José Manuel Gómez
d23cb6b9c2
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Weekly Strategic Report / run (push) Has been cancelled
- analyzer.py: use abs() for tracking discrepancy so both directions alert - slack_reporter.py: daily table adds L.AT/L.GA/Coste/€.AT/€.GA columns; margin uses Google Ads conversions × PPL; split monthly rankings into separate blocks; fix stale margen field - run.py: leads_grupo always uses Google Ads conversions; tracking alert shows direction (over/under attribution) - agent.py: increase decide() max_tokens 700→750 to fix JSON truncation for long campaign names Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
377 lines
15 KiB
Python
377 lines
15 KiB
Python
import json
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import subprocess
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import requests
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import config
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from datetime import datetime, timedelta, date
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def _git_version() -> str:
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try:
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return subprocess.check_output(
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["git", "rev-parse", "--short", "HEAD"],
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stderr=subprocess.DEVNULL,
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text=True,
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).strip()
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except Exception:
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return "unknown"
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ALERT_LOSS_EUR = 200 # pérdida absoluta > 200€ → alerta
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ALERT_MARGIN_PCT = -50 # margen % < -50% → alerta
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TOP_N = 5 # campañas a mostrar en rankings
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MESES_ES = {
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1: "Enero", 2: "Febrero", 3: "Marzo", 4: "Abril",
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5: "Mayo", 6: "Junio", 7: "Julio", 8: "Agosto",
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9: "Septiembre", 10: "Octubre", 11: "Noviembre", 12: "Diciembre",
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}
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def _parse_metricas(metricas_json: str) -> dict:
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try:
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return json.loads(metricas_json) if metricas_json else {}
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except (json.JSONDecodeError, TypeError):
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return {}
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def _last_n_days_combined(current_md: dict, prev_md: dict, now: datetime, n: int) -> dict:
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"""
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Combina MetricasDiarias del mes actual y del anterior para calcular los últimos n días.
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Las claves son strings de día ("01"…"31"); para ordenar por fecha real se añade el mes.
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"""
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entries = []
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prev_month = now.month - 1 if now.month > 1 else 12
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prev_year = now.year if now.month > 1 else now.year - 1
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for day_str, vals in prev_md.items():
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try:
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entries.append((date(prev_year, prev_month, int(day_str)), vals))
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except ValueError:
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pass
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for day_str, vals in current_md.items():
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try:
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entries.append((date(now.year, now.month, int(day_str)), vals))
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except ValueError:
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pass
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entries.sort(key=lambda x: x[0])
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last_n = entries[-n:]
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total_coste = round(sum(v.get("coste", 0) for _, v in last_n), 2)
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total_ingreso = round(sum(v.get("ingreso", 0) for _, v in last_n), 2)
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return {
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"coste": total_coste,
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"ingreso": total_ingreso,
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"margen": round(total_ingreso - total_coste, 2),
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"n_days": len(last_n),
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}
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def _trunc(text: str, limit: int = 2950) -> str:
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return text if len(text) <= limit else text[:limit - 3] + "…"
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def _fmt_eur(v: float) -> str:
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sign = "+" if v > 0 else ""
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return f"{sign}{v:,.0f}€".replace(",", ".")
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def _curso(name: str, max_len: int = 40) -> str:
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return name[:max_len] + ("…" if len(name) > max_len else "")
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def build_and_send(collected: list, dry_run: bool, prev_month_metricas: dict = None,
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portfolio_analysis_text: str = None) -> None:
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if not config.SLACK_WEBHOOK_URL:
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print(" ⚠️ SLACK_WEBHOOK_URL no configurada, omitiendo envío.")
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return
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now = datetime.now()
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ayer = now - timedelta(days=1)
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cambio_mes = ayer.month != now.month
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mes_sumatorio = MESES_ES[ayer.month] if cambio_mes else None
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prev_md_map = prev_month_metricas or {}
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fco = [item for item in collected if item["campaign"]["curso"].lower().startswith("fco_")]
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primer_dia_mes = now.day == 1
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# ── Totales del mes en curso ─────────────────────────────────────────────
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inv_total = round(sum(item["metrics"].get("cost", 0) for item in fco), 2)
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conv_total = int(sum(item["analysis"]["conversiones_google"] for item in fco))
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ing_leads_ppl = round(sum(item["analysis"]["revenue_estimado"] for item in fco), 2)
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if primer_dia_mes:
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# Mes recién empezado: no hay datos de sumatorio para el mes en curso
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ing_sumatorio = 0.0
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margen_sumatorio = 0.0
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pct_sumatorio = 0.0
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margen_leads_ppl = 0.0
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pct_leads_ppl = 0.0
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else:
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ing_sumatorio = 0.0
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coste_sumatorio = 0.0
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for item in fco:
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for d in _parse_metricas(item["campaign"].get("metricas_diarias", "{}")).values():
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ing_sumatorio += d.get("ingreso", 0)
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coste_sumatorio += d.get("coste", 0)
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ing_sumatorio = round(ing_sumatorio, 2)
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coste_sumatorio = round(coste_sumatorio, 2)
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margen_sumatorio = round(ing_sumatorio - coste_sumatorio, 2)
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margen_leads_ppl = round(ing_leads_ppl - inv_total, 2)
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pct_sumatorio = round(margen_sumatorio / ing_sumatorio * 100, 1) if ing_sumatorio > 0 else 0.0
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pct_leads_ppl = round(margen_leads_ppl / ing_leads_ppl * 100, 1) if ing_leads_ppl > 0 else 0.0
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# ── Totales del mes anterior (solo día 1) ────────────────────────────────
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if primer_dia_mes and prev_md_map:
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# Iterar sobre TODAS las campañas del mes anterior filtrando fco_
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# (no solo las activas en el mes en curso, para no perder campañas pausadas)
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prev_fco = [v for v in prev_md_map.values() if v.get("nombre", "").lower().startswith("fco_")]
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prev_inv = round(sum(v.get("coste_mes", 0) for v in prev_fco), 2)
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prev_conv = int(sum(v.get("conv_mes", 0) for v in prev_fco))
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prev_ing = round(sum(
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sum(d.get("ingreso", 0) for d in _parse_metricas(v.get("metricas", "{}")).values())
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for v in prev_fco
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), 2)
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prev_margen = round(prev_ing - prev_inv, 2)
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prev_pct = round(prev_margen / prev_ing * 100, 1) if prev_ing > 0 else 0.0
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else:
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prev_inv = prev_conv = prev_ing = prev_margen = prev_pct = None
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# ── Últimos 5 días (combinando meses si hay cambio de mes) ───────────────
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last5_rows = []
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for item in fco:
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gid = item["campaign"]["google_campaign_id"]
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current_md = _parse_metricas(item["campaign"].get("metricas_diarias", "{}"))
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prev_md = _parse_metricas(prev_md_map.get(gid, {}).get("metricas", "{}"))
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s = _last_n_days_combined(current_md, prev_md, now, 5)
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if s["n_days"] == 0:
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continue
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last5_rows.append({
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"curso": item["campaign"]["curso"],
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"margen": s["margen"],
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"ingreso": s["ingreso"],
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"coste": s["coste"],
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})
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last5_rows.sort(key=lambda x: x["margen"])
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worst_last5 = last5_rows[:TOP_N]
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best_last5 = list(reversed(last5_rows[-TOP_N:]))
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# ── Mes en curso ─────────────────────────────────────────────────────────
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month_rows = []
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for item in fco:
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cost = item["metrics"].get("cost", 0)
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rev = item["analysis"]["revenue_estimado"]
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month_rows.append({
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"curso": item["campaign"]["curso"],
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"margen": round(rev - cost, 2),
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"margen_pct": round(item["analysis"]["margen"] * 100, 1),
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"ingreso": round(rev, 2),
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"coste": round(cost, 2),
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})
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month_rows.sort(key=lambda x: x["margen"])
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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, "ing_gads": 0.0, "ing_at": 0.0, "leads_gads": 0, "leads_at": 0}
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daily_totals[d]["coste"] += vals.get("coste", 0)
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daily_totals[d]["ing_gads"] += vals.get("ingreso", 0) # conv_hoy × PPL
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daily_totals[d]["ing_at"] += vals.get("leads_lake", 0) * ppl # leads_lake × PPL
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daily_totals[d]["leads_gads"] += int(vals.get("leads", 0))
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daily_totals[d]["leads_at"] += int(vals.get("leads_lake", 0))
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def _eur(v: float) -> str:
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return f"{v:,.0f}€".replace(",", ".")
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def _marg(v: float) -> str:
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return ("+" if v >= 0 else "") + f"{v:,.0f}€".replace(",", ".")
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def _pct(v: float) -> str:
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return ("+" if v >= 0 else "") + f"{v:.1f}%"
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margin_table_block = None
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if daily_totals and not primer_dia_mes:
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hdr = f"{'Día':>3} {'L.AT':>5} {'L.GA':>5} {'Coste':>7} {'€.AT':>7} {'€.GA':>7} {'Margen':>9} {'%':>7}"
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sep = "─" * len(hdr)
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rows = [hdr, sep]
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tot = {"coste": 0.0, "ing_gads": 0.0, "ing_at": 0.0, "leads_gads": 0, "leads_at": 0}
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for d in sorted(daily_totals):
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v = daily_totals[d]
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ing = v["ing_gads"] # margen: conv Google Ads × PPL
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marg = ing - v["coste"]
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pct_v = round(marg / ing * 100, 1) if ing > 0 else 0.0
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for k in tot:
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tot[k] += v[k]
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rows.append(
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f"{d:02d} {v['leads_at']:>5} {v['leads_gads']:>5}"
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f" {_eur(v['coste']):>7}"
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f" {_eur(v['ing_at']):>7} {_eur(v['ing_gads']):>7}"
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f" {_marg(marg):>9} {_pct(pct_v):>7}"
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)
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tot_ing = tot["ing_gads"]
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tot_marg = tot_ing - tot["coste"]
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tot_pct = round(tot_marg / tot_ing * 100, 1) if tot_ing > 0 else 0.0
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rows.append(sep)
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rows.append(
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f"{'TOT':>3} {tot['leads_at']:>5} {tot['leads_gads']:>5}"
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f" {_eur(tot['coste']):>7}"
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f" {_eur(tot['ing_at']):>7} {_eur(tot['ing_gads']):>7}"
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f" {_marg(tot_marg):>9} {_pct(tot_pct):>7}"
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)
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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": _trunc(f"📅 *MÉTRICAS POR DÍA — {MESES_ES[now.month].upper()}* _(L.AT=leads Airtable · L.GA=conv Google Ads · €.AT=fact×PPL Airtable · €.GA=fact×PPL Google Ads)_\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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if r["margen"] < -ALERT_LOSS_EUR or r["margen_pct"] < ALERT_MARGIN_PCT
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]
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alerts.sort(key=lambda x: x["margen"])
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# ── Construir bloques ─────────────────────────────────────────────────────
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mode = "🔵 DRY RUN" if dry_run else "⚡ PRODUCCIÓN"
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version = _git_version()
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blocks = [
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{
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"type": "header",
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"text": {"type": "plain_text", "text": f"LEADS OPTIMIZER v{version} — {now.strftime('%d/%m/%Y %H:%M')} {mode}"},
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},
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{
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"type": "section",
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"text": {
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"type": "mrkdwn",
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"text": (
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f"📊 *RESUMEN DEL MES EN CURSO ({MESES_ES[now.month]})*\n"
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f"Inversión: *{inv_total:,.0f}€* | Conversiones: *{conv_total}*\n"
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+ (
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f"Ingreso por sumatorio: *0€* | Margen por sumatorio: *0€*\n"
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f"Ingreso LeadsxPPL: *0€* | Margen por LeadsxPPL: *0€*"
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if primer_dia_mes else
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f"Ingreso por sumatorio: *{ing_sumatorio:,.0f}€* | Margen por sumatorio: *{_fmt_eur(margen_sumatorio)}* ({pct_sumatorio}%)\n"
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f"Ingreso LeadsxPPL: *{ing_leads_ppl:,.0f}€* | Margen por LeadsxPPL: *{_fmt_eur(margen_leads_ppl)}* ({pct_leads_ppl}%)"
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)
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).replace(",", "."),
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},
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},
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]
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if primer_dia_mes and prev_inv is not None:
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blocks.append({"type": "divider"})
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blocks.append({
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"type": "section",
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"text": {
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"type": "mrkdwn",
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"text": (
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f"📅 *CIERRE {MESES_ES[ayer.month].upper()} (mes anterior)*\n"
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f"_Datos de Google Ads_\n"
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f"Inversión: *{prev_inv:,.0f}€* | Conversiones: *{prev_conv}*\n"
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f"Ingreso por sumatorio: *{prev_ing:,.0f}€* | Margen: *{_fmt_eur(prev_margen)}* ({prev_pct}%)"
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).replace(",", "."),
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},
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})
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if alerts:
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alert_lines = "\n".join(
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f" 🔴 `{_curso(a['curso'])}` → Pérdida *{_fmt_eur(a['margen'])}* ({a['margen_pct']}%)"
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for a in alerts
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)
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blocks.append({"type": "divider"})
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blocks.append({
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"type": "section",
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"text": {
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"type": "mrkdwn",
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"text": f"🚨 *ALERTAS — CAMPAÑAS CON PÉRDIDAS IMPORTANTES*\n{alert_lines}",
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},
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})
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def _ranking_last5_text(rows, label):
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lines = [label]
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for i, r in enumerate(rows, 1):
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lines.append(f" {i}. `{_curso(r['curso'])}`")
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lines.append(
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f" Coste: {r['coste']:,.0f}€ | Ingreso: {r['ingreso']:,.0f}€ | Margen: *{_fmt_eur(r['margen'])}*"
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.replace(",", ".")
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)
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return "\n".join(lines)
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def _ranking_month_text(rows, label):
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lines = [label]
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for i, r in enumerate(rows, 1):
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lines.append(
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f" {i}. `{_curso(r['curso'])}` → *{_fmt_eur(r['margen'])}* ({r['margen_pct']}%)"
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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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"text": {
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"type": "mrkdwn",
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"text": _trunc(_ranking_last5_text(worst_last5, "📉 *PEOR — ÚLTIMOS 5 DÍAS*")),
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},
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})
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blocks.append({
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"type": "section",
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"text": {
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"type": "mrkdwn",
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"text": _trunc(_ranking_last5_text(best_last5, "📈 *MEJOR — ÚLTIMOS 5 DÍAS*")),
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},
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})
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if not primer_dia_mes:
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blocks.append({"type": "divider"})
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blocks.append({
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"type": "section",
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"text": {
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"type": "mrkdwn",
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"text": _trunc(_ranking_month_text(worst_month, "📉 *PEOR — MES EN CURSO*")),
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},
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})
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blocks.append({
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"type": "section",
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"text": {
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"type": "mrkdwn",
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"text": _trunc(_ranking_month_text(best_month, "📈 *MEJOR — MES EN CURSO*")),
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},
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})
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if portfolio_analysis_text:
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blocks.append({"type": "divider"})
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blocks.append({
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"type": "section",
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"text": {
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"type": "mrkdwn",
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"text": _trunc(f"🤖 *DIAGNÓSTICO ESTRATÉGICO*\n{portfolio_analysis_text}"),
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},
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})
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payload = {"blocks": blocks}
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try:
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resp = requests.post(config.SLACK_WEBHOOK_URL, json=payload, timeout=10)
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if resp.status_code != 200:
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print(f" ⚠️ Slack respondió {resp.status_code}: {resp.text[:200]}")
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except Exception as e:
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print(f" ⚠️ Error enviando a Slack: {e}")
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