José Manuel Gómez
a94c18c13c
- agent.py: portfolio_daily_analysis() for daily Slack block, weekly_strategic_analysis() for deep weekly report - run.py: call portfolio analysis before Slack send - slack_reporter.py: add strategic diagnosis block at end of daily report - weekly_report.py: standalone weekly report script - .github/workflows/weekly.yml: runs Mondays at 9am (CEST) Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
444 lines
18 KiB
Python
444 lines
18 KiB
Python
import sys
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import io
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import os
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import re
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import json
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sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding="utf-8", line_buffering=True)
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from airtable_client import AirtableClient
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from google_ads_client import GoogleAdsClient
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from analyzer import analyze
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from agent import decide, portfolio_daily_analysis
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from optimizer import apply_decision
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from slack_reporter import build_and_send
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import config
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from datetime import datetime, timedelta
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class Tee:
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"""Escribe simultáneamente en consola y en archivo de log."""
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def __init__(self, filepath):
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os.makedirs(os.path.dirname(filepath), exist_ok=True)
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self._file = open(filepath, "w", encoding="utf-8")
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self._stdout = sys.stdout
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def write(self, data):
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self._stdout.write(data)
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self._file.write(data)
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def flush(self):
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self._stdout.flush()
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if not self._file.closed:
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self._file.flush()
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def close(self):
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self._file.close()
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ICONOS = {
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"PAUSAR": "⛔",
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"SPRINT": "🚀",
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"ACELERAR": "📈",
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"FRENAR": "📉",
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"EN_RITMO": "✅",
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}
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ACCION_ICONOS = {
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"PAUSAR": "⛔",
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"AUMENTAR_PRESUPUESTO": "📈",
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"REDUCIR_PRESUPUESTO": "📉",
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"MANTENER": "✅",
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}
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# Google Ads customer ID (sin guiones) para construir enlaces directos
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_CUSTOMER_ID = config.GOOGLE_ADS_LOGIN_CUSTOMER_ID.replace("-", "")
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def _ads_link(campaign_id: str) -> str:
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return f"https://ads.google.com/aw/campaigns?campaignId={campaign_id}&__c={_CUSTOMER_ID}"
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def _priority(item: dict) -> int:
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"""
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0 — Crítica: urgencia PAUSAR o SPRINT
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1 — No crítica: accion cambia presupuesto pero urgencia no es crítica
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2 — Mantener: accion MANTENER
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"""
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urgencia = item["analysis"]["urgencia"]
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accion = item["decision"]["accion"]
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if urgencia in ("PAUSAR", "SPRINT"):
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return 0
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if accion != "MANTENER":
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return 1
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return 2
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SECCION_LABELS = {
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0: "ACCIONES CRÍTICAS (PAUSAR / AUMENTAR)",
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1: "ACCIONES NO CRÍTICAS",
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2: "SIN CAMBIOS (MANTENER)",
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}
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CRITICIDAD_MAP = {
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0: "Crítico",
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1: "Peligro",
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2: "Mantener",
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}
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def run():
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print(f"\n{'='*55}")
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print(f" LEADS OPTIMIZER — {datetime.now().strftime('%d/%m/%Y %H:%M')}")
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print(f" Modo: {'DRY RUN (sin cambios)' if config.DRY_RUN else '⚡ PRODUCCIÓN'}")
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print(f"{'='*55}\n")
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at = AirtableClient()
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gads = GoogleAdsClient()
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# Sincronizar catálogo de campañas desde Google Ads → Airtable
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print("→ Sincronizando campañas desde Google Ads...")
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google_campaigns = gads.get_all_campaigns()
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monthly_metrics = gads.get_monthly_metrics_all()
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today_metrics = gads.get_yesterday_metrics_all()
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_ayer_date = (datetime.now() - timedelta(days=1))
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leads_yesterday = at.get_leads_by_campaign_on_date(_ayer_date.strftime("%Y-%m-%d"))
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print(" Calculando PPL y CapTotalMes...")
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ppl_lookup, cap_lookup = at.build_campaign_lookups()
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sync_result = at.sync_campaigns_from_google_ads(google_campaigns, monthly_metrics, ppl_lookup, cap_lookup)
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if sync_result["created"]:
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print(f" ✅ Campañas nuevas importadas ({len(sync_result['created'])}):")
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for c in sync_result["created"]:
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print(f" + [{c['id']}] {c['name']} → {c['status']}")
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if sync_result["updated"]:
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print(f" 🔄 Campañas actualizadas ({len(sync_result['updated'])}):")
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for c in sync_result["updated"]:
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for field, val in c["changes"].items():
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print(f" ~ [{c['id']}] {c['name']} | {field}: '{val['antes']}' → '{val['ahora']}'")
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if not sync_result["created"] and not sync_result["updated"]:
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print(" ✓ Sin cambios en el catálogo.")
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# Sincronizar GACampaignMes (campañas con actividad este mes)
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print(" Sincronizando GACampaignMes...")
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gcm_result = at.sync_gacampaignmes(
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google_campaigns, monthly_metrics, ppl_lookup, cap_lookup,
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sync_result["at_by_gid"]
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)
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print(f" ✓ GACampaignMes: {gcm_result['created']} nuevas, {gcm_result['updated']} actualizadas.")
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print()
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campaigns = at.get_active_gacampaignmes()
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print(f"→ {len(campaigns)} campañas con actividad este mes")
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print("→ Analizando...\n")
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# Detección anticipada de cursos con Search Y PMX simultáneos
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def _course_num(name: str) -> str | None:
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m = re.search(r'fco_(?:search|pmx)_(\d+)', name, re.IGNORECASE)
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return m.group(1) if m else None
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course_types_pre: dict[str, set] = {}
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for c in campaigns:
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num = _course_num(c["curso"])
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if num:
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course_types_pre.setdefault(num, set())
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if "pmx" in c["curso"].lower():
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course_types_pre[num].add("pmx")
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elif "search" in c["curso"].lower():
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course_types_pre[num].add("search")
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courses_with_both = {
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num for num, types in course_types_pre.items()
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if "pmx" in types and "search" in types
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}
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# Detección anticipada de cursos con campaña _leadform activa
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courses_with_leadform = {
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_course_num(c["curso"])
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for c in campaigns
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if "_leadform" in c["curso"].lower() and _course_num(c["curso"])
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}
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# === PRIMERA PASADA: recopilar datos de todas las campañas ===
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collected = []
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skipped = []
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status_updates = [] # (gcm_id, campaign_at_id, google_status) para batch update
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for campaign in campaigns:
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cid = campaign["google_campaign_id"]
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leads, lead_ids = at.get_leads_this_month_gads(cid, campaign["curso"])
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at.update_gacampaignmes_leads_lake(campaign["airtable_id"], lead_ids)
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metrics = gads.get_campaign_metrics(cid)
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if not metrics:
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skipped.append(f"[{cid}] {campaign['curso']}")
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continue
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# Recopilar status para actualizar ambas tablas al final del pase
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google_status = metrics.get("status", "")
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if google_status:
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status_updates.append((
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campaign["airtable_id"],
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campaign.get("campaign_at_id", ""),
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google_status,
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))
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# Para PMX con companion Search: usar conversiones Google como leads de análisis
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course_num = _course_num(campaign["curso"])
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is_pmx_with_companion = (
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"pmx" in campaign["curso"].lower()
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and course_num in courses_with_both
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)
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leads_grupo = int(metrics.get("conversions", 0)) if is_pmx_with_companion else leads
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analysis = analyze(campaign, leads_grupo, metrics)
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decision = decide(analysis)
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collected.append({
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"campaign": campaign,
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"leads": leads,
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"leads_grupo": leads_grupo,
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"metrics": metrics,
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"analysis": analysis,
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"decision": decision,
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"today_metrics": today_metrics.get(cid, {}),
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})
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# Actualizar status en GACampaignMes y Google Ads Campaigns
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if status_updates:
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at.batch_update_status(status_updates)
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if skipped:
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print(f" ⚠️ {len(skipped)} campañas sin métricas omitidas:")
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for s in skipped:
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print(f" · {s}")
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print()
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# Ordenar: 0=críticas → 1=no críticas → 2=mantener
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collected.sort(key=_priority)
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# === SEGUNDA PASADA: imprimir en orden + aplicar decisiones ===
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resumen = []
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advice_updates = [] # (gcm_record_id, consejo, criticidad) para batch update final
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metricas_updates = [] # {airtable_id, metricas_json} para MetricasDiarias
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games_agg: dict = {} # dia_hoy → {coste, ingreso_sum, ingreso_lxp, leads, leads_lake}
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games_mes = {"coste_mes": 0.0, "conv_mes": 0, "ppl_leads": 0.0, "leads_total": 0}
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ayer = datetime.now() - timedelta(days=1)
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dia_hoy = ayer.strftime("%d")
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cambio_mes = ayer.month != datetime.now().month
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last_priority = -1
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for item in collected:
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campaign = item["campaign"]
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leads = item["leads"]
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metrics = item["metrics"]
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analysis = item["analysis"]
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decision = item["decision"]
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today_m = item["today_metrics"]
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cid = campaign["google_campaign_id"]
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p = _priority(item)
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if p != last_priority:
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print(f"\n{'━'*55}")
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print(f" {SECCION_LABELS[p]}")
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print(f"{'━'*55}")
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last_priority = p
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icono = ICONOS.get(analysis["urgencia"], "❓")
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accion_icono = ACCION_ICONOS.get(decision["accion"], "")
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print(f"{'─'*55}")
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print(f"📚 {campaign['curso']}")
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print(f" ID: {cid} | PPL: {campaign['ppl']}€ | Cap: {campaign['capping_mensual']} leads")
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print(f" 🔗 {_ads_link(cid)}")
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print(f" Leads mes: {leads}/{campaign['capping_mensual']} "
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f"({analysis['ratio_leads']*100:.0f}% cap) | "
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f"Ratio mes: {analysis['ratio_mes']*100:.0f}%")
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print(f" CPA actual: {analysis['cpa_actual']}€ | "
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f"CPA máximo: {analysis['cpa_maximo']}€ | "
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f"Margen: {analysis['margen']*100:.0f}%")
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print(f" Urgencia: {icono} {analysis['urgencia']} | "
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f"Rentable: {'✅' if analysis['rentable'] else '❌'}")
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if analysis["alerta_tracking"]:
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print(f" 🚨 ALERTA TRACKING: {analysis['discrepancia_tracking']} leads de diferencia "
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f"entre Airtable ({campaign['conv_leads_lake_mes']}) y Google Ads ({int(analysis['conversiones_google'])})")
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print(f" Decisión: {accion_icono} {decision['accion']} "
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f"(confianza: {decision['confianza']*100:.0f}%)")
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print(f" Justificación: {decision['justificacion']}")
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if decision.get("consejo"):
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print(f" 💡 Consejo: {decision['consejo']}")
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if decision.get("alerta"):
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print(f" 🚨 {decision['alerta']}")
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apply_decision(campaign, decision, metrics, gads)
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conv_google = int(analysis["conversiones_google"])
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discrepancia_fresh = abs(conv_google - leads)
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if discrepancia_fresh > 10:
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log_text = (
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f"⚠️ DISCREPANCIA: {leads} leads Airtable vs {conv_google} conv Google Ads "
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f"(Δ {discrepancia_fresh})"
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)
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else:
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log_text = f"OK: {leads} leads Airtable | {conv_google} conv Google Ads"
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# Nota de reatribución PMX para campañas Search con companion PMX del mismo curso
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course_num = _course_num(campaign["curso"])
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if "search" in campaign["curso"].lower() and course_num in courses_with_both:
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log_text += " | ⚠️ PMX ATTRIBUTION: campaña Search con companion PMX activo — parte de las conversiones de Google pueden estar reatribuidas a la campaña PMX"
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# Nota leadform: los leads se capturan en Google sin pasar por la web
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is_leadform = "_leadform" in campaign["curso"].lower()
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if is_leadform:
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log_text += " | ℹ️ LEADFORM: leads capturados directamente en Google (sin visitar la web) — no llegan a Airtable"
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elif course_num in courses_with_leadform:
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log_text += " | ⚠️ LEADFORM COMPANION: existe una campaña _leadform activa para este curso — parte de las conversiones de Google pueden provenir de leads capturados directamente en Google"
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# Métricas diarias: coste hoy, ingreso (conversiones × PPL) y margen
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coste_hoy = round(today_m.get("cost", 0), 2)
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conv_hoy = today_m.get("conversions", 0)
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ingreso_hoy = round(conv_hoy * campaign["ppl"], 2)
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margen_hoy = round(ingreso_hoy - coste_hoy, 2)
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try:
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md = json.loads(campaign["metricas_diarias"])
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except (json.JSONDecodeError, TypeError):
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md = {}
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leads_lake_hoy = leads_yesterday.get(cid, 0)
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md[dia_hoy] = {"coste": coste_hoy, "ingreso": ingreso_hoy, "margen": margen_hoy, "leads": int(conv_hoy), "leads_lake": leads_lake_hoy}
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campaign["metricas_diarias"] = json.dumps(md, ensure_ascii=False)
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if campaign["curso"].lower().startswith("fco_"):
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if dia_hoy not in games_agg:
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games_agg[dia_hoy] = {"coste": 0.0, "ingreso_sum": 0.0, "ingreso_lxp": 0.0, "leads": 0, "leads_lake": 0}
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games_agg[dia_hoy]["coste"] += coste_hoy
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games_agg[dia_hoy]["ingreso_sum"] += ingreso_hoy
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games_agg[dia_hoy]["ingreso_lxp"] += leads_lake_hoy * campaign["ppl"]
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games_agg[dia_hoy]["leads"] += int(conv_hoy)
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games_agg[dia_hoy]["leads_lake"] += leads_lake_hoy
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# Totales mes (fuente: Google Ads acumulado mensual)
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games_mes["coste_mes"] += metrics.get("cost", 0)
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games_mes["conv_mes"] += int(analysis["conversiones_google"])
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# PPLMedio ponderado por leads Airtable
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games_mes["ppl_leads"] += campaign["ppl"] * leads
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games_mes["leads_total"] += leads
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metricas_updates.append({
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"airtable_id": campaign["airtable_id"],
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"metricas_json": json.dumps(md, ensure_ascii=False),
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})
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advice_updates.append((
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campaign["airtable_id"],
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decision.get("consejo", ""),
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CRITICIDAD_MAP[p],
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log_text,
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))
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resumen.append({
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"curso": campaign["curso"],
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"urgencia": analysis["urgencia"],
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"accion": decision["accion"],
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"leads": f"{leads}/{campaign['capping_mensual']}",
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"cpa": analysis["cpa_actual"],
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"margen": f"{analysis['margen']*100:.0f}%",
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"consejo": decision.get("consejo", ""),
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"link": _ads_link(cid),
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"prioridad": p,
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})
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print()
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# Guardar consejos y criticidad en GACampaignMes
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if advice_updates:
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print(f"→ Guardando consejos y criticidad en GACampaignMes ({len(advice_updates)} registros)...")
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at.batch_update_gacampaignmes_advice(advice_updates)
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print(" ✓ Consejos y criticidad guardados.")
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# Guardar métricas diarias en MetricasDiarias
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if metricas_updates:
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print(f"→ Actualizando MetricasDiarias ({len(metricas_updates)} registros)...")
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if cambio_mes:
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# Ayer pertenece al mes anterior: redirigir escritura al GACampaignMes correcto
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prev_map = at.get_gcm_id_map_for_month(ayer.month, ayer.year)
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for u in metricas_updates:
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gid = next(
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(item["campaign"]["google_campaign_id"]
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for item in collected if item["campaign"]["airtable_id"] == u["airtable_id"]),
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None,
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)
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if gid and gid in prev_map:
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u["airtable_id"] = prev_map[gid]
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at.batch_update_metricas_diarias(metricas_updates)
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print(" ✓ MetricasDiarias actualizado.")
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# Actualizar GAMes con el agregado diario fco_
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if games_agg:
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print("→ Actualizando GAMes...")
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games_rid = at.get_or_create_games_record(ayer.month, ayer.year)
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games_md = at.get_games_metricas(games_rid)
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for d, vals in games_agg.items():
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games_md[d] = {k: round(v, 2) if isinstance(v, float) else v for k, v in vals.items()}
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coste_mes = round(games_mes["coste_mes"], 2)
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conv_mes = games_mes["conv_mes"]
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ppl_medio = round(games_mes["ppl_leads"] / games_mes["leads_total"], 2) if games_mes["leads_total"] > 0 else 0.0
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cpa_medio = round(coste_mes / conv_mes, 2) if conv_mes > 0 else 0.0
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totales = {
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"CosteMes": coste_mes,
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"ConvMes": conv_mes,
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"PPLMedio": ppl_medio,
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"CPAMedio": cpa_medio,
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}
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at.update_games_metricas(games_rid, json.dumps(games_md, ensure_ascii=False), totales)
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print(" ✓ GAMes actualizado.")
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# Snapshot diario: ConvLeadsLakeMesFinal + ConvLeadsLakeMesGrupo
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# PMX con companion Search → Grupo = conversiones Google (ya calculado en leads_grupo)
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final_leads_data = [
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{
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"airtable_id": item["campaign"]["airtable_id"],
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"conv_leads_lake_mes": item["leads"],
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"conv_leads_lake_mes_grupo": item["leads_grupo"],
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}
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for item in collected
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]
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if final_leads_data:
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print(f"→ Actualizando ConvLeadsLakeMesFinal ({len(final_leads_data)} registros)...")
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at.batch_update_gacampaignmes_final_leads(final_leads_data)
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print(" ✓ ConvLeadsLakeMesFinal actualizado.")
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# Resumen final ordenado por prioridad (ya está ordenado)
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print(f"{'='*55}")
|
||
print("RESUMEN FINAL")
|
||
print(f"{'='*55}")
|
||
last_p = -1
|
||
for r in resumen:
|
||
if r["prioridad"] != last_p:
|
||
print(f"\n --- {SECCION_LABELS[r['prioridad']]} ---")
|
||
last_p = r["prioridad"]
|
||
print(f" {r['curso'][:35]:<35} | {r['urgencia']:<12} | {r['accion']:<25} | {r['leads']} leads | {r['margen']} margen")
|
||
print(f" {'':35} {r['link']}")
|
||
if r["consejo"]:
|
||
print(f" {'':35} 💡 {r['consejo']}")
|
||
print()
|
||
|
||
# Enviar resumen a Slack
|
||
print("→ Generando análisis estratégico del portfolio...")
|
||
portfolio_text = portfolio_daily_analysis(collected)
|
||
print("→ Enviando resumen a Slack...")
|
||
prev_month_metricas = at.get_metricas_diarias_prev_month() if (cambio_mes or datetime.now().day <= 5) else {}
|
||
build_and_send(collected, config.DRY_RUN, prev_month_metricas, portfolio_text)
|
||
print(" ✓ Resumen enviado a Slack.")
|
||
|
||
|
||
if __name__ == "__main__":
|
||
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
|
||
log_path = os.path.join("logs", f"{timestamp}.log")
|
||
tee = Tee(log_path)
|
||
sys.stdout = tee
|
||
try:
|
||
run()
|
||
finally:
|
||
tee.close()
|
||
print(f"\nLog guardado en: {log_path}", file=tee._stdout)
|