Downgrade Claude calls to sonnet-4-6 for cost efficiency

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

.github/workflows/weekly.yml

@@ -0,0 +1,35 @@
+name: Weekly Strategic Report
+
+on:
+  schedule:
+    - cron: '0 7 * * 1'   # Lunes 9:00 AM hora española (CEST/UTC+2)
+  workflow_dispatch:
+
+jobs:
+  run:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.12'
+
+      - name: Install dependencies
+        run: pip install -r requirements.txt
+
+      - name: Run weekly report
+        env:
+          AIRTABLE_TOKEN: ${{ secrets.AIRTABLE_TOKEN }}
+          AIRTABLE_BASE_ID: ${{ secrets.AIRTABLE_BASE_ID }}
+          GOOGLE_ADS_DEVELOPER_TOKEN: ${{ secrets.GOOGLE_ADS_DEVELOPER_TOKEN }}
+          GOOGLE_ADS_CLIENT_ID: ${{ secrets.GOOGLE_ADS_CLIENT_ID }}
+          GOOGLE_ADS_CLIENT_SECRET: ${{ secrets.GOOGLE_ADS_CLIENT_SECRET }}
+          GOOGLE_ADS_REFRESH_TOKEN: ${{ secrets.GOOGLE_ADS_REFRESH_TOKEN }}
+          GOOGLE_ADS_LOGIN_CUSTOMER_ID: ${{ secrets.GOOGLE_ADS_LOGIN_CUSTOMER_ID }}
+          ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
+          SLACK_WEBHOOK_URL: ${{ secrets.SLACK_WEBHOOK_URL }}
+        run: python weekly_report.py

README.md

@@ -38,6 +38,8 @@ export GOOGLE_ADS_REFRESH_TOKEN=xxxx
 export GOOGLE_ADS_LOGIN_CUSTOMER_ID=1234567890  # sin guiones
 
 export ANTHROPIC_API_KEY=sk-ant-xxxx
+
+export SLACK_WEBHOOK_URL=xxx
 ```
 
 ---

agent.py

@@ -1,9 +1,23 @@
 import json
+from datetime import datetime
 import anthropic
 import config
 
 client = anthropic.Anthropic(api_key=config.ANTHROPIC_API_KEY)
 
+PORTFOLIO_SYSTEM = """
+Eres un experto en marketing de performance para una agencia de generación de leads en formación.
+Recibes datos agregados del portfolio de campañas de Google Ads (solo campañas fco_).
+Responde siempre en español, de forma concisa y accionable. Sin markdown, sin bullet symbols especiales, usa guiones simples (-).
+"""
+
+WEEKLY_SYSTEM = """
+Eres un consultor senior de marketing de performance especializado en generación de leads para formación.
+Recibes el análisis semanal del portfolio de campañas de Google Ads (solo campañas fco_).
+Tu análisis debe ser estratégico, comparando la semana actual con la anterior, identificando tendencias y proponiendo acciones concretas.
+Responde siempre en español. Sin markdown, sin bullet symbols especiales, usa guiones simples (-).
+"""
+
 SYSTEM_PROMPT = """
 Eres un agente experto en optimización de campañas de generación de leads para centros de formación.
 Cada campaña corresponde a un curso concreto con un PPL (precio por lead) fijo acordado con los centros compradores.
@@ -48,9 +62,172 @@ El campo consejo debe ser accionable y específico: qué revisar, qué cambiar,
 """
 
 
+def _classify_type(curso: str) -> str:
+    c = curso.lower()
+    if "_leadform" in c:
+        return "leadform"
+    if "_pmx" in c or "pmx_" in c:
+        return "pmx"
+    if "search" in c:
+        return "search"
+    return "otro"
+
+
+def portfolio_daily_analysis(collected: list) -> str:
+    """Análisis estratégico diario del portfolio fco_. Devuelve texto plano para Slack."""
+    from datetime import datetime
+    now = datetime.now()
+    fco = [i for i in collected if i["campaign"]["curso"].lower().startswith("fco_")]
+
+    tipos: dict = {}
+    leadforms_detail = []
+    alertas_tracking = 0
+    campañas_perdida = 0
+
+    for item in fco:
+        t = _classify_type(item["campaign"]["curso"])
+        m = item["metrics"]
+        a = item["analysis"]
+        cost = m.get("cost", 0)
+        conv = a["conversiones_google"]
+        ppl  = item["campaign"]["ppl"]
+        rev  = a["revenue_estimado"]
+        margen_pct = round((rev - cost) / rev * 100, 1) if rev > 0 else 0.0
+
+        if t not in tipos:
+            tipos[t] = {"campañas": 0, "inversion": 0.0, "conversiones": 0, "ingreso": 0.0}
+        tipos[t]["campañas"]    += 1
+        tipos[t]["inversion"]   += cost
+        tipos[t]["conversiones"] += conv
+        tipos[t]["ingreso"]     += rev
+
+        if a.get("alerta_tracking"):
+            alertas_tracking += 1
+        if rev > 0 and cost > rev:
+            campañas_perdida += 1
+
+        if t == "leadform":
+            leadforms_detail.append({
+                "curso": item["campaign"]["curso"][:40],
+                "cpa_google": round(cost / conv, 2) if conv > 0 else None,
+                "conv_google": int(conv),
+                "conv_airtable": item["leads"],
+                "margen_pct": margen_pct,
+            })
+
+    resumen_tipos = {}
+    for t, d in tipos.items():
+        cpa = round(d["inversion"] / d["conversiones"], 2) if d["conversiones"] > 0 else None
+        ing = d["ingreso"]
+        margen = round((ing - d["inversion"]) / ing * 100, 1) if ing > 0 else 0.0
+        resumen_tipos[t] = {
+            "campañas": d["campañas"],
+            "inversion": round(d["inversion"], 2),
+            "conversiones": int(d["conversiones"]),
+            "cpa_medio": cpa,
+            "margen_pct": margen,
+        }
+
+    data = {
+        "fecha": now.strftime("%d/%m/%Y"),
+        "dia_del_mes": now.day,
+        "campañas_totales": len(fco),
+        "campañas_en_perdida": campañas_perdida,
+        "alertas_tracking": alertas_tracking,
+        "rendimiento_por_tipo": resumen_tipos,
+        "detalle_leadforms": leadforms_detail,
+    }
+
+    try:
+        response = client.messages.create(
+            model="claude-sonnet-4-6",
+            max_tokens=500,
+            system=PORTFOLIO_SYSTEM,
+            messages=[{
+                "role": "user",
+                "content": (
+                    "Analiza estos datos del portfolio y proporciona:\n"
+                    "1. Diagnóstico en 2 frases\n"
+                    "2. Problemas principales (máx 3, con guión)\n"
+                    "3. Acciones prioritarias (máx 3, muy concretas, con guión)\n"
+                    "Si hay campañas leadform, evalúa específicamente su situación.\n\n"
+                    f"{json.dumps(data, ensure_ascii=False, indent=2)}"
+                ),
+            }],
+        )
+        return response.content[0].text.strip()
+    except Exception as e:
+        return f"Error generando análisis: {e}"
+
+
+def weekly_strategic_analysis(games_md_this: dict, games_md_prev_week: dict,
+                               collected: list, mes_nombre: str) -> str:
+    """
+    Análisis estratégico semanal profundo.
+    games_md_this: MetricasDiarias de GAMes de los últimos 7 días (esta semana).
+    games_md_prev_week: MetricasDiarias de GAMes de los 7 días anteriores.
+    collected: lista de campañas del optimizer.
+    """
+    def _week_summary(md: dict) -> dict:
+        coste = ing = leads = leads_lake = 0.0
+        for v in md.values():
+            coste      += v.get("coste", 0)
+            ing        += v.get("ingreso_sum", 0)
+            leads      += v.get("leads", 0)
+            leads_lake += v.get("leads_lake", 0)
+        margen = round((ing - coste) / ing * 100, 1) if ing > 0 else 0.0
+        cpa    = round(coste / leads, 2) if leads > 0 else None
+        return {"coste": round(coste, 2), "ingreso": round(ing, 2),
+                "leads_google": int(leads), "leads_airtable": int(leads_lake),
+                "margen_pct": margen, "cpa": cpa}
+
+    fco = [i for i in collected if i["campaign"]["curso"].lower().startswith("fco_")]
+
+    # Top 5 peores por CPA del mes
+    peores = sorted(
+        [{"curso": i["campaign"]["curso"][:40],
+          "cpa": i["analysis"]["cpa_actual"],
+          "conv": int(i["analysis"]["conversiones_google"]),
+          "margen_pct": round(i["analysis"]["margen"] * 100, 1)}
+         for i in fco if i["analysis"]["cpa_actual"] > 0],
+        key=lambda x: x["cpa"], reverse=True
+    )[:5]
+
+    data = {
+        "mes": mes_nombre,
+        "semana_actual": _week_summary(games_md_this),
+        "semana_anterior": _week_summary(games_md_prev_week),
+        "top5_peor_cpa_mes": peores,
+        "leadforms_activos": sum(1 for i in fco if "_leadform" in i["campaign"]["curso"].lower()),
+        "campañas_totales": len(fco),
+    }
+
+    try:
+        response = client.messages.create(
+            model="claude-sonnet-4-6",
+            max_tokens=900,
+            system=WEEKLY_SYSTEM,
+            messages=[{
+                "role": "user",
+                "content": (
+                    "Genera el informe estratégico semanal con:\n"
+                    "1. Resumen ejecutivo (3 frases comparando esta semana con la anterior)\n"
+                    "2. Tendencias clave detectadas (máx 4, con guión)\n"
+                    "3. Situación campañas leadform y qué hacer con ellas\n"
+                    "4. Acciones estratégicas prioritarias para la próxima semana (máx 4, muy concretas, con guión)\n"
+                    "5. Una frase de conclusión sobre si el portfolio va en la dirección correcta\n\n"
+                    f"{json.dumps(data, ensure_ascii=False, indent=2)}"
+                ),
+            }],
+        )
+        return response.content[0].text.strip()
+    except Exception as e:
+        return f"Error generando análisis semanal: {e}"
+
+
 def decide(analysis: dict) -> dict:
     response = client.messages.create(
-        model="claude-sonnet-4-20250514",
+        model="claude-sonnet-4-6",
         max_tokens=400,
         system=SYSTEM_PROMPT,
         messages=[{

airtable_client.py

@@ -14,6 +14,7 @@ class AirtableClient:
         self.centrocurso = self.api.table(config.AIRTABLE_BASE_ID, "CentroCurso")
         self.cursomes = self.api.table(config.AIRTABLE_BASE_ID, "CursoMes")
         self.gacampaignmes = self.api.table(config.AIRTABLE_BASE_ID, "GACampaignMes")
+        self.games = self.api.table(config.AIRTABLE_BASE_ID, "GAMes")
 
     MESES_ES = {
         1: "Enero", 2: "Febrero", 3: "Marzo", 4: "Abril",
@@ -338,24 +339,45 @@ class AirtableClient:
     def get_leads_this_month_gads(self, campaign_id: str, campaign_name: str = "") -> tuple[int, list[str]]:
         """
         Leads del mes actual para una campaña de Google Ads.
-        Cubre tres vías de atribución:
+        Cubre cuatro vías de atribución:
           1. GACampaignID / GoogleCampaignID (leads web normales)
           2. gad_campaignid en attr_referer (UTM web)
           3. attr_referer = campaign_name con UserAgent Google-Ads-Notifications (Lead Form)
+          4. attr_cursoid = course_num AND attr_utm_source = 'pmx'|'google' (atribución por curso)
         """
         now = datetime.now()
         mes_inicio = f"{now.year}-{now.month:02d}-01"
+
         leadform_clause = (
             f"AND({{attr_referer}}='{campaign_name}',{{UserAgent del visitante}}='Google-Ads-Notifications')"
             if campaign_name else "FALSE()"
         )
+
+        # Extraer número de curso y tipo de fuente del nombre de campaña
+        curso_clause = "FALSE()"
+        m = re.search(r'fco_(?:search|pmx)_(\d+)', campaign_name, re.IGNORECASE)
+        if m:
+            course_num = m.group(1)
+            if "pmx" in campaign_name.lower() and "_leadform" not in campaign_name.lower():
+                utm_source = "pmx"
+            elif "search" in campaign_name.lower():
+                utm_source = "google"
+            else:
+                utm_source = None
+            if utm_source:
+                curso_clause = (
+                    f"AND({{attr_cursoid}}='{course_num}',"
+                    f"{{attr_utm_source}}='{utm_source}')"
+                )
+
         formula = (
             f"AND("
             f"OR("
             f"{{GACampaignID}}='{campaign_id}',"
             f"FIND(',{campaign_id},',',' & {{GoogleCampaignID}} & ','),"
             f"FIND('gad_campaignid={campaign_id}',{{attr_referer}}),"
-            f"{leadform_clause}"
+            f"{leadform_clause},"
+            f"{curso_clause}"
             f"),"
             f"{{creado}}>='{mes_inicio}'"
             f")"
@@ -478,6 +500,28 @@ class AirtableClient:
         for i in range(0, len(batch), 10):
             self.gacampaignmes.batch_update(batch[i:i+10])
 
+    # ── GAMes ──────────────────────────────────────────────────────────────── #
+
+    def get_or_create_games_record(self, mes: int, anio: int) -> str:
+        records = self.games.all(formula=f"AND({{Mes}}='{mes}',{{Año}}='{anio}')")
+        if records:
+            return records[0]["id"]
+        r = self.games.create({"Mes": str(mes), "Año": str(anio)})
+        return r["id"]
+
+    def update_games_metricas(self, record_id: str, metricas_json: str, totales: dict = None) -> None:
+        fields = {"MetricasDiarias": metricas_json}
+        if totales:
+            fields.update(totales)
+        self.games.update(record_id, fields)
+
+    def get_games_metricas(self, record_id: str) -> dict:
+        r = self.games.get(record_id)
+        try:
+            return json.loads(r["fields"].get("MetricasDiarias") or "{}")
+        except (json.JSONDecodeError, TypeError):
+            return {}
+
     def batch_update_gacampaignmes_advice(self, updates: list[tuple]) -> None:
         """
         Actualiza en lote los campos 'Consejo', 'Criticidad' y 'Log' de GACampaignMes.

backfill_games_mayo.py

@@ -0,0 +1,73 @@
+"""
+Script one-off: crea el registro de mayo 2026 en GAMes y rellena MetricasDiarias
+con el agregado diario de todas las campañas fco_ a partir de GACampaignMes.
+
+Ejecutar una sola vez:
+    python backfill_games_mayo.py
+"""
+import json
+from airtable_client import AirtableClient
+
+at = AirtableClient()
+
+MES  = 5
+ANIO = 2026
+
+print("Cargando registros GACampaignMes de mayo (campañas fco_)...")
+records = at.gacampaignmes.all(
+    formula="AND({Mes}='5',{Año}='2026')",
+    fields=["CampaignID", "MetricasDiarias", "Campaign Name (from CampaignID)"],
+)
+
+campaigns_records = at.campaigns.all(fields=["CampaignID", "PPL"])
+at_id_to_info = {
+    r["id"]: {
+        "gid": str(r["fields"].get("CampaignID", "")).strip(),
+        "ppl": float(r["fields"].get("PPL", 0) or 0),
+    }
+    for r in campaigns_records
+}
+
+# Agregar métricas por día para campañas fco_
+daily_agg: dict[str, dict] = {}
+
+for r in records:
+    at_cids = r["fields"].get("CampaignID", [])
+    if not at_cids:
+        continue
+    info = at_id_to_info.get(at_cids[0], {})
+    ppl = info.get("ppl", 0)
+
+    campaign_names = r["fields"].get("Campaign Name (from CampaignID)", [])
+    campaign_name  = (campaign_names[0] if campaign_names else "").lower()
+    if not campaign_name.startswith("fco_"):
+        continue
+
+    try:
+        md = json.loads(r["fields"].get("MetricasDiarias") or "{}")
+    except (json.JSONDecodeError, TypeError):
+        md = {}
+
+    for day_str, vals in md.items():
+        if day_str not in daily_agg:
+            daily_agg[day_str] = {"coste": 0.0, "ingreso_sum": 0.0, "ingreso_lxp": 0.0, "leads": 0, "leads_lake": 0}
+        daily_agg[day_str]["coste"]       += vals.get("coste", 0)
+        daily_agg[day_str]["ingreso_sum"] += vals.get("ingreso", 0)
+        daily_agg[day_str]["ingreso_lxp"] += vals.get("leads_lake", 0) * ppl
+        daily_agg[day_str]["leads"]       += int(vals.get("leads", 0))
+        daily_agg[day_str]["leads_lake"]  += int(vals.get("leads_lake", 0))
+
+print(f"  > Dias agregados: {sorted(daily_agg.keys())}")
+
+# Redondear
+for d in daily_agg:
+    daily_agg[d] = {k: round(v, 2) if isinstance(v, float) else v for k, v in daily_agg[d].items()}
+
+print("Creando/obteniendo registro GAMes de mayo 2026...")
+games_rid = at.get_or_create_games_record(MES, ANIO)
+print(f"  > Record ID: {games_rid}")
+
+print("Guardando MetricasDiarias en GAMes...")
+at.update_games_metricas(games_rid, json.dumps(daily_agg, ensure_ascii=False))
+
+print(f"OK Backfill GAMes completado: {len(daily_agg)} dias guardados.")

google_ads_client.py

@@ -133,6 +133,66 @@ class GoogleAdsClient:
             print(f"  ❌ Error obteniendo métricas mensuales: {e}")
         return result
 
+    def get_monthly_metrics_all(self) -> dict:
+        """
+        Métricas del mes en curso para TODAS las campañas en una sola query.
+        Retorna dict {campaign_id: {cost, conversions, clicks, impressions, ctr,
+                                    status, budget_daily, budget_resource_name, name}}.
+        """
+        ga_service = self.client.get_service("GoogleAdsService")
+        query = """
+            SELECT
+                campaign.id,
+                campaign.name,
+                campaign.status,
+                campaign_budget.amount_micros,
+                campaign_budget.resource_name,
+                metrics.cost_micros,
+                metrics.conversions,
+                metrics.clicks,
+                metrics.impressions
+            FROM campaign
+            WHERE campaign.status != 'REMOVED'
+              AND segments.date DURING THIS_MONTH
+        """
+        raw: dict = {}
+        try:
+            response = ga_service.search(customer_id=self.customer_id, query=query)
+            for row in response:
+                cid = str(row.campaign.id)
+                if cid not in raw:
+                    raw[cid] = {
+                        "name":                 row.campaign.name,
+                        "status":               row.campaign.status.name,
+                        "budget_daily":         row.campaign_budget.amount_micros / 1_000_000,
+                        "budget_resource_name": row.campaign_budget.resource_name,
+                        "cost": 0.0, "conversions": 0.0, "clicks": 0, "impressions": 0,
+                    }
+                m = row.metrics
+                raw[cid]["cost"]        += m.cost_micros / 1_000_000
+                raw[cid]["conversions"] += m.conversions
+                raw[cid]["clicks"]      += m.clicks
+                raw[cid]["impressions"] += m.impressions
+        except GoogleAdsException as e:
+            print(f"  ❌ Error obteniendo métricas mensuales bulk: {e}")
+
+        result = {}
+        for cid, d in raw.items():
+            imp = d["impressions"]
+            result[cid] = {
+                "campaign_id":          cid,
+                "name":                 d["name"],
+                "status":               d["status"],
+                "budget_daily":         round(d["budget_daily"], 2),
+                "budget_resource_name": d["budget_resource_name"],
+                "cost":                 round(d["cost"], 2),
+                "conversions":          d["conversions"],
+                "clicks":               d["clicks"],
+                "impressions":          imp,
+                "ctr":                  round(d["clicks"] / imp * 100, 2) if imp > 0 else 0.0,
+            }
+        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

@@ -8,7 +8,7 @@ sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding="utf-8", line_bufferin
 from airtable_client import AirtableClient
 from google_ads_client import GoogleAdsClient
 from analyzer import analyze
-from agent import decide
+from agent import decide, portfolio_daily_analysis
 from optimizer import apply_decision
 from slack_reporter import build_and_send
 import config
@@ -156,6 +156,15 @@ def run():
         if "_leadform" in c["curso"].lower() and _course_num(c["curso"])
     }
 
+    # Precomputar conversiones Google de cada campaña leadform por número de curso
+    leadform_conv_by_course: dict[str, int] = {}
+    for c in campaigns:
+        if "_leadform" in c["curso"].lower():
+            num = _course_num(c["curso"])
+            if num:
+                lf_conv = int(monthly_metrics.get(c["google_campaign_id"], {}).get("conversions", 0))
+                leadform_conv_by_course[num] = leadform_conv_by_course.get(num, 0) + lf_conv
+
     # === PRIMERA PASADA: recopilar datos de todas las campañas ===
     collected = []
     skipped = []
@@ -167,7 +176,7 @@ def run():
         leads, lead_ids = at.get_leads_this_month_gads(cid, campaign["curso"])
         at.update_gacampaignmes_leads_lake(campaign["airtable_id"], lead_ids)
 
-        metrics = gads.get_campaign_metrics(cid)
+        metrics = monthly_metrics.get(cid)
         if not metrics:
             skipped.append(f"[{cid}] {campaign['curso']}")
             continue
@@ -181,13 +190,20 @@ def run():
                 google_status,
             ))
 
-        # Para PMX con companion Search: usar conversiones Google como leads de análisis
         course_num = _course_num(campaign["curso"])
-        is_pmx_with_companion = (
-            "pmx" in campaign["curso"].lower()
-            and course_num in courses_with_both
-        )
-        leads_grupo = int(metrics.get("conversions", 0)) if is_pmx_with_companion else leads
+        is_pmx = "pmx" in campaign["curso"].lower() and "_leadform" not in campaign["curso"].lower()
+
+        # PMX con companion Search: conversiones Google del PMX (search fluye hacia PMX)
+        is_pmx_with_companion = is_pmx and course_num in courses_with_both
+        # PMX con companion Leadform: conv PMX + conv leadform (leads no llegan a Airtable)
+        is_pmx_with_leadform  = is_pmx and course_num in courses_with_leadform
+
+        if is_pmx_with_leadform:
+            leads_grupo = int(metrics.get("conversions", 0)) + leadform_conv_by_course.get(course_num, 0)
+        elif is_pmx_with_companion:
+            leads_grupo = int(metrics.get("conversions", 0))
+        else:
+            leads_grupo = leads
 
         analysis = analyze(campaign, leads_grupo, metrics)
         decision = decide(analysis)
@@ -200,6 +216,7 @@ def run():
             "analysis": analysis,
             "decision": decision,
             "today_metrics": today_metrics.get(cid, {}),
+            "is_pmx_with_leadform": is_pmx_with_leadform,
         })
 
     # Actualizar status en GACampaignMes y Google Ads Campaigns
@@ -219,6 +236,8 @@ def run():
     resumen = []
     advice_updates = []        # (gcm_record_id, consejo, criticidad) para batch update final
     metricas_updates = []      # {airtable_id, metricas_json} para MetricasDiarias
+    games_agg: dict = {}       # dia_hoy → {coste, ingreso_sum, ingreso_lxp, leads, leads_lake}
+    games_mes = {"coste_mes": 0.0, "conv_mes": 0, "ppl_leads": 0.0, "leads_total": 0}
     ayer        = datetime.now() - timedelta(days=1)
     dia_hoy     = ayer.strftime("%d")
     cambio_mes  = ayer.month != datetime.now().month
@@ -291,8 +310,9 @@ def run():
         is_leadform = "_leadform" in campaign["curso"].lower()
         if is_leadform:
             log_text += " | ℹ️ LEADFORM: leads capturados directamente en Google (sin visitar la web) — no llegan a Airtable"
-        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"
+        elif item.get("is_pmx_with_leadform"):
+            lf_conv = leadform_conv_by_course.get(course_num, 0)
+            log_text += f" | ℹ️ LEADFORM COMPANION: {lf_conv} conv leadform sumadas a leads_grupo para este curso"
 
         # Métricas diarias: coste hoy, ingreso (conversiones × PPL) y margen
         coste_hoy   = round(today_m.get("cost", 0), 2)
@@ -306,6 +326,21 @@ def run():
         leads_lake_hoy = leads_yesterday.get(cid, 0)
         md[dia_hoy] = {"coste": coste_hoy, "ingreso": ingreso_hoy, "margen": margen_hoy, "leads": int(conv_hoy), "leads_lake": leads_lake_hoy}
         campaign["metricas_diarias"] = json.dumps(md, ensure_ascii=False)
+
+        if campaign["curso"].lower().startswith("fco_"):
+            if dia_hoy not in games_agg:
+                games_agg[dia_hoy] = {"coste": 0.0, "ingreso_sum": 0.0, "ingreso_lxp": 0.0, "leads": 0, "leads_lake": 0}
+            games_agg[dia_hoy]["coste"]       += coste_hoy
+            games_agg[dia_hoy]["ingreso_sum"] += ingreso_hoy
+            games_agg[dia_hoy]["ingreso_lxp"] += leads_lake_hoy * campaign["ppl"]
+            games_agg[dia_hoy]["leads"]       += int(conv_hoy)
+            games_agg[dia_hoy]["leads_lake"]  += leads_lake_hoy
+            # Totales mes (fuente: Google Ads acumulado mensual)
+            games_mes["coste_mes"] += metrics.get("cost", 0)
+            games_mes["conv_mes"]  += int(analysis["conversiones_google"])
+            # PPLMedio ponderado por leads Airtable
+            games_mes["ppl_leads"]    += campaign["ppl"] * leads
+            games_mes["leads_total"]  += leads
         metricas_updates.append({
             "airtable_id": campaign["airtable_id"],
             "metricas_json": json.dumps(md, ensure_ascii=False),
@@ -355,6 +390,26 @@ def run():
         at.batch_update_metricas_diarias(metricas_updates)
         print("  ✓ MetricasDiarias actualizado.")
 
+    # Actualizar GAMes con el agregado diario fco_
+    if games_agg:
+        print("→ Actualizando GAMes...")
+        games_rid = at.get_or_create_games_record(ayer.month, ayer.year)
+        games_md  = at.get_games_metricas(games_rid)
+        for d, vals in games_agg.items():
+            games_md[d] = {k: round(v, 2) if isinstance(v, float) else v for k, v in vals.items()}
+        coste_mes = round(games_mes["coste_mes"], 2)
+        conv_mes  = games_mes["conv_mes"]
+        ppl_medio = round(games_mes["ppl_leads"] / games_mes["leads_total"], 2) if games_mes["leads_total"] > 0 else 0.0
+        cpa_medio = round(coste_mes / conv_mes, 2) if conv_mes > 0 else 0.0
+        totales = {
+            "CosteMes": coste_mes,
+            "ConvMes":  conv_mes,
+            "PPLMedio": ppl_medio,
+            "CPAMedio": cpa_medio,
+        }
+        at.update_games_metricas(games_rid, json.dumps(games_md, ensure_ascii=False), totales)
+        print("  ✓ GAMes actualizado.")
+
     # Snapshot diario: ConvLeadsLakeMesFinal + ConvLeadsLakeMesGrupo
     # PMX con companion Search → Grupo = conversiones Google (ya calculado en leads_grupo)
     final_leads_data = [
@@ -386,9 +441,11 @@ def run():
     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)
+    build_and_send(collected, config.DRY_RUN, prev_month_metricas, portfolio_text)
     print("  ✓ Resumen enviado a Slack.")
 
 

slack_reporter.py

@@ -63,7 +63,8 @@ def _curso(name: str, max_len: int = 40) -> str:
     return name[:max_len] + ("…" if len(name) > max_len else "")
 
 
-def build_and_send(collected: list, dry_run: bool, prev_month_metricas: dict = None) -> None:
+def build_and_send(collected: list, dry_run: bool, prev_month_metricas: dict = None,
+                   portfolio_analysis_text: str = None) -> None:
     if not config.SLACK_WEBHOOK_URL:
         print("  ⚠️  SLACK_WEBHOOK_URL no configurada, omitiendo envío.")
         return
@@ -91,11 +92,15 @@ def build_and_send(collected: list, dry_run: bool, prev_month_metricas: dict = N
         margen_leads_ppl = 0.0
         pct_leads_ppl    = 0.0
     else:
-        ing_sumatorio = round(sum(
-            sum(d.get("ingreso", 0) for d in _parse_metricas(item["campaign"].get("metricas_diarias", "{}")).values())
-            for item in fco
-        ), 2)
-        margen_sumatorio = round(ing_sumatorio - inv_total, 2)
+        ing_sumatorio   = 0.0
+        coste_sumatorio = 0.0
+        for item in fco:
+            for d in _parse_metricas(item["campaign"].get("metricas_diarias", "{}")).values():
+                ing_sumatorio   += d.get("ingreso", 0)
+                coste_sumatorio += d.get("coste", 0)
+        ing_sumatorio   = round(ing_sumatorio, 2)
+        coste_sumatorio = round(coste_sumatorio, 2)
+        margen_sumatorio = round(ing_sumatorio - coste_sumatorio, 2)
         margen_leads_ppl = round(ing_leads_ppl - inv_total, 2)
         pct_sumatorio    = round(margen_sumatorio / ing_sumatorio  * 100, 1) if ing_sumatorio  > 0 else 0.0
         pct_leads_ppl    = round(margen_leads_ppl / ing_leads_ppl * 100, 1) if ing_leads_ppl  > 0 else 0.0
@@ -151,6 +156,49 @@ def build_and_send(collected: list, dry_run: bool, prev_month_metricas: dict = N
     worst_month = month_rows[:TOP_N]
     best_month  = list(reversed(month_rows[-TOP_N:]))
 
+    # ── Tabla de márgenes diarios ─────────────────────────────────────────────
+    daily_totals: dict[int, dict] = {}
+    for item in fco:
+        ppl = item["campaign"].get("ppl", 0)
+        md  = _parse_metricas(item["campaign"].get("metricas_diarias", "{}"))
+        for day_str, vals in md.items():
+            try:
+                d = int(day_str)
+            except ValueError:
+                continue
+            if d not in daily_totals:
+                daily_totals[d] = {"coste": 0.0, "ingreso_sum": 0.0}
+            daily_totals[d]["coste"]       += vals.get("coste", 0)
+            daily_totals[d]["ingreso_sum"] += vals.get("ingreso", 0)
+
+    margin_table_block = None
+    if daily_totals and not primer_dia_mes:
+        rows = ["Día      Margen €    %"]
+        total_coste = total_ing = 0.0
+        for d in sorted(daily_totals):
+            coste   = daily_totals[d]["coste"]
+            ing_sum = daily_totals[d]["ingreso_sum"]
+            margen  = ing_sum - coste
+            pct     = round(margen / ing_sum * 100, 1) if ing_sum > 0 else 0.0
+            total_coste += coste
+            total_ing   += ing_sum
+            s_eur = ("+" if margen >= 0 else "") + f"{margen:,.0f}€".replace(",", ".")
+            s_pct = ("+" if pct >= 0 else "") + f"{pct:.1f}%"
+            rows.append(f"{d:02d}    {s_eur:>9}  {s_pct:>7}")
+        total_margen = total_ing - total_coste
+        total_pct    = round(total_margen / total_ing * 100, 1) if total_ing > 0 else 0.0
+        s_tot_eur = ("+" if total_margen >= 0 else "") + f"{total_margen:,.0f}€".replace(",", ".")
+        s_tot_pct = ("+" if total_pct >= 0 else "") + f"{total_pct:.1f}%"
+        rows.append("─" * 24)
+        rows.append(f"TOT  {s_tot_eur:>9}  {s_tot_pct:>7}")
+        margin_table_block = {
+            "type": "section",
+            "text": {
+                "type": "mrkdwn",
+                "text": f"📅 *MÁRGENES POR DÍA — {MESES_ES[now.month].upper()}*\n```\n" + "\n".join(rows) + "\n```",
+            },
+        }
+
     # ── Alertas ──────────────────────────────────────────────────────────────
     alerts = [
         r for r in month_rows
@@ -232,6 +280,10 @@ def build_and_send(collected: list, dry_run: bool, prev_month_metricas: dict = N
             )
         return "\n".join(lines)
 
+    if margin_table_block:
+        blocks.append({"type": "divider"})
+        blocks.append(margin_table_block)
+
     blocks.append({"type": "divider"})
     blocks.append({
         "type": "section",
@@ -264,6 +316,16 @@ def build_and_send(collected: list, dry_run: bool, prev_month_metricas: dict = N
             ],
         })
 
+    if portfolio_analysis_text:
+        blocks.append({"type": "divider"})
+        blocks.append({
+            "type": "section",
+            "text": {
+                "type": "mrkdwn",
+                "text": f"🤖 *DIAGNÓSTICO ESTRATÉGICO*\n{portfolio_analysis_text}",
+            },
+        })
+
     payload = {"blocks": blocks}
     try:
         resp = requests.post(config.SLACK_WEBHOOK_URL, json=payload, timeout=10)

weekly_report.py

@@ -0,0 +1,134 @@
+"""
+Informe estratégico semanal de Leads Optimizer.
+Se ejecuta los lunes via GitHub Actions y envía un análisis profundo a Slack.
+"""
+import json
+import sys
+import os
+import requests
+from datetime import datetime, timedelta
+
+import config
+from airtable_client import AirtableClient
+from google_ads_client import GoogleAdsClient
+from agent import weekly_strategic_analysis
+from analyzer import analyze
+from slack_reporter import MESES_ES
+
+MESES_ES_LOCAL = MESES_ES
+
+
+def _get_week_days(offset_weeks: int = 0) -> list[str]:
+    """Devuelve los 7 días YYYY-MM-DD de la semana offset_weeks atrás (0=esta, 1=anterior)."""
+    today = datetime.now().date()
+    monday = today - timedelta(days=today.weekday()) - timedelta(weeks=offset_weeks)
+    return [(monday + timedelta(days=i)).strftime("%Y-%m-%d") for i in range(7)]
+
+
+def _filter_games_md_by_days(games_md: dict, days: list[str]) -> dict:
+    """Filtra MetricasDiarias de GAMes a los días indicados (formato 'DD')."""
+    day_keys = {d[8:10] for d in days}
+    return {k: v for k, v in games_md.items() if k in day_keys}
+
+
+def run_weekly():
+    now = datetime.now()
+    print(f"\n{'='*55}")
+    print(f"  INFORME SEMANAL — {now.strftime('%d/%m/%Y %H:%M')}")
+    print(f"{'='*55}\n")
+
+    at   = AirtableClient()
+    gads = GoogleAdsClient()
+
+    # Obtener MetricasDiarias de GAMes del mes en curso
+    mes, anio = now.month, now.year
+    print("Cargando GAMes...")
+    games_rid = at.get_or_create_games_record(mes, anio)
+    games_md  = at.get_games_metricas(games_rid)
+
+    # Calcular semanas
+    this_week_days = _get_week_days(0)
+    prev_week_days = _get_week_days(1)
+
+    games_this = _filter_games_md_by_days(games_md, this_week_days)
+    games_prev = _filter_games_md_by_days(games_md, prev_week_days)
+
+    # Si la semana anterior cruza con el mes pasado, intentar cargar ese GAMes también
+    prev_month_days = [d for d in prev_week_days if d[:7] != now.strftime("%Y-%m")]
+    if prev_month_days:
+        prev_mes = mes - 1 if mes > 1 else 12
+        prev_anio = anio if mes > 1 else anio - 1
+        prev_records = at.games.all(formula=f"AND({{Mes}}='{prev_mes}',{{Año}}='{prev_anio}')")
+        if prev_records:
+            try:
+                prev_md = json.loads(prev_records[0]["fields"].get("MetricasDiarias") or "{}")
+            except Exception:
+                prev_md = {}
+            extra = _filter_games_md_by_days(prev_md, prev_month_days)
+            games_prev.update(extra)
+
+    # Cargar métricas de campañas activas para el análisis de portfolio
+    print("Cargando campañas activas...")
+    campaigns = at.get_active_gacampaignmes()
+    monthly_metrics = gads.get_monthly_metrics_all()
+
+    collected = []
+    for campaign in campaigns:
+        cid = campaign["google_campaign_id"]
+        metrics = monthly_metrics.get(cid, {"cost": 0, "conversions": 0, "clicks": 0,
+                                            "impressions": 0, "ctr": 0, "budget_daily": 0,
+                                            "status": "UNKNOWN"})
+        leads = campaign.get("conv_leads_lake_mes", 0)
+        analysis = analyze(campaign, leads, metrics)
+        collected.append({"campaign": campaign, "metrics": metrics,
+                          "analysis": analysis, "leads": leads})
+
+    print("Generando análisis estratégico semanal con IA...")
+    mes_nombre = MESES_ES_LOCAL.get(mes, str(mes))
+    analysis_text = weekly_strategic_analysis(games_this, games_prev, collected, mes_nombre)
+
+    # Construir mensaje Slack
+    if not config.SLACK_WEBHOOK_URL:
+        print("SLACK_WEBHOOK_URL no configurada.")
+        print("\n--- ANÁLISIS ---\n")
+        print(analysis_text)
+        return
+
+    this_range = f"{this_week_days[0][8:10]}/{this_week_days[0][5:7]}–{this_week_days[-1][8:10]}/{this_week_days[-1][5:7]}"
+    prev_range = f"{prev_week_days[0][8:10]}/{prev_week_days[0][5:7]}–{prev_week_days[-1][8:10]}/{prev_week_days[-1][5:7]}"
+
+    blocks = [
+        {
+            "type": "header",
+            "text": {"type": "plain_text",
+                     "text": f"INFORME SEMANAL — {now.strftime('%d/%m/%Y')}"},
+        },
+        {
+            "type": "section",
+            "text": {
+                "type": "mrkdwn",
+                "text": f"_Semana actual: {this_range}  |  Semana anterior: {prev_range}_",
+            },
+        },
+        {"type": "divider"},
+        {
+            "type": "section",
+            "text": {
+                "type": "mrkdwn",
+                "text": f"🤖 *ANÁLISIS ESTRATÉGICO SEMANAL*\n{analysis_text}",
+            },
+        },
+    ]
+
+    try:
+        resp = requests.post(config.SLACK_WEBHOOK_URL, json={"blocks": blocks}, timeout=10)
+        if resp.status_code != 200:
+            print(f"⚠️ Slack respondió {resp.status_code}: {resp.text[:200]}")
+        else:
+            print("✓ Informe semanal enviado a Slack.")
+    except Exception as e:
+        print(f"⚠️ Error enviando a Slack: {e}")
+
+
+if __name__ == "__main__":
+    run_weekly()