José Manuel Gómez
9239e2f67f
Ports meta-optimizer's Meta Ads execution/approval/creative-analysis layer (agent.py, meta_ads_client.py, baserow_client.py, slack_notifier.py, approval_server.py) and replaces the per-vertical CPL model with the PPL + monthly-capping-per-course model already used by leads-optimizer, via a new airtable_client.py that shares Cursos/Familias/CentroCurso/ CursoMes/Leads Lake with that project and adds Meta Ads Campaigns / MetaCampaignMes alongside its Google Ads Campaigns / GACampaignMes.
197 lines
8.4 KiB
Python
197 lines
8.4 KiB
Python
"""
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Backfill: genera snapshots históricos con análisis Claude para un rango de fechas.
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Usa ventana de 1 día (no 3d/7d, los datos históricos ya están fijados) y
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reconstruye el capping/PPL/familia y los leads acumulados del curso tal como
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estaban en cada fecha histórica (as_of_date), no el estado actual.
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Uso:
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python backfill.py # mes en curso → ayer
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python backfill.py --from 2026-06-01 --to 2026-06-04
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python backfill.py --skip-existing # no reprocesa días ya guardados
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"""
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import sys
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import io
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import argparse
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sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding="utf-8", line_buffering=True)
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from datetime import datetime, timedelta
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import config
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from meta_ads_client import MetaAdsClient
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from airtable_client import AirtableClient, extract_cursoid
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from agent import decide, analyze_unit
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from baserow_client import BaserowClient
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import analyzer
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_ACTION_MAP = {
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"PAUSE": "PAUSE", "REDUCE_BUDGET": "REDUCE_BUDGET",
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"INCREASE_BUDGET": "INCREASE_BUDGET", "MAINTAIN": "MAINTAIN",
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"PAUSAR": "PAUSE", "REDUCIR_PRESUPUESTO": "REDUCE_BUDGET",
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"AUMENTAR_PRESUPUESTO": "INCREASE_BUDGET", "MANTENER": "MAINTAIN",
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}
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def run_backfill(date_from: str, date_to: str, skip_existing: bool = False):
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meta = MetaAdsClient()
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baserow = BaserowClient()
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airtable = AirtableClient()
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# Build date list
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d = datetime.strptime(date_from, "%Y-%m-%d")
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d_end = datetime.strptime(date_to, "%Y-%m-%d")
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dates = []
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while d <= d_end:
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dates.append(d.strftime("%Y-%m-%d"))
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d += timedelta(days=1)
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print(f"\n{'='*60}")
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print(f" BACKFILL FORMACIÓN {date_from} → {date_to} ({len(dates)} días)")
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print(f"{'='*60}\n")
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total_saved = 0
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total_skip = 0
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_lookups_cache: dict = {} # {mes_año: (ppl_lookup, cap_lookup, familia_lookup)}
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for run_date in dates:
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print(f"\n── {run_date} ───────────────────────────────────────────────")
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mes_key = run_date[:7]
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if mes_key not in _lookups_cache:
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_lookups_cache[mes_key] = airtable.build_campaign_lookups(as_of_date=run_date)
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ppl_lookup, cap_lookup, familia_lookup = _lookups_cache[mes_key]
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# Pre-load existing snapshots for this date if skip_existing
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existing_names: set = set()
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if skip_existing:
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try:
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for r in baserow.get_snapshots_for_date(run_date):
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existing_names.add(r.get("campaign_name", ""))
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except Exception:
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pass
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campaign_metrics = meta.get_campaign_metrics(run_date, run_date)
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if not campaign_metrics:
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print(" Sin campañas con gasto.")
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continue
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print(f" {len(campaign_metrics)} campañas activas.")
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adset_bids_cache: dict = {}
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for cid, metrics in campaign_metrics.items():
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camp_name = metrics["name"]
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if skip_existing and camp_name in existing_names:
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print(f" SKIP {camp_name[:55]}")
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total_skip += 1
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continue
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cursoid = extract_cursoid(camp_name) or ""
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familia = familia_lookup.get(cursoid, "Sin familia")
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ppl = ppl_lookup.get(cursoid, 0)
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cap = cap_lookup.get(cursoid, 0)
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cpa_max = round(ppl * 0.70, 2)
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leads_entregados, _ = airtable.get_leads_this_month_meta(camp_name, as_of_date=run_date)
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print(f" {camp_name[:55]}")
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print(f" Spend {metrics['spend']}€ Leads {metrics['leads']} PPL {ppl}€ "
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f"CPAmax {cpa_max}€ Leads mes {leads_entregados}/{cap or '∞'}")
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campaign_config = {
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"curso": camp_name, "meta_campaign_id": cid, "ppl": ppl,
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"cpa_maximo": cpa_max, "capping_mensual": cap,
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"conv_leads_lake_mes": leads_entregados,
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}
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ads_metrics = {
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"spend": metrics["spend"], "leads": metrics["leads"],
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"ctr": metrics["ctr"], "clicks": metrics["clicks"], "status": "ACTIVE",
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}
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analysis = analyzer.analyze(campaign_config, leads_entregados, ads_metrics)
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try:
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decision = decide(analysis)
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action_type = _ACTION_MAP.get(decision.get("action", "MAINTAIN"), "MAINTAIN")
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except Exception as e:
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print(f" ERROR decide: {e}")
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decision = {"action": "MAINTAIN", "justification": "", "parameter": 1.0}
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action_type = "MAINTAIN"
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print(f" Urgencia: {analysis['urgencia']} Decision: {action_type} — "
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f"{(decision.get('justification') or '')[:70]}")
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# ── Claude: adsets ──────────────────────────────────────────────
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adsets_detail = []
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try:
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for as_m in meta.get_adset_metrics(cid, run_date, run_date)[:5]:
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result = analyze_unit(as_m, "adset")
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adsets_detail.append({**as_m, **result})
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print(f" [Adset] {as_m['name'][:45]} — {result.get('evaluacion','')[:50]}")
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except Exception as e:
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print(f" ERROR adsets: {e}")
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if cid not in adset_bids_cache:
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try:
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adset_bids_cache[cid] = meta.get_adset_bid_configs(cid)
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except Exception:
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adset_bids_cache[cid] = {}
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for adset in adsets_detail:
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b = adset_bids_cache[cid].get(adset["id"], {})
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adset["cost_cap_eur"] = b.get("cost_cap_eur")
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adset["bid_strategy"] = b.get("bid_strategy", "")
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# ── Claude: anuncios ────────────────────────────────────────────
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ads_detail = []
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try:
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for ad_m in meta.get_ad_metrics(cid, run_date, run_date)[:5]:
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ad_m["ppl"] = ppl
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ad_m["cpa_maximo"] = cpa_max
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result = analyze_unit(ad_m, "ad")
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ads_detail.append({**ad_m, **result})
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print(f" [Ad] {ad_m['name'][:45]} — {result.get('evaluacion','')[:50]}")
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except Exception as e:
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print(f" ERROR ads: {e}")
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# margin en € (mismo proxy que usa run.py): leads*PPL - gasto
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margin = round(metrics["leads"] * ppl - metrics["spend"], 2)
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try:
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baserow.save_daily_snapshot({
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"run_date": run_date,
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"campaign_id": cid,
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"campaign_name": camp_name,
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"familia": familia,
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"spend": metrics["spend"],
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"leads": metrics["leads"],
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"cpl": metrics["cpl"],
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"margin": margin,
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"action_type": action_type,
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"justification": decision.get("justification") or "",
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"adsets": adsets_detail,
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"ads": ads_detail,
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})
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print(" ✓ Snapshot guardado")
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total_saved += 1
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except Exception as e:
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print(f" ERROR snapshot: {e}")
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print(f"\n{'='*60}")
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print(f" Backfill completo. Guardados: {total_saved} Saltados: {total_skip}")
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print(f"{'='*60}\n")
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if __name__ == "__main__":
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now = datetime.now()
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default_from = f"{now.year}-{now.month:02d}-01"
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default_to = (now - timedelta(days=1)).strftime("%Y-%m-%d")
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parser = argparse.ArgumentParser(description="Backfill Meta Optimizer Formación snapshots")
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parser.add_argument("--from", dest="date_from", default=default_from,
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help=f"Fecha inicio YYYY-MM-DD (default: {default_from})")
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parser.add_argument("--to", dest="date_to", default=default_to,
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help=f"Fecha fin YYYY-MM-DD (default: {default_to})")
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parser.add_argument("--skip-existing", action="store_true",
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help="No reprocesa campañas que ya tienen snapshot ese día")
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args = parser.parse_args()
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run_backfill(args.date_from, args.date_to, args.skip_existing)
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