fix(sf-10): velocity LEFT JOIN + velocity_data_available flag #284
3 changed files with 198 additions and 86 deletions
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@ -47,6 +47,9 @@ class VelocityResult:
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period_end: str # YYYY-MM
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sample_competitors: list[dict[str, Any]] # top-5 для UI
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by_room_bucket: dict[str, dict[str, Any]] # агрегат по комнатности
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# True если ≥1 конкурент имеет маппинг в objective_complex_mapping;
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# False → конкуренты найдены, но данных Objective нет — velocity = 0.
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velocity_data_available: bool = True
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def as_dict(self) -> dict[str, Any]:
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return {
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@ -59,6 +62,7 @@ class VelocityResult:
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"period": {"start": self.period_start, "end": self.period_end},
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"sample_competitors": self.sample_competitors,
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"by_room_bucket": self.by_room_bucket,
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"velocity_data_available": self.velocity_data_available,
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}
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@ -165,6 +169,8 @@ def compute_velocity(
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# objective_corpus_room_month.
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# deals_total_vol_m2 = DDU + DKP м² за месяц (primary signal).
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# deals_total_count > 0 — фильтрует месяцы без сделок.
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# LEFT JOIN с objective_complex_mapping: конкуренты без маппинга не
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# выпадают — они включаются в список но с total_sqm=NULL (→ 0.0).
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# GROUP BY domrf_obj_id чтобы сохранить совместимость с caller (obj_id key).
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try:
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with db.begin_nested():
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@ -172,25 +178,32 @@ def compute_velocity(
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db.execute(
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text(
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"""
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WITH mapped AS (
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WITH all_competitors AS (
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SELECT unnest(CAST(:obj_ids AS int[])) AS obj_id
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),
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mapped AS (
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SELECT cm.domrf_obj_id AS obj_id,
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cm.objective_complex_name
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FROM objective_complex_mapping cm
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WHERE cm.domrf_obj_id = ANY(:obj_ids)
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)
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SELECT
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m.obj_id,
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ac.obj_id,
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SUM(COALESCE(crm.deals_total_vol_m2,
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crm.deals_total_count * 45.0)) AS total_sqm,
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COUNT(DISTINCT crm.report_month) AS months_with_data,
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MIN(crm.report_month) AS period_start,
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MAX(crm.report_month) AS period_end
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FROM objective_corpus_room_month crm
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JOIN mapped m
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ON m.objective_complex_name = crm.project_name
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WHERE crm.report_month >= (CURRENT_DATE - CAST(:window_interval AS interval))
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MAX(crm.report_month) AS period_end,
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CASE WHEN m.obj_id IS NOT NULL THEN TRUE
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ELSE FALSE END AS has_mapping
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FROM all_competitors ac
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LEFT JOIN mapped m ON m.obj_id = ac.obj_id
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LEFT JOIN objective_corpus_room_month crm
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ON crm.project_name = m.objective_complex_name
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AND crm.report_month >= (
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CURRENT_DATE - CAST(:window_interval AS interval))
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AND crm.deals_total_count > 0
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GROUP BY m.obj_id
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GROUP BY ac.obj_id, m.obj_id
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"""
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),
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{
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@ -209,6 +222,44 @@ def compute_velocity(
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if not sales_rows:
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return None
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# Проверяем: есть ли хотя бы один конкурент с маппингом (has_mapping=True).
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# Если нет — возвращаем velocity=0 с явным флагом velocity_data_available=False,
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# вместо того чтобы отбросить всех конкурентов (старый INNER JOIN поведение).
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has_any_mapping = any(bool(r["has_mapping"]) for r in sales_rows)
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if not has_any_mapping:
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logger.info(
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"velocity: %d competitors found but none mapped in objective_complex_mapping;"
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" returning velocity=0 with data_available=False",
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len(obj_ids),
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)
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ekb_median = (
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_get_ekb_median(db, months_window=months_window) or _EKB_MEDIAN_FALLBACK_SQM_PER_MONTH
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)
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n_comps = len(comp_rows)
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sample = [
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{
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"obj_id": oid,
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**competitor_meta[oid],
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"total_sqm_period": 0.0,
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"by_room_bucket": {},
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}
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for oid in obj_ids[:5]
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if oid in competitor_meta
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]
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return VelocityResult(
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competitors_count=n_comps,
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monthly_velocity_sqm=0.0,
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ekb_median_sqm=ekb_median,
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velocity_score=0.0,
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confidence="low",
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months_observed=0,
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period_start="",
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period_end="",
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sample_competitors=sample,
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by_room_bucket={},
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velocity_data_available=False,
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)
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# ── Step 2b: разбивка по комнатности (room_bucket) ───────────────────────
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# Тот же маппинг domrf_obj_id → project_name. Агрегируем по room_bucket
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# для отображения структуры спроса в UI.
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@ -278,46 +329,85 @@ def compute_velocity(
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for bucket, data in by_bucket_agg.items()
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}
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total_sqm = sum(float(r["total_sqm"] or 0.0) for r in sales_rows)
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months_observed = max((int(r["months_with_data"] or 0) for r in sales_rows), default=0)
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period_start_dates = [r["period_start"] for r in sales_rows if r["period_start"]]
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period_end_dates = [r["period_end"] for r in sales_rows if r["period_end"]]
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# Считаем только по строкам с маппингом — unmapped строки дают total_sqm=NULL.
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mapped_sales_rows = [r for r in sales_rows if bool(r["has_mapping"])]
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total_sqm = sum(float(r["total_sqm"] or 0.0) for r in mapped_sales_rows)
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months_observed = max((int(r["months_with_data"] or 0) for r in mapped_sales_rows), default=0)
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period_start_dates = [r["period_start"] for r in mapped_sales_rows if r["period_start"]]
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period_end_dates = [r["period_end"] for r in mapped_sales_rows if r["period_end"]]
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period_start = min(period_start_dates).strftime("%Y-%m") if period_start_dates else ""
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period_end = max(period_end_dates).strftime("%Y-%m") if period_end_dates else ""
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if months_observed == 0 or total_sqm <= 0:
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return None
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# Среднемесячный объём в расчёте: суммарный по всем конкурентам / месяцев.
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# Чем больше конкурентов с данными — тем весомее результат.
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monthly_velocity = total_sqm / months_observed
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# ── Step 3: ЕКБ-медиана ──────────────────────────────────────────────────
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ekb_median = (
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_get_ekb_median(db, months_window=months_window) or _EKB_MEDIAN_FALLBACK_SQM_PER_MONTH
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)
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n_comps = len(comp_rows)
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# Если mapped-конкурентов нет данных — partial coverage → velocity=0.
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if months_observed == 0 or total_sqm <= 0:
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logger.info(
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"velocity: %d competitors found, %d mapped, but no sales data in window;"
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" returning velocity=0 with data_available=False",
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len(obj_ids),
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len(mapped_sales_rows),
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)
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sample_partial = sorted(
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[
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{
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"obj_id": oid,
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**competitor_meta[oid],
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"total_sqm_period": 0.0,
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"by_room_bucket": {},
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}
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for oid in obj_ids
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if oid in competitor_meta
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],
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key=lambda x: x["total_sqm_period"], # type: ignore[arg-type]
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reverse=True,
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)[:5]
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return VelocityResult(
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competitors_count=n_comps,
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monthly_velocity_sqm=0.0,
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ekb_median_sqm=ekb_median,
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velocity_score=0.0,
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confidence="low",
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months_observed=0,
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period_start="",
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period_end="",
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sample_competitors=sample_partial,
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by_room_bucket={},
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velocity_data_available=False,
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)
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# Среднемесячный объём в расчёте: суммарный по всем конкурентам / месяцев.
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# Чем больше конкурентов с данными — тем весомее результат.
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monthly_velocity = total_sqm / months_observed
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# ── Step 4: нормализация → score 0..1 ────────────────────────────────────
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# Логика: сравниваем суммарный velocity радиуса с «нормой» одного ЖК.
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# Если в радиусе продаётся N × ekb_median → рынок горячий.
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# Нормируем: score = min(1.0, total_velocity / (n_competitors × ekb_median × 2))
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# Cap 2×median = «насыщен». Итоговый score 0..1.
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n_with_sales = len(sales_rows)
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# n_with_sales — только mapped конкуренты (у unmapped данных нет).
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n_with_sales = len(mapped_sales_rows)
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denominator = n_with_sales * ekb_median * 2.0 if n_with_sales > 0 else ekb_median * 2.0
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velocity_score = min(1.0, max(0.0, monthly_velocity / denominator))
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# ── Step 5: confidence ───────────────────────────────────────────────────
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n_comps = len(comp_rows)
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mapped_conf: Literal["high", "medium", "low"]
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if n_comps >= 10 and months_observed >= 5:
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confidence: Literal["high", "medium", "low"] = "high"
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mapped_conf = "high"
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elif n_comps >= 5 and months_observed >= 3:
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confidence = "medium"
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mapped_conf = "medium"
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else:
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confidence = "low"
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mapped_conf = "low"
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# ── Step 6: top-5 конкурентов по объёму продаж ───────────────────────────
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sales_by_id: dict[int, float] = {
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int(r["obj_id"]): float(r["total_sqm"] or 0.0) for r in sales_rows
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int(r["obj_id"]): float(r["total_sqm"] or 0.0) for r in mapped_sales_rows
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}
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sample = sorted(
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[
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@ -339,12 +429,13 @@ def compute_velocity(
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monthly_velocity_sqm=monthly_velocity,
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ekb_median_sqm=ekb_median,
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velocity_score=velocity_score,
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confidence=confidence,
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confidence=mapped_conf,
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months_observed=months_observed,
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period_start=period_start,
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period_end=period_end,
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sample_competitors=sample,
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by_room_bucket=by_room_bucket,
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velocity_data_available=True,
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)
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@ -49,6 +49,7 @@ export function VelocityBlock({ velocity }: VelocityBlockProps) {
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);
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}
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const dataAvailable = velocity.velocity_data_available !== false;
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const confColor = CONFIDENCE_COLOR[velocity.confidence];
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const scorePct = formatPercent(velocity.velocity_score);
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const ratio = velocity.monthly_velocity_sqm / velocity.ekb_median_sqm;
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@ -64,6 +65,12 @@ export function VelocityBlock({ velocity }: VelocityBlockProps) {
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}}
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>
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<SectionLabel>Темп продаж конкурентов</SectionLabel>
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<div style={{ display: "flex", alignItems: "center", gap: 6 }}>
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{!dataAvailable && (
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<span className="bg-slate-100 text-slate-500 text-xs px-2 py-0.5 rounded">
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нет данных velocity
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</span>
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)}
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<span
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style={{
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padding: "2px 8px",
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@ -78,8 +85,10 @@ export function VelocityBlock({ velocity }: VelocityBlockProps) {
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{CONFIDENCE_LABEL[velocity.confidence]}
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</span>
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</div>
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</div>
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{/* Score gauge */}
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{/* Score gauge — показываем только если данные есть */}
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{dataAvailable && (
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<div style={{ marginBottom: 12 }}>
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<div
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style={{
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@ -91,7 +100,9 @@ export function VelocityBlock({ velocity }: VelocityBlockProps) {
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}}
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>
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<span>Velocity-score</span>
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<span style={{ fontWeight: 600, color: "#111827" }}>{scorePct}</span>
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<span style={{ fontWeight: 600, color: "#111827" }}>
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{scorePct}
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</span>
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</div>
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<div
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style={{
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@ -122,14 +133,21 @@ export function VelocityBlock({ velocity }: VelocityBlockProps) {
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: `${formatPercent(ratio)} от среднего`}
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</div>
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</div>
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)}
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{/* Period + competitors meta */}
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<div style={{ fontSize: 12, color: "#6b7280", marginBottom: 8 }}>
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В радиусе 3 км: <b>{velocity.competitors_count}</b> ЖК · период{" "}
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В радиусе 3 км: <b>{velocity.competitors_count}</b> ЖК
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{dataAvailable && (
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<>
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{" "}
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· период{" "}
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<b>
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{velocity.period.start} → {velocity.period.end}
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</b>{" "}
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({velocity.months_observed} мес)
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</>
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)}
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</div>
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{/* By room bucket aggregate */}
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@ -241,6 +241,9 @@ export interface Velocity {
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period: VelocityPeriod;
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sample_competitors: VelocityCompetitor[];
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by_room_bucket?: Record<string, VelocityBucketStat>;
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// True если ≥1 конкурент имеет маппинг в objective_complex_mapping.
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// False → конкуренты найдены, velocity=0, данных Objective нет.
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velocity_data_available?: boolean;
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}
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// G5 (#32) — Gate verdict: can_build_mkd
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