Add daily metrics snapshot (MetricasDiarias) to GACampaignMes

Each optimizer run writes yesterday's cost, revenue (conversions x PPL)
and margin per campaign as a JSON entry keyed by day number into the
MetricasDiarias field. Yesterday is used because Google Ads data for
the current day is not yet available.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

airtable_client.py

@@ -1,5 +1,6 @@
 from pyairtable import Api
 from datetime import datetime
+import json
 import config
 
 
@@ -307,6 +308,7 @@ class AirtableClient:
                 "cpa_maximo": float(f.get("CPAMax") or 0),
                 "margen_objetivo": float(f.get("MargenObjetivo") or 0),
                 "conv_leads_lake_mes": int(f.get("ConvLeadsLakeMes") or 0),
+                "metricas_diarias": f.get("MetricasDiarias") or "{}",
             })
         return result
 
@@ -377,6 +379,18 @@ class AirtableClient:
         for i in range(0, len(batch), 10):
             self.gacampaignmes.batch_update(batch[i:i+10])
 
+    def batch_update_metricas_diarias(self, updates: list[dict]) -> None:
+        """
+        Actualiza MetricasDiarias en GACampaignMes.
+        updates: lista de {airtable_id, metricas_json (str)}
+        """
+        batch = [
+            {"id": u["airtable_id"], "fields": {"MetricasDiarias": u["metricas_json"]}}
+            for u in updates
+        ]
+        for i in range(0, len(batch), 10):
+            self.gacampaignmes.batch_update(batch[i:i+10])
+
     def batch_update_gacampaignmes_advice(self, updates: list[tuple]) -> None:
         """
         Actualiza en lote los campos 'Consejo', 'Criticidad' y 'Log' de GACampaignMes.

google_ads_client.py

@@ -1,5 +1,6 @@
 from google.ads.googleads.client import GoogleAdsClient as GAdsClient
 from google.ads.googleads.errors import GoogleAdsException
+from datetime import datetime
 import config
 
 
@@ -69,6 +70,36 @@ class GoogleAdsClient:
             print(f"  ❌ Error obteniendo métricas mensuales: {e}")
         return result
 
+    def get_yesterday_metrics_all(self) -> dict:
+        """
+        Devuelve métricas del día anterior para TODAS las campañas en una sola query.
+        Retorna dict {campaign_id: {conversions, cost}}.
+        """
+        from datetime import timedelta
+        yesterday = (datetime.now() - timedelta(days=1)).strftime("%Y-%m-%d")
+        ga_service = self.client.get_service("GoogleAdsService")
+        query = f"""
+            SELECT
+                campaign.id,
+                metrics.conversions,
+                metrics.cost_micros
+            FROM campaign
+            WHERE campaign.status != 'REMOVED'
+              AND segments.date = '{yesterday}'
+        """
+        result = {}
+        try:
+            response = ga_service.search(customer_id=self.customer_id, query=query)
+            for row in response:
+                cid = str(row.campaign.id)
+                result[cid] = {
+                    "conversions": row.metrics.conversions,
+                    "cost": row.metrics.cost_micros / 1_000_000,
+                }
+        except GoogleAdsException as e:
+            print(f"  ❌ Error obteniendo métricas de hoy: {e}")
+        return result
+
     def get_campaign_metrics(self, campaign_id: str) -> dict:
         """Métricas del mes en curso para una campaña concreta (acumulado mensual)."""
         ga_service = self.client.get_service("GoogleAdsService")

run.py

@@ -2,6 +2,7 @@ import sys
 import io
 import os
 import re
+import json
 sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding="utf-8", line_buffering=True)
 
 from airtable_client import AirtableClient
@@ -97,6 +98,7 @@ def run():
     print("→ Sincronizando campañas desde Google Ads...")
     google_campaigns = gads.get_all_campaigns()
     monthly_metrics = gads.get_monthly_metrics_all()
+    today_metrics = gads.get_yesterday_metrics_all()
     print("  Calculando PPL y CapTotalMes...")
     ppl_lookup, cap_lookup = at.build_campaign_lookups()
     sync_result = at.sync_campaigns_from_google_ads(google_campaigns, monthly_metrics, ppl_lookup, cap_lookup)
@@ -194,6 +196,7 @@ def run():
             "metrics": metrics,
             "analysis": analysis,
             "decision": decision,
+            "today_metrics": today_metrics.get(cid, {}),
         })
 
     # Actualizar status en GACampaignMes y Google Ads Campaigns
@@ -211,16 +214,20 @@ def run():
 
     # === SEGUNDA PASADA: imprimir en orden + aplicar decisiones ===
     resumen = []
-    advice_updates = []   # (gcm_record_id, consejo, criticidad) para batch update final
+    advice_updates = []        # (gcm_record_id, consejo, criticidad) para batch update final
+    metricas_updates = []      # {airtable_id, metricas_json} para MetricasDiarias
+    from datetime import timedelta
+    dia_hoy = (datetime.now() - timedelta(days=1)).strftime("%d")
     last_priority = -1
 
     for item in collected:
-        campaign = item["campaign"]
-        leads    = item["leads"]
-        metrics  = item["metrics"]
-        analysis = item["analysis"]
-        decision = item["decision"]
-        cid      = campaign["google_campaign_id"]
+        campaign     = item["campaign"]
+        leads        = item["leads"]
+        metrics      = item["metrics"]
+        analysis     = item["analysis"]
+        decision     = item["decision"]
+        today_m      = item["today_metrics"]
+        cid          = campaign["google_campaign_id"]
 
         p = _priority(item)
         if p != last_priority:
@@ -283,6 +290,21 @@ def run():
         elif course_num in courses_with_leadform:
             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"
 
+        # Métricas diarias: coste hoy, ingreso (conversiones × PPL) y margen
+        coste_hoy   = round(today_m.get("cost", 0), 2)
+        conv_hoy    = today_m.get("conversions", 0)
+        ingreso_hoy = round(conv_hoy * campaign["ppl"], 2)
+        margen_hoy  = round(ingreso_hoy - coste_hoy, 2)
+        try:
+            md = json.loads(campaign["metricas_diarias"])
+        except (json.JSONDecodeError, TypeError):
+            md = {}
+        md[dia_hoy] = {"coste": coste_hoy, "ingreso": ingreso_hoy, "margen": margen_hoy}
+        metricas_updates.append({
+            "airtable_id": campaign["airtable_id"],
+            "metricas_json": json.dumps(md, ensure_ascii=False),
+        })
+
         advice_updates.append((
             campaign["airtable_id"],
             decision.get("consejo", ""),
@@ -310,6 +332,12 @@ def run():
         at.batch_update_gacampaignmes_advice(advice_updates)
         print("  ✓ Consejos y criticidad guardados.")
 
+    # Guardar métricas diarias en MetricasDiarias
+    if metricas_updates:
+        print(f"→ Actualizando MetricasDiarias ({len(metricas_updates)} registros)...")
+        at.batch_update_metricas_diarias(metricas_updates)
+        print("  ✓ MetricasDiarias actualizado.")
+
     # Snapshot diario: ConvLeadsLakeMesFinal + ConvLeadsLakeMesGrupo
     # PMX con companion Search → Grupo = conversiones Google (ya calculado en leads_grupo)
     final_leads_data = [