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>
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@ -14,6 +14,7 @@ class AirtableClient:
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self.centrocurso = self.api.table(config.AIRTABLE_BASE_ID, "CentroCurso")
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self.cursomes = self.api.table(config.AIRTABLE_BASE_ID, "CursoMes")
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self.gacampaignmes = self.api.table(config.AIRTABLE_BASE_ID, "GACampaignMes")
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self.games = self.api.table(config.AIRTABLE_BASE_ID, "GAMes")
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MESES_ES = {
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1: "Enero", 2: "Febrero", 3: "Marzo", 4: "Abril",
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@ -478,6 +479,25 @@ class AirtableClient:
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for i in range(0, len(batch), 10):
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self.gacampaignmes.batch_update(batch[i:i+10])
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# ── GAMes ──────────────────────────────────────────────────────────────── #
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def get_or_create_games_record(self, mes: int, anio: int) -> str:
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records = self.games.all(formula=f"AND({{Mes}}='{mes}',{{Año}}='{anio}')")
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if records:
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return records[0]["id"]
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r = self.games.create({"Mes": str(mes), "Año": str(anio)})
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return r["id"]
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def update_games_metricas(self, record_id: str, metricas_json: str) -> None:
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self.games.update(record_id, {"MetricasDiarias": metricas_json})
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def get_games_metricas(self, record_id: str) -> dict:
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r = self.games.get(record_id)
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try:
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return json.loads(r["fields"].get("MetricasDiarias") or "{}")
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except (json.JSONDecodeError, TypeError):
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return {}
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def batch_update_gacampaignmes_advice(self, updates: list[tuple]) -> None:
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"""
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Actualiza en lote los campos 'Consejo', 'Criticidad' y 'Log' de GACampaignMes.
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73
backfill_games_mayo.py
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73
backfill_games_mayo.py
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@ -0,0 +1,73 @@
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"""
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Script one-off: crea el registro de mayo 2026 en GAMes y rellena MetricasDiarias
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con el agregado diario de todas las campañas fco_ a partir de GACampaignMes.
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Ejecutar una sola vez:
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python backfill_games_mayo.py
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"""
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import json
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from airtable_client import AirtableClient
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at = AirtableClient()
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MES = 5
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ANIO = 2026
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print("Cargando registros GACampaignMes de mayo (campañas fco_)...")
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records = at.gacampaignmes.all(
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formula="AND({Mes}='5',{Año}='2026')",
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fields=["CampaignID", "MetricasDiarias", "Campaign Name (from CampaignID)"],
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)
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campaigns_records = at.campaigns.all(fields=["CampaignID", "PPL"])
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at_id_to_info = {
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r["id"]: {
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"gid": str(r["fields"].get("CampaignID", "")).strip(),
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"ppl": float(r["fields"].get("PPL", 0) or 0),
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}
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for r in campaigns_records
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}
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# Agregar métricas por día para campañas fco_
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daily_agg: dict[str, dict] = {}
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for r in records:
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at_cids = r["fields"].get("CampaignID", [])
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if not at_cids:
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continue
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info = at_id_to_info.get(at_cids[0], {})
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ppl = info.get("ppl", 0)
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campaign_names = r["fields"].get("Campaign Name (from CampaignID)", [])
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campaign_name = (campaign_names[0] if campaign_names else "").lower()
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if not campaign_name.startswith("fco_"):
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continue
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try:
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md = json.loads(r["fields"].get("MetricasDiarias") or "{}")
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except (json.JSONDecodeError, TypeError):
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md = {}
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for day_str, vals in md.items():
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if day_str not in daily_agg:
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daily_agg[day_str] = {"coste": 0.0, "ingreso_sum": 0.0, "ingreso_lxp": 0.0, "leads": 0, "leads_lake": 0}
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daily_agg[day_str]["coste"] += vals.get("coste", 0)
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daily_agg[day_str]["ingreso_sum"] += vals.get("ingreso", 0)
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daily_agg[day_str]["ingreso_lxp"] += vals.get("leads_lake", 0) * ppl
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daily_agg[day_str]["leads"] += int(vals.get("leads", 0))
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daily_agg[day_str]["leads_lake"] += int(vals.get("leads_lake", 0))
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print(f" > Dias agregados: {sorted(daily_agg.keys())}")
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# Redondear
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for d in daily_agg:
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daily_agg[d] = {k: round(v, 2) if isinstance(v, float) else v for k, v in daily_agg[d].items()}
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print("Creando/obteniendo registro GAMes de mayo 2026...")
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games_rid = at.get_or_create_games_record(MES, ANIO)
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print(f" > Record ID: {games_rid}")
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print("Guardando MetricasDiarias en GAMes...")
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at.update_games_metricas(games_rid, json.dumps(daily_agg, ensure_ascii=False))
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print(f"OK Backfill GAMes completado: {len(daily_agg)} dias guardados.")
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20
run.py
20
run.py
@ -219,6 +219,7 @@ def run():
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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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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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@ -306,6 +307,15 @@ def run():
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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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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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@ -355,6 +365,16 @@ def run():
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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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at.update_games_metricas(games_rid, json.dumps(games_md, ensure_ascii=False))
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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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@ -151,6 +151,42 @@ def build_and_send(collected: list, dry_run: bool, prev_month_metricas: dict = N
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worst_month = month_rows[:TOP_N]
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best_month = list(reversed(month_rows[-TOP_N:]))
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# ── Tabla de márgenes diarios ─────────────────────────────────────────────
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daily_totals: dict[int, dict] = {}
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for item in fco:
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ppl = item["campaign"].get("ppl", 0)
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md = _parse_metricas(item["campaign"].get("metricas_diarias", "{}"))
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for day_str, vals in md.items():
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try:
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d = int(day_str)
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except ValueError:
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continue
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if d not in daily_totals:
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daily_totals[d] = {"coste": 0.0, "ingreso_sum": 0.0, "ingreso_lxp": 0.0}
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daily_totals[d]["coste"] += vals.get("coste", 0)
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daily_totals[d]["ingreso_sum"] += vals.get("ingreso", 0)
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daily_totals[d]["ingreso_lxp"] += vals.get("leads_lake", 0) * ppl
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margin_table_block = None
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if daily_totals and not primer_dia_mes:
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rows = ["Día Sumatorio LeadsxPPL"]
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for d in sorted(daily_totals):
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coste = daily_totals[d]["coste"]
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ing_sum = daily_totals[d]["ingreso_sum"]
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ing_lxp = daily_totals[d]["ingreso_lxp"]
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pct_sum = round((ing_sum - coste) / ing_sum * 100, 1) if ing_sum > 0 else 0.0
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pct_lxp = round((ing_lxp - coste) / ing_lxp * 100, 1) if ing_lxp > 0 else 0.0
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s_sum = ("+" if pct_sum >= 0 else "") + f"{pct_sum:.1f}%"
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s_lxp = ("+" if pct_lxp >= 0 else "") + f"{pct_lxp:.1f}%"
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rows.append(f"{d:02d} {s_sum:>9} {s_lxp:>9}")
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margin_table_block = {
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"type": "section",
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"text": {
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"type": "mrkdwn",
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"text": f"📅 *MÁRGENES POR DÍA — {MESES_ES[now.month].upper()}*\n```\n" + "\n".join(rows) + "\n```",
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},
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}
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# ── Alertas ──────────────────────────────────────────────────────────────
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alerts = [
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r for r in month_rows
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@ -232,6 +268,10 @@ def build_and_send(collected: list, dry_run: bool, prev_month_metricas: dict = N
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)
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return "\n".join(lines)
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if margin_table_block:
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blocks.append({"type": "divider"})
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blocks.append(margin_table_block)
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blocks.append({"type": "divider"})
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blocks.append({
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"type": "section",
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