Fix Slack monthly summary: correct investment, conversions and margin calculations

- Use CosteMes and ConvMes from all previous month fco_ campaigns (not just
  those active in current month) to avoid missing paused campaigns
- Show day-1 cierre block with Google Ads data labeled explicitly
- Show current month as 0 on day 1 (no data yet)
- Fetch Campaign Name in get_metricas_diarias_prev_month for fco_ filtering
- Fetch prev month data for days 1-5 (last 5 days can span two months)

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

airtable_client.py

@@ -379,6 +379,59 @@ class AirtableClient:
         for i in range(0, len(batch), 10):
             self.gacampaignmes.batch_update(batch[i:i+10])
 
+    def get_metricas_diarias_prev_month(self) -> dict:
+        """
+        Devuelve {google_campaign_id: {metricas, coste_mes, conv_mes}} del mes anterior.
+        Usado en el reporter de Slack cuando hay cambio de mes.
+        """
+        now = datetime.now()
+        prev_month = now.month - 1 if now.month > 1 else 12
+        prev_year  = now.year if now.month > 1 else now.year - 1
+        formula = f"AND({{Mes}}='{prev_month}',{{Año}}='{prev_year}')"
+        records = self.gacampaignmes.all(
+            formula=formula,
+            fields=["CampaignID", "MetricasDiarias", "CosteMes", "ConvMes",
+                    "Campaign Name (from CampaignID)"],
+        )
+        campaigns_records = self.campaigns.all(fields=["CampaignID"])
+        at_id_to_gid = {r["id"]: str(r["fields"].get("CampaignID", "")).strip() for r in campaigns_records}
+        result = {}
+        for r in records:
+            at_cids  = r["fields"].get("CampaignID", [])
+            metricas = r["fields"].get("MetricasDiarias") or "{}"
+            coste    = float(r["fields"].get("CosteMes") or 0)
+            conv     = float(r["fields"].get("ConvMes") or 0)
+            name_list = r["fields"].get("Campaign Name (from CampaignID)", [])
+            name = name_list[0] if name_list else ""
+            if at_cids:
+                gid = at_id_to_gid.get(at_cids[0], "")
+                if gid:
+                    result[gid] = {
+                        "metricas":  metricas,
+                        "coste_mes": coste,
+                        "conv_mes":  conv,
+                        "nombre":    name,
+                    }
+        return result
+
+    def get_gcm_id_map_for_month(self, mes: int, anio: int) -> dict:
+        """
+        Devuelve {google_campaign_id: gcm_record_id} para el mes/año indicado.
+        Útil para escribir MetricasDiarias en el mes correcto cuando hay cambio de mes.
+        """
+        formula = f"AND({{Mes}}='{mes}',{{Año}}='{anio}')"
+        records = self.gacampaignmes.all(formula=formula, fields=["CampaignID"])
+        campaigns_records = self.campaigns.all(fields=["CampaignID"])
+        at_id_to_gid = {r["id"]: str(r["fields"].get("CampaignID", "")).strip() for r in campaigns_records}
+        result = {}
+        for r in records:
+            at_cids = r["fields"].get("CampaignID", [])
+            if at_cids:
+                gid = at_id_to_gid.get(at_cids[0], "")
+                if gid:
+                    result[gid] = r["id"]
+        return result
+
     def batch_update_metricas_diarias(self, updates: list[dict]) -> None:
         """
         Actualiza MetricasDiarias en GACampaignMes.

run.py

@@ -218,7 +218,9 @@ def run():
     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")
+    ayer        = datetime.now() - timedelta(days=1)
+    dia_hoy     = ayer.strftime("%d")
+    cambio_mes  = ayer.month != datetime.now().month
     last_priority = -1
 
     for item in collected:
@@ -337,6 +339,17 @@ def run():
     # Guardar métricas diarias en MetricasDiarias
     if metricas_updates:
         print(f"→ Actualizando MetricasDiarias ({len(metricas_updates)} registros)...")
+        if cambio_mes:
+            # Ayer pertenece al mes anterior: redirigir escritura al GACampaignMes correcto
+            prev_map = at.get_gcm_id_map_for_month(ayer.month, ayer.year)
+            for u in metricas_updates:
+                gid = next(
+                    (item["campaign"]["google_campaign_id"]
+                     for item in collected if item["campaign"]["airtable_id"] == u["airtable_id"]),
+                    None,
+                )
+                if gid and gid in prev_map:
+                    u["airtable_id"] = prev_map[gid]
         at.batch_update_metricas_diarias(metricas_updates)
         print("  ✓ MetricasDiarias actualizado.")
 
@@ -372,7 +385,8 @@ def run():
 
     # Enviar resumen a Slack
     print("→ Enviando resumen a Slack...")
-    build_and_send(collected, config.DRY_RUN)
+    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)
     print("  ✓ Resumen enviado a Slack.")
 
 

slack_reporter.py

@@ -1,7 +1,7 @@
 import json
 import requests
 import config
-from datetime import datetime
+from datetime import datetime, timedelta, date
 
 
 ALERT_LOSS_EUR = 200    # pérdida absoluta > 200€ → alerta
@@ -9,6 +9,13 @@ ALERT_MARGIN_PCT = -50  # margen % < -50% → alerta
 TOP_N = 5               # campañas a mostrar en rankings
 
 
+MESES_ES = {
+    1: "Enero", 2: "Febrero", 3: "Marzo", 4: "Abril",
+    5: "Mayo", 6: "Junio", 7: "Julio", 8: "Agosto",
+    9: "Septiembre", 10: "Octubre", 11: "Noviembre", 12: "Diciembre",
+}
+
+
 def _parse_metricas(metricas_json: str) -> dict:
     try:
         return json.loads(metricas_json) if metricas_json else {}
@@ -16,13 +23,34 @@ def _parse_metricas(metricas_json: str) -> dict:
         return {}
 
 
-def _last_n_days_sum(md: dict, n: int) -> dict:
-    days = sorted(md.keys())[-n:]
+def _last_n_days_combined(current_md: dict, prev_md: dict, now: datetime, n: int) -> dict:
+    """
+    Combina MetricasDiarias del mes actual y del anterior para calcular los últimos n días.
+    Las claves son strings de día ("01"…"31"); para ordenar por fecha real se añade el mes.
+    """
+    entries = []
+
+    prev_month = now.month - 1 if now.month > 1 else 12
+    prev_year  = now.year if now.month > 1 else now.year - 1
+    for day_str, vals in prev_md.items():
+        try:
+            entries.append((date(prev_year, prev_month, int(day_str)), vals))
+        except ValueError:
+            pass
+
+    for day_str, vals in current_md.items():
+        try:
+            entries.append((date(now.year, now.month, int(day_str)), vals))
+        except ValueError:
+            pass
+
+    entries.sort(key=lambda x: x[0])
+    last_n = entries[-n:]
     return {
-        "coste":   round(sum(md[d].get("coste",   0) for d in days), 2),
-        "ingreso": round(sum(md[d].get("ingreso", 0) for d in days), 2),
-        "margen":  round(sum(md[d].get("margen",  0) for d in days), 2),
-        "n_days":  len(days),
+        "coste":   round(sum(v.get("coste",   0) for _, v in last_n), 2),
+        "ingreso": round(sum(v.get("ingreso", 0) for _, v in last_n), 2),
+        "margen":  round(sum(v.get("margen",  0) for _, v in last_n), 2),
+        "n_days":  len(last_n),
     }
 
 
@@ -31,38 +59,70 @@ def _fmt_eur(v: float) -> str:
     return f"{sign}{v:,.0f}€".replace(",", ".")
 
 
-def _curso(name: str, max_len: int = 42) -> str:
+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) -> None:
+def build_and_send(collected: list, dry_run: bool, prev_month_metricas: dict = None) -> None:
     if not config.SLACK_WEBHOOK_URL:
         print("  ⚠️  SLACK_WEBHOOK_URL no configurada, omitiendo envío.")
         return
 
-    now = datetime.now()
+    now  = datetime.now()
+    ayer = now - timedelta(days=1)
+    cambio_mes    = ayer.month != now.month
+    mes_sumatorio = MESES_ES[ayer.month] if cambio_mes else None
+    prev_md_map   = prev_month_metricas or {}
 
     fco = [item for item in collected if item["campaign"]["curso"].lower().startswith("fco_")]
 
-    # ── Totales del mes ──────────────────────────────────────────────────────
-    inv_total = round(sum(item["metrics"].get("cost", 0) for item in fco), 2)
+    primer_dia_mes = now.day == 1
 
-    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)
+    # ── Totales del mes en curso ─────────────────────────────────────────────
+    inv_total  = round(sum(item["metrics"].get("cost", 0) for item in fco), 2)
+    conv_total = int(sum(item["analysis"]["conversiones_google"] for item in fco))
     ing_leads_ppl = round(sum(item["analysis"]["revenue_estimado"] for item in fco), 2)
 
-    margen_sumatorio  = round(ing_sumatorio  - inv_total, 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
+    if primer_dia_mes:
+        # Mes recién empezado: no hay datos de sumatorio para el mes en curso
+        ing_sumatorio    = 0.0
+        margen_sumatorio = 0.0
+        pct_sumatorio    = 0.0
+        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)
+        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
 
-    # ── Últimos 5 días (desde MetricasDiarias) ───────────────────────────────
+    # ── Totales del mes anterior (solo día 1) ────────────────────────────────
+    if primer_dia_mes and prev_md_map:
+        # Iterar sobre TODAS las campañas del mes anterior filtrando fco_
+        # (no solo las activas en el mes en curso, para no perder campañas pausadas)
+        prev_fco = [v for v in prev_md_map.values() if v.get("nombre", "").lower().startswith("fco_")]
+        prev_inv  = round(sum(v.get("coste_mes", 0) for v in prev_fco), 2)
+        prev_conv = int(sum(v.get("conv_mes", 0) for v in prev_fco))
+        prev_ing  = round(sum(
+            sum(d.get("ingreso", 0) for d in _parse_metricas(v.get("metricas", "{}")).values())
+            for v in prev_fco
+        ), 2)
+        prev_margen = round(prev_ing - prev_inv, 2)
+        prev_pct    = round(prev_margen / prev_ing * 100, 1) if prev_ing > 0 else 0.0
+    else:
+        prev_inv = prev_conv = prev_ing = prev_margen = prev_pct = None
+
+    # ── Últimos 5 días (combinando meses si hay cambio de mes) ───────────────
     last5_rows = []
     for item in fco:
-        md = _parse_metricas(item["campaign"].get("metricas_diarias", "{}"))
-        s = _last_n_days_sum(md, 5)
+        gid        = item["campaign"]["google_campaign_id"]
+        current_md = _parse_metricas(item["campaign"].get("metricas_diarias", "{}"))
+        prev_md    = _parse_metricas(prev_md_map.get(gid, {}).get("metricas", "{}"))
+        s = _last_n_days_combined(current_md, prev_md, now, 5)
         if s["n_days"] == 0:
             continue
         last5_rows.append({
@@ -80,12 +140,10 @@ def build_and_send(collected: list, dry_run: bool) -> None:
     for item in fco:
         cost = item["metrics"].get("cost", 0)
         rev  = item["analysis"]["revenue_estimado"]
-        loss = round(rev - cost, 2)
-        pct  = round(item["analysis"]["margen"] * 100, 1)
         month_rows.append({
             "curso":      item["campaign"]["curso"],
-            "margen":     loss,
-            "margen_pct": pct,
+            "margen":     round(rev - cost, 2),
+            "margen_pct": round(item["analysis"]["margen"] * 100, 1),
             "ingreso":    round(rev, 2),
             "coste":      round(cost, 2),
         })
@@ -113,15 +171,35 @@ def build_and_send(collected: list, dry_run: bool) -> None:
             "text": {
                 "type": "mrkdwn",
                 "text": (
-                    f"📊 *RESUMEN DEL MES*\n"
-                    f"Inversión: *{inv_total:,.0f}€*\n"
-                    f"Ingreso por sumatorio: *{ing_sumatorio:,.0f}€*  |  Margen por sumatorio: *{_fmt_eur(margen_sumatorio)}* ({pct_sumatorio}%)\n"
-                    f"Ingreso LeadsxPPL: *{ing_leads_ppl:,.0f}€*  |  Margen por LeadsxPPL: *{_fmt_eur(margen_leads_ppl)}* ({pct_leads_ppl}%)"
+                    f"📊 *RESUMEN DEL MES EN CURSO ({MESES_ES[now.month]})*\n"
+                    f"Inversión: *{inv_total:,.0f}€*  |  Conversiones: *{conv_total}*\n"
+                    + (
+                        f"Ingreso por sumatorio: *0€*  |  Margen por sumatorio: *0€*\n"
+                        f"Ingreso LeadsxPPL: *0€*  |  Margen por LeadsxPPL: *0€*"
+                        if primer_dia_mes else
+                        f"Ingreso por sumatorio: *{ing_sumatorio:,.0f}€*  |  Margen por sumatorio: *{_fmt_eur(margen_sumatorio)}* ({pct_sumatorio}%)\n"
+                        f"Ingreso LeadsxPPL: *{ing_leads_ppl:,.0f}€*  |  Margen por LeadsxPPL: *{_fmt_eur(margen_leads_ppl)}* ({pct_leads_ppl}%)"
+                    )
                 ).replace(",", "."),
             },
         },
     ]
 
+    if primer_dia_mes and prev_inv is not None:
+        blocks.append({"type": "divider"})
+        blocks.append({
+            "type": "section",
+            "text": {
+                "type": "mrkdwn",
+                "text": (
+                    f"📅 *CIERRE {MESES_ES[ayer.month].upper()} (mes anterior)*\n"
+                    f"_Datos de Google Ads_\n"
+                    f"Inversión: *{prev_inv:,.0f}€*  |  Conversiones: *{prev_conv}*\n"
+                    f"Ingreso por sumatorio: *{prev_ing:,.0f}€*  |  Margen: *{_fmt_eur(prev_margen)}* ({prev_pct}%)"
+                ).replace(",", "."),
+            },
+        })
+
     if alerts:
         alert_lines = "\n".join(
             f"  🔴 `{_curso(a['curso'])}` → Pérdida *{_fmt_eur(a['margen'])}* ({a['margen_pct']}%)"
@@ -136,44 +214,55 @@ def build_and_send(collected: list, dry_run: bool) -> None:
             },
         })
 
-    blocks.append({"type": "divider"})
-
-    def _ranking_text(rows, label, show_pct=False):
-        lines = []
+    def _ranking_last5_text(rows, label):
+        lines = [label]
         for i, r in enumerate(rows, 1):
-            pct_str = f" ({r['margen_pct']}%)" if show_pct else ""
-            lines.append(f"  {i}. `{_curso(r['curso'])}` → *{_fmt_eur(r['margen'])}*{pct_str}")
-        return f"{label}\n" + "\n".join(lines)
+            lines.append(f"  {i}. `{_curso(r['curso'])}`")
+            lines.append(
+                f"      Coste: {r['coste']:,.0f}€  |  Ingreso: {r['ingreso']:,.0f}€  |  Margen: *{_fmt_eur(r['margen'])}*"
+                .replace(",", ".")
+            )
+        return "\n".join(lines)
 
-    blocks.append({
-        "type": "section",
-        "fields": [
-            {
-                "type": "mrkdwn",
-                "text": _ranking_text(worst_last5, "📉 *PEOR — ÚLTIMOS 5 DÍAS*"),
-            },
-            {
-                "type": "mrkdwn",
-                "text": _ranking_text(best_last5, "📈 *MEJOR — ÚLTIMOS 5 DÍAS*"),
-            },
-        ],
-    })
+    def _ranking_month_text(rows, label):
+        lines = [label]
+        for i, r in enumerate(rows, 1):
+            lines.append(
+                f"  {i}. `{_curso(r['curso'])}` → *{_fmt_eur(r['margen'])}* ({r['margen_pct']}%)"
+            )
+        return "\n".join(lines)
 
     blocks.append({"type": "divider"})
-
     blocks.append({
         "type": "section",
-        "fields": [
-            {
-                "type": "mrkdwn",
-                "text": _ranking_text(worst_month, "📉 *PEOR — MES EN CURSO*", show_pct=True),
-            },
-            {
-                "type": "mrkdwn",
-                "text": _ranking_text(best_month, "📈 *MEJOR — MES EN CURSO*", show_pct=True),
-            },
-        ],
+        "text": {
+            "type": "mrkdwn",
+            "text": _ranking_last5_text(worst_last5, "📉 *PEOR — ÚLTIMOS 5 DÍAS*"),
+        },
     })
+    blocks.append({
+        "type": "section",
+        "text": {
+            "type": "mrkdwn",
+            "text": _ranking_last5_text(best_last5, "📈 *MEJOR — ÚLTIMOS 5 DÍAS*"),
+        },
+    })
+
+    if not primer_dia_mes:
+        blocks.append({"type": "divider"})
+        blocks.append({
+            "type": "section",
+            "fields": [
+                {
+                    "type": "mrkdwn",
+                    "text": _ranking_month_text(worst_month, "📉 *PEOR — MES EN CURSO*"),
+                },
+                {
+                    "type": "mrkdwn",
+                    "text": _ranking_month_text(best_month, "📈 *MEJOR — MES EN CURSO*"),
+                },
+            ],
+        })
 
     payload = {"blocks": blocks}
     try: