Rate Analysis: fix hotel detail 500 + timeline 422, match frontend shapes; signed % delta badge
- /analysis/hotel/{id}: lead_bucket alias in ORDER BY CASE broke Postgres; replaced
bucketed curve with per-days_ahead curve and reshaped response to the frontend
HotelAnalysis interface (strategy/advance_curve/dow_breakdown/sold_out_pattern)
- /analysis/hotel/{id}/timeline: accept ?date= (was rate_date, 422) and return
flat TimelineEntry array
- /analysis/hotels: alias to hotel_id/hotel_name/date_count for the selector
- strategy pcts default 0 (frontend calls .toFixed), added peak_months
- Market View badge now shows +/-% vs our rate instead of 100-index
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
This commit is contained in:
parent
ea9484e3fa
commit
349236cd7e
2 changed files with 108 additions and 97 deletions
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@ -33,9 +33,9 @@ async def list_analysis_hotels(
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params["tier"] = tier
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result = await db.execute(
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text(f"""
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SELECT h.id, h.name, h.tier, h.star_rating, h.review_score,
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h.booking_com_url,
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COUNT(DISTINCT r.rate_date) AS scraped_dates,
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SELECT h.id AS hotel_id, h.name AS hotel_name, h.tier,
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h.star_rating, h.review_score, h.booking_com_url,
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COUNT(DISTINCT r.rate_date) AS date_count,
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MAX(r.scraped_at) AS last_scraped
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FROM booking_com_hotels h
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LEFT JOIN booking_com_rates r ON r.hotel_id = h.id
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@ -60,54 +60,45 @@ async def analyse_hotel(
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):
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require_cap(user, "rate_analysis")
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async with AsyncSessionLocal() as db:
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# Hotel info
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# Hotel must exist
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hotel_row = await db.execute(
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text("SELECT id, name, tier, star_rating, booking_com_url FROM booking_com_hotels WHERE id = :id"),
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text("SELECT id FROM booking_com_hotels WHERE id = :id"),
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{"id": hotel_id}
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)
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hotel = hotel_row.mappings().fetchone()
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if not hotel:
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if not hotel_row.fetchone():
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from fastapi import HTTPException
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raise HTTPException(status_code=404, detail="Hotel not found")
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# Advance purchase curve
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params = {"hid": hotel_id, "from_date": from_date, "to_date": to_date}
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# Advance purchase curve — avg price per days-ahead-of-stay
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apc_result = await db.execute(
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text("""
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SELECT
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CASE
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WHEN (rate_date - scraped_at::date) <= 7 THEN '0-7d'
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WHEN (rate_date - scraped_at::date) <= 29 THEN '8-29d'
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WHEN (rate_date - scraped_at::date) <= 89 THEN '30-89d'
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ELSE '90+d'
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END AS lead_bucket,
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ROUND(AVG(rate_gross)::numeric, 2) AS avg_rate,
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(rate_date - scraped_at::date) AS days_ahead,
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ROUND(AVG(rate_gross)::numeric, 2) AS avg_price,
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COUNT(*) AS sample_count
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FROM booking_com_rates
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WHERE hotel_id = :hid
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AND rate_date BETWEEN :from_date AND :to_date
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AND availability_status = 'available'
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AND rate_gross IS NOT NULL
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GROUP BY lead_bucket
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ORDER BY
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CASE lead_bucket
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WHEN '0-7d' THEN 1
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WHEN '8-29d' THEN 2
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WHEN '30-89d' THEN 3
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ELSE 4
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END
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AND (rate_date - scraped_at::date) >= 0
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GROUP BY days_ahead
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ORDER BY days_ahead
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"""),
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{"hid": hotel_id, "from_date": from_date, "to_date": to_date}
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params
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)
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advance_purchase_curve = [dict(r) for r in apc_result.mappings().all()]
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advance_curve = [dict(r) for r in apc_result.mappings().all()]
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# Day-of-week averages (latest scrape per date)
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# Day-of-week averages (latest scrape per date), Mon=0 … Sun=6
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dow_result = await db.execute(
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text("""
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SELECT
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EXTRACT(DOW FROM rate_date)::int AS dow,
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TO_CHAR(rate_date, 'Dy') AS dow_label,
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ROUND(AVG(rate_gross)::numeric, 2) AS avg_rate,
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COUNT(DISTINCT rate_date) AS date_count
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(EXTRACT(ISODOW FROM rate_date)::int - 1) AS dow,
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TO_CHAR(rate_date, 'Dy') AS dow_name,
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ROUND(AVG(rate_gross)::numeric, 2) AS avg_price,
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COUNT(DISTINCT rate_date) AS count
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FROM (
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-- Latest row per date first, THEN filter: a 'not_listed'
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-- latest row must drop the date, not resurface an older rate
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@ -120,48 +111,41 @@ async def analyse_hotel(
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) latest
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WHERE availability_status = 'available'
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AND rate_gross IS NOT NULL
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GROUP BY dow, dow_label
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GROUP BY dow, dow_name
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ORDER BY dow
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"""),
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{"hid": hotel_id, "from_date": from_date, "to_date": to_date}
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params
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)
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dow_analysis = [dict(r) for r in dow_result.mappings().all()]
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dow_breakdown = [dict(r) for r in dow_result.mappings().all()]
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# Sold-out pattern by day-of-week
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sold_out_result = await db.execute(
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# Latest status + rate per stay date (sold-out pattern, peak months)
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latest_result = await db.execute(
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text("""
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SELECT
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EXTRACT(DOW FROM rate_date)::int AS dow,
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TO_CHAR(rate_date, 'Dy') AS dow_label,
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COUNT(DISTINCT rate_date) AS total_dates,
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COUNT(DISTINCT rate_date) FILTER (
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WHERE availability_status = 'sold_out'
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) AS sold_out_dates
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FROM (
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SELECT DISTINCT ON (rate_date)
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rate_date, availability_status
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FROM booking_com_rates
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WHERE hotel_id = :hid
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AND rate_date BETWEEN :from_date AND :to_date
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ORDER BY rate_date, scraped_at DESC
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) latest
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GROUP BY dow, dow_label
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ORDER BY dow
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SELECT DISTINCT ON (rate_date)
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rate_date, rate_gross, availability_status
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FROM booking_com_rates
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WHERE hotel_id = :hid
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AND rate_date BETWEEN :from_date AND :to_date
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ORDER BY rate_date, scraped_at DESC
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"""),
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{"hid": hotel_id, "from_date": from_date, "to_date": to_date}
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params
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)
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sold_out_pattern = [dict(r) for r in sold_out_result.mappings().all()]
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latest_rows = latest_result.mappings().all()
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sold_out_pattern = [
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{
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"stay_date": str(r["rate_date"]),
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"sold_out_pct": 100.0 if r["availability_status"] == "sold_out" else 0.0,
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}
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for r in latest_rows
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]
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# Strategy summary
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strategy = _compute_strategy(advance_purchase_curve, dow_analysis, sold_out_pattern)
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strategy = _compute_strategy(advance_curve, dow_breakdown, latest_rows)
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return {
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"hotel": dict(hotel),
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"date_range": {"from": str(from_date), "to": str(to_date)},
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"advance_purchase_curve": advance_purchase_curve,
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"dow_analysis": dow_analysis,
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"strategy": strategy,
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"advance_curve": advance_curve,
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"dow_breakdown": dow_breakdown,
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"sold_out_pattern": sold_out_pattern,
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"strategy_summary": strategy,
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}
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@ -170,7 +154,7 @@ async def analyse_hotel(
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@router.get("/hotel/{hotel_id}/timeline")
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async def rate_timeline(
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hotel_id: int,
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rate_date: date = Query(...),
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rate_date: date = Query(..., alias="date"),
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user=Depends(get_current_user)
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):
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require_cap(user, "rate_analysis")
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@ -182,20 +166,25 @@ async def rate_timeline(
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rate_gross,
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availability_status,
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rooms_left,
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room_type,
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(rate_date - scraped_at::date) AS days_out
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room_type
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FROM booking_com_rates
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WHERE hotel_id = :hid AND rate_date = :rd
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ORDER BY scraped_at ASC
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"""),
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{"hid": hotel_id, "rd": rate_date}
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)
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rows = result.mappings().all()
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return {
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"hotel_id": hotel_id,
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"rate_date": str(rate_date),
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"timeline": [dict(r) for r in rows],
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}
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return [
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{
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"scraped_at": r["scraped_at"].isoformat() if r["scraped_at"] else None,
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"room_id": r["room_type"] or "cheapest",
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"rate_id": "",
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"room_label": r["room_type"] or "Cheapest rate",
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"rate_label": r["availability_status"],
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"price_incl": float(r["rate_gross"]) if r["rate_gross"] is not None else None,
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"availability": r["rooms_left"] or 0,
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}
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for r in result.mappings().all()
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]
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# ─── Own vs Competitor comparison ────────────────────────────────────────────
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@ -281,39 +270,60 @@ async def rate_comparison(
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# ─── Strategy computation helper ─────────────────────────────────────────────
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def _compute_strategy(apc: list, dow: list, sold_out: list) -> dict:
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# Advance discount — compare 0-7d vs 30-89d
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rates_by_bucket = {r["lead_bucket"]: float(r["avg_rate"]) for r in apc if r.get("avg_rate")}
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advance_discount_pct = None
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if "0-7d" in rates_by_bucket and "30-89d" in rates_by_bucket:
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close_in = rates_by_bucket["0-7d"]
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far_out = rates_by_bucket["30-89d"]
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if far_out > 0:
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# Positive = closes in higher (scarcity premium); negative = discount for advance
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advance_discount_pct = round((close_in - far_out) / far_out * 100, 1)
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def _compute_strategy(advance_curve: list, dow_breakdown: list, latest_rows: list) -> dict:
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"""Shape matches the frontend StrategyLabel interface — pcts must never be null."""
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# Weekend premium — Fri(5)+Sat(6) vs Mon(1)–Thu(4)
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rates_by_dow = {r["dow"]: float(r["avg_rate"]) for r in dow if r.get("avg_rate")}
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weekend_premium_pct = None
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weekend_rates = [rates_by_dow[d] for d in [5, 6] if d in rates_by_dow]
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weekday_rates = [rates_by_dow[d] for d in [1, 2, 3, 4] if d in rates_by_dow]
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def _weighted_avg(points):
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total = sum(p["sample_count"] for p in points)
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if not total:
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return None
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return sum(float(p["avg_price"]) * p["sample_count"] for p in points) / total
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# Advance discount — close-in (0-7d) vs far-out (30d+)
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close_in = _weighted_avg([p for p in advance_curve if p["days_ahead"] <= 7])
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far_out = _weighted_avg([p for p in advance_curve if p["days_ahead"] >= 30])
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advance_discount_pct = 0.0
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has_advance_data = close_in is not None and far_out is not None and far_out > 0
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if has_advance_data:
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# Positive = closes in higher (scarcity premium); negative = discount for advance
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advance_discount_pct = round((close_in - far_out) / far_out * 100, 1)
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# Weekend premium — Fri(4)-Sun(6) vs Mon(0)-Thu(3), dow is Mon=0 … Sun=6
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rates_by_dow = {r["dow"]: float(r["avg_price"]) for r in dow_breakdown if r.get("avg_price")}
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weekend_premium_pct = 0.0
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weekend_rates = [rates_by_dow[d] for d in [4, 5, 6] if d in rates_by_dow]
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weekday_rates = [rates_by_dow[d] for d in [0, 1, 2, 3] if d in rates_by_dow]
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if weekend_rates and weekday_rates:
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avg_wk = sum(weekend_rates) / len(weekend_rates)
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avg_wd = sum(weekday_rates) / len(weekday_rates)
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if avg_wd > 0:
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weekend_premium_pct = round((avg_wk - avg_wd) / avg_wd * 100, 1)
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# Sold-out rate
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total_dates = sum(r["total_dates"] for r in sold_out)
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sold_out_dates = sum(r["sold_out_dates"] for r in sold_out)
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sold_out_rate_pct = round(sold_out_dates / total_dates * 100, 1) if total_dates > 0 else None
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# Sold-out rate — % of stay dates whose latest status is sold_out
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sold_out_rate_pct = 0.0
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if latest_rows:
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sold_out_dates = sum(1 for r in latest_rows if r["availability_status"] == "sold_out")
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sold_out_rate_pct = round(sold_out_dates / len(latest_rows) * 100, 1)
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# Peak months — months averaging >10% above the overall average
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monthly: dict = {}
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for r in latest_rows:
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if r["availability_status"] == "available" and r["rate_gross"] is not None:
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key = (r["rate_date"].month, r["rate_date"].strftime("%b"))
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monthly.setdefault(key, []).append(float(r["rate_gross"]))
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peak_months = []
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if monthly:
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overall = sum(sum(v) for v in monthly.values()) / sum(len(v) for v in monthly.values())
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peak_months = [
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label for (num, label), v in sorted(monthly.items())
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if sum(v) / len(v) > overall * 1.10
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]
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# Strategy label
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label = "Mixed / insufficient data"
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if advance_discount_pct is not None:
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if has_advance_data:
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if advance_discount_pct <= -5:
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label = "Advance-booking discounter"
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elif advance_discount_pct >= 8 and (sold_out_rate_pct or 0) >= 10:
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elif advance_discount_pct >= 8 and sold_out_rate_pct >= 10:
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label = "Yield manager (scarcity-driven)"
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elif advance_discount_pct >= 3:
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label = "Flat-rate / hold-firm strategy"
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@ -321,8 +331,9 @@ def _compute_strategy(apc: list, dow: list, sold_out: list) -> dict:
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label = "Stable pricing"
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return {
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"label": label,
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"advance_discount_pct": advance_discount_pct,
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"weekend_premium_pct": weekend_premium_pct,
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"avg_sold_out_rate_pct": sold_out_rate_pct,
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"strategy_label": label,
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"peak_months": peak_months,
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}
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