/** Deterministic budget simulation from tenant ad_daily + kpi_weekly. */

import { AdDailyRow, KpiWeeklyRow } from '@/lib/metrics';
import { normalizePlatform } from '@/lib/ad-tracker-range';

export type SimChannel = 'meta' | 'google' | 'blended';

export type SimBaseline = {
  historical_cpl: number;
  conversion_rate_pct: number;
  avg_ticket: number;
  sample_spend: number;
  sample_leads: number;
  sample_deals: number;
  sample_revenue: number;
  channel: string;
  source: 'tenant_metrics';
};

export type SimBand = {
  leads: number;
  deals: number;
  revenue: number;
  roas: number;
};

export type SimResult = {
  target_channel: string;
  baseline: SimBaseline;
  conservative_p10: SimBand;
  expected_p50: SimBand;
  optimistic_p90: SimBand;
};

function n(v: unknown): number {
  const x = Number(v);
  return Number.isFinite(x) ? x : 0;
}

function matchChannel(platform: string | undefined, ch: SimChannel): boolean {
  if (ch === 'blended') return true;
  const p = normalizePlatform(platform);
  if (ch === 'meta') return p === 'meta';
  if (ch === 'google') return p === 'google';
  return false;
}

function band(
  spend: number,
  cpl: number,
  convRate: number,
  ticket: number,
  cplMult: number,
  convMult: number
): SimBand {
  const effectiveCpl = Math.max(1, cpl * cplMult);
  const leads = Math.round(spend / effectiveCpl);
  const deals = Math.round(leads * Math.min(0.95, convRate * convMult));
  const revenue = deals * ticket;
  const roas = spend > 0 ? Math.round((revenue / spend) * 100) / 100 : 0;
  return { leads, deals, revenue: Math.round(revenue * 100) / 100, roas };
}

/** Build simulation from this tenant's warehouse rows. */
export function simulateFromTenantMetrics(opts: {
  spend: number;
  channel: SimChannel;
  daily: AdDailyRow[];
  weekly: KpiWeeklyRow[];
}): SimResult {
  const { spend, channel, daily, weekly } = opts;
  const dailyCh = daily.filter((r) => matchChannel(r.platform, channel));
  const srcDaily = dailyCh.length ? dailyCh : daily;

  const sampleSpend = srcDaily.reduce((a, r) => a + n(r.spend), 0);
  const sampleLeads = srcDaily.reduce(
    (a, r) => a + n(r.platform_leads ?? r.conversions),
    0
  );

  const weeklyCh = weekly.filter((r) => {
    if (channel === 'blended') return true;
    const c = (r.channel || '').toLowerCase();
    if (channel === 'meta') {
      return (
        c.includes('meta') ||
        c.includes('facebook') ||
        c.includes('instagram') ||
        c.includes('fb')
      );
    }
    if (channel === 'google') {
      return c.includes('google') || c.includes('search') || c.includes('pmax');
    }
    return true;
  });
  const srcWeekly = weeklyCh.length ? weeklyCh : weekly;

  const wSpend = srcWeekly.reduce((a, r) => a + n(r.total_spend), 0);
  const wLeads = srcWeekly.reduce((a, r) => a + n(r.total_leads), 0);
  const wDeals = srcWeekly.reduce((a, r) => a + n(r.cohort_deals_won), 0);
  const wRevenue = srcWeekly.reduce((a, r) => a + n(r.cohort_revenue_won), 0);

  const spendBase = sampleSpend > 0 ? sampleSpend : wSpend;
  const leadsBase = sampleLeads > 0 ? sampleLeads : wLeads;
  let historicalCpl =
    leadsBase > 0
      ? spendBase / leadsBase
      : sampleSpend > 0
        ? sampleSpend / Math.max(1, sampleLeads)
        : 25;
  if (!Number.isFinite(historicalCpl) || historicalCpl <= 0) historicalCpl = 25;

  const convRate =
    wLeads > 0
      ? wDeals / wLeads
      : wDeals > 0 && leadsBase > 0
        ? wDeals / leadsBase
        : 0.12;
  const avgTicket =
    wDeals > 0 ? wRevenue / wDeals : wRevenue > 0 ? wRevenue : 800;

  const baseline: SimBaseline = {
    historical_cpl: Math.round(historicalCpl * 100) / 100,
    conversion_rate_pct: Math.round(Math.max(0.01, convRate) * 1000) / 10,
    avg_ticket: Math.round(avgTicket * 100) / 100,
    sample_spend: Math.round(spendBase * 100) / 100,
    sample_leads: Math.round(leadsBase),
    sample_deals: Math.round(wDeals),
    sample_revenue: Math.round(wRevenue * 100) / 100,
    channel: channel === 'blended' ? 'blended' : channel,
    source: 'tenant_metrics',
  };

  const cr = Math.max(0.01, convRate);
  return {
    target_channel: baseline.channel,
    baseline,
    conservative_p10: band(spend, historicalCpl, cr, avgTicket, 1.35, 0.75),
    expected_p50: band(spend, historicalCpl, cr, avgTicket, 1.0, 1.0),
    optimistic_p90: band(spend, historicalCpl, cr, avgTicket, 0.75, 1.2),
  };
}