue_planned_rub"] * poi_factor, 2)
             adjusted_velocity = bucket_velocity * pf_pow
             b["months_to_sellout"] = (
                 round(b["units_planned"] / adjusted_velocity, 1) if adjusted_velocity > 0 else None
@@ -1511,6 +1740,11 @@ def recommend_mix(
             total_units += b["units_planned"]
         else:
             b["months_to_sellout"] = None
+    # Итог revenue + weighted_avg_price после POI-correction (linear scale).
+    if have_revenue:
+        total_revenue *= poi_factor
+    if weighted_avg_price is not None:
+        weighted_avg_price = round(weighted_avg_price * poi_factor, 2)
 
     # 5c) Inverse mode: target_months → required price_factor.
     # required_velocity = total_units / target_months
@@ -1617,6 +1851,7 @@ def recommend_mix(
             "district_median_price_per_m2": district_median,
             "district_factor": round(district_factor, 4),
             "class_multiplier": round(class_multiplier, 4),
+            "class_multiplier_source": class_multiplier_source,
             "target_class": target_class,
             "months_window": months_window,
             "effective_window_months": effective_window,
@@ -1630,6 +1865,18 @@ def recommend_mix(
             "velocity_objects": vel["objects_count"],
             "competitors_count": competitors,
             "competitors_scope": competitors_scope,
+            "saturation_median": sat_median,
+            "saturation_n": sat_n,
+            "sat_factor": round(sat_factor, 4),
+            "velocity_trend_ratio": (round(trend_ratio, 2) if trend_ratio is not None else None),
+            "trend_recent_units": trend_recent,
+            "trend_prior_units": trend_prior,
+            "trend_factor": round(trend_factor, 4),
+            "poi_score": round(poi_score, 1) if poi_score is not None else None,
+            "poi_score_city_avg": (round(city_avg_poi, 1) if city_avg_poi is not None else None),
+            "poi_factor": round(poi_factor, 4),
+            "mortgage_rate_pct": mortgage_rate,
+            "mortgage_rate_period": mortgage_period,
             "elasticity": elasticity,
             "elasticity_r2": elast["r2"],
             "elasticity_n": elast["n"],

frontend/src/app/analytics/recommend/page.tsx

@@ -153,14 +153,35 @@ export default function RecommendPage() {
                     lineHeight: 1.4,
                   }}
                 >
-                  💼 <strong>«{data.scope.district}</strong>
+                  <div>
-                  {data.scope.target_class
+                    💼 <strong>«{data.scope.district}</strong>
-                    ? ` · ${data.scope.target_class}`
+                    {data.scope.target_class
-                    : ""}
+                      ? ` · ${data.scope.target_class}`
-                  {input.area_total_m2
+                      : ""}
-                    ? ` · ${input.area_total_m2.toLocaleString("ru")} м²`
+                    {input.area_total_m2
-                    : ""}
+                      ? ` · ${input.area_total_m2.toLocaleString("ru")} м²`
-                  <strong>»:</strong> {data.summary.headline}
+                      : ""}
+                    <strong>»:</strong> {data.summary.headline}
+                  </div>
+                  <div
+                    style={{
+                      marginTop: 8,
+                      paddingTop: 8,
+                      borderTop: "1px solid rgba(255,255,255,0.1)",
+                      fontSize: 12,
+                      color: "#94a3b8",
+                      fontWeight: 400,
+                    }}
+                  >
+                    📊
+                    {data.scope.mortgage_rate_pct != null
+                      ? ` Ставка ЦБ ${data.scope.mortgage_rate_pct.toFixed(2)}% (${data.scope.mortgage_rate_period})`
+                      : " ставка ЦБ нет данных"}
+                    {data.scope.poi_score != null &&
+                    data.scope.poi_score_city_avg != null
+                      ? ` · POI ${data.scope.poi_score.toFixed(0)}/${data.scope.poi_score_city_avg.toFixed(0)} (${data.scope.poi_score > data.scope.poi_score_city_avg ? "выше" : "ниже"} среднего)`
+                      : ""}
+                  </div>
                 </div>
               ) : null}
 
@@ -240,24 +261,43 @@ export default function RecommendPage() {
                   }
                 />
                 <KpiCard
-                  label="District factor"
+                  label="Sold% медиана"
-                  value={`×${data.scope.district_factor.toFixed(2)}`}
+                  value={
+                    data.scope.saturation_median != null
+                      ? `${data.scope.saturation_median.toFixed(0)}%`
+                      : "—"
+                  }
                   hint={
-                    data.scope.district_factor === 1
+                    data.scope.saturation_median == null
-                      ? "median ekb_districts NULL"
+                      ? "<5 ЖК с данными"
-                      : "корректировка к city median"
+                      : data.scope.saturation_median > 50
+                        ? `зрелый рынок (${data.scope.saturation_n} ЖК)`
+                        : data.scope.saturation_median < 25
+                          ? `свежий, мало распродано (${data.scope.saturation_n} ЖК)`
+                          : `умеренная зрелость (${data.scope.saturation_n} ЖК)`
                   }
                 />
                 <KpiCard
-                  label="Class multiplier"
+                  label="Тренд 6 мес"
-                  value={`×${data.scope.class_multiplier.toFixed(2)}`}
+                  value={
-                  hint={
+                    data.scope.velocity_trend_ratio != null
-                    data.scope.class_multiplier === 1
+                      ? `${data.scope.velocity_trend_ratio > 1 ? "🚀" : data.scope.velocity_trend_ratio < 0.8 ? "❄" : "→"} ×${data.scope.velocity_trend_ratio.toFixed(2)}`
-                      ? data.scope.target_class
+                      : "—"
-                        ? `нет данных по ${data.scope.target_class}`
-                        : "класс не задан"
-                      : `корректировка цен ${data.scope.target_class}`
                   }
+                  hint={
+                    data.scope.velocity_trend_ratio == null
+                      ? "нет sale_graph"
+                      : data.scope.velocity_trend_ratio > 1.5
+                        ? "рынок ускоряется"
+                        : data.scope.velocity_trend_ratio < 0.8
+                          ? "остывает"
+                          : "стабильный"
+                  }
+                />
+                <KpiCard
+                  label="Class × District"
+                  value={`×${(data.scope.class_multiplier * data.scope.district_factor * data.scope.poi_factor).toFixed(2)}`}
+                  hint={`Class ×${data.scope.class_multiplier.toFixed(2)} · District ×${data.scope.district_factor.toFixed(2)} · POI ×${data.scope.poi_factor.toFixed(2)}`}
                 />
               </div>
 

frontend/src/components/analytics/RecommendVelocityPanel.tsx

@@ -264,8 +264,18 @@ export function RecommendVelocityPanel({
           ? `sale_graph: ${scope.velocity_objects} ЖК / ${scope.velocity_observations} точек`
           : "fallback на rosreestr-сделки"}
         ), нормирован на <strong>{scope.competitors_count}</strong> активных
-        конкурентов в районе ). При price ×{priceFactor.toFixed(2)} темп =
+        конкурентов в районе. Применены macro-факторы:{" "}
-        базовый × {priceFactor.toFixed(2)}^{elasticity} = ×{pfPow.toFixed(3)}.
+        <strong>sat ×{scope.sat_factor.toFixed(2)}</strong>
+        {scope.saturation_median != null
+          ? ` (sold% ${scope.saturation_median.toFixed(0)})`
+          : ""}{" "}
+        · <strong>trend ×{scope.trend_factor.toFixed(2)}</strong>
+        {scope.velocity_trend_ratio != null
+          ? ` (raw ×${scope.velocity_trend_ratio.toFixed(2)} clamp 0.7..2.0)`
+          : ""}{" "}
+        · <strong>POI ×{scope.poi_factor.toFixed(2)}</strong> (на цену) ). При
+        price ×{priceFactor.toFixed(2)} темп = базовый ×{" "}
+        {priceFactor.toFixed(2)}^{elasticity} = ×{pfPow.toFixed(3)}.
       </div>
     </div>
   );

frontend/src/types/analytics.ts

@@ -239,6 +239,7 @@ export interface RecommendMixOutput {
     district_factor: number;
     class_multiplier: number;
     target_class: string | null;
+    class_multiplier_source: "realty_zk_vs_comfort" | null;
     months_window: number;
     effective_window_months: number;
     region_code: number;
@@ -253,6 +254,18 @@ export interface RecommendMixOutput {
       | "district"
       | "region"
       | "fallback_singleton";
+    saturation_median: number | null;
+    saturation_n: number;
+    sat_factor: number;
+    velocity_trend_ratio: number | null;
+    trend_recent_units: number;
+    trend_prior_units: number;
+    trend_factor: number;
+    poi_score: number | null;
+    poi_score_city_avg: number | null;
+    poi_factor: number;
+    mortgage_rate_pct: number | null;
+    mortgage_rate_period: string | null;
     elasticity: number;
     elasticity_r2: number;
     elasticity_n: number;