Add GAMes table and daily margin table in Slack

- GAMes: new Airtable table aggregating daily fco_ metrics (coste, ingreso_sum, ingreso_lxp, leads, leads_lake)
- run.py: accumulate fco_ daily aggregate and write to GAMes each run
- slack_reporter.py: replace sparkline with daily margin % table (Sumatorio + LeadsxPPL per day)
- backfill_games_mayo.py: populated GAMes with all 17 existing May days

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

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",
@@ -478,6 +479,25 @@ 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) -> None:
+        self.games.update(record_id, {"MetricasDiarias": metricas_json})
+
+    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.")

run.py

@@ -219,6 +219,7 @@ 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}
     ayer        = datetime.now() - timedelta(days=1)
     dia_hoy     = ayer.strftime("%d")
     cambio_mes  = ayer.month != datetime.now().month
@@ -306,6 +307,15 @@ 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
         metricas_updates.append({
             "airtable_id": campaign["airtable_id"],
             "metricas_json": json.dumps(md, ensure_ascii=False),
@@ -355,6 +365,16 @@ 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()}
+        at.update_games_metricas(games_rid, json.dumps(games_md, ensure_ascii=False))
+        print("  ✓ GAMes actualizado.")
+
     # Snapshot diario: ConvLeadsLakeMesFinal + ConvLeadsLakeMesGrupo
     # PMX con companion Search → Grupo = conversiones Google (ya calculado en leads_grupo)
     final_leads_data = [

slack_reporter.py

@@ -151,6 +151,42 @@ 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, "ingreso_lxp": 0.0}
+            daily_totals[d]["coste"]       += vals.get("coste", 0)
+            daily_totals[d]["ingreso_sum"] += vals.get("ingreso", 0)
+            daily_totals[d]["ingreso_lxp"] += vals.get("leads_lake", 0) * ppl
+
+    margin_table_block = None
+    if daily_totals and not primer_dia_mes:
+        rows = ["Día   Sumatorio  LeadsxPPL"]
+        for d in sorted(daily_totals):
+            coste   = daily_totals[d]["coste"]
+            ing_sum = daily_totals[d]["ingreso_sum"]
+            ing_lxp = daily_totals[d]["ingreso_lxp"]
+            pct_sum = round((ing_sum - coste) / ing_sum * 100, 1) if ing_sum > 0 else 0.0
+            pct_lxp = round((ing_lxp - coste) / ing_lxp * 100, 1) if ing_lxp > 0 else 0.0
+            s_sum = ("+" if pct_sum >= 0 else "") + f"{pct_sum:.1f}%"
+            s_lxp = ("+" if pct_lxp >= 0 else "") + f"{pct_lxp:.1f}%"
+            rows.append(f"{d:02d}    {s_sum:>9}  {s_lxp:>9}")
+        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 +268,10 @@ 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",