Earnings Calendar → Content Timing

import os, requests, time, csv, io

AV_KEY = os.environ["ALPHA_VANTAGE_KEY"]
AV_BASE = "https://www.alphavantage.co/query"
MV = os.environ["MAVERA_API_KEY"]
MV_BASE = "https://app.mavera.io/api/v1"
MV_H = {"Authorization": f"Bearer {MV}", "Content-Type": "application/json"}

AUDIENCE_SECTORS = ["Technology", "Communication Services", "Consumer Discretionary"]
BRAND = "B2B marketing platform. Audience: Marketing leaders at enterprise companies. Tone: analytical, forward-looking."

# 1. Fetch earnings calendar (returns CSV)
r = requests.get(AV_BASE, params={
    "function": "EARNINGS_CALENDAR", "horizon": "3month", "apikey": AV_KEY,
})
r.raise_for_status()
reader = csv.DictReader(io.StringIO(r.text))
all_earnings = [row for row in reader]
print(f"Total earnings events: {len(all_earnings)}")

# 2. Filter to relevant companies
relevant = [e for e in all_earnings
    if any(s.lower() in (e.get("name","") + e.get("symbol","")).lower() for s in ["AAPL","MSFT","GOOGL","META","AMZN","SHOP","HBS","CRM","ADBE","TWLO"])
    or float(e.get("estimate","0") or "0") > 1.0]
relevant.sort(key=lambda x: x.get("reportDate",""))
print(f"Relevant earnings: {len(relevant)}")

# 3. Build earnings timeline
timeline = "\n".join(
    f"- {e.get('reportDate','')} | {e.get('symbol','')} ({e.get('name','')}) | "
    f"Est EPS: ${e.get('estimate','N/A')} | Fiscal: {e.get('fiscalDateEnding','')}"
    for e in relevant[:20]
)

# 4. Mave content planning
plan = requests.post(f"{MV_BASE}/mave/chat", headers=MV_H, json={
    "message": f"Content strategist specializing in earnings-cycle timing.\n\n"
        f"BRAND: {BRAND}\n\n"
        f"UPCOMING EARNINGS:\n{timeline}\n\n"
        "For the top 8 most impactful earnings events, create a content timing plan:\n\n"
        "PRE-EARNINGS (3-5 days before):\n- Thought piece angle that positions us as analysts\n"
        "EARNINGS DAY:\n- Real-time commentary template (LinkedIn post format)\n"
        "POST-EARNINGS (1-2 days after):\n- Analysis framework content piece\n\n"
        "Include: publish date, format, headline, key hook, why our audience cares."
}).json()
print(f"\n{plan.get('content', '')[:2000]}")

# 5. Generate first content piece
if relevant:
    first = relevant[0]
    gen = requests.post(f"{MV_BASE}/generations", headers=MV_H, json={
        "prompt": f"Write a pre-earnings thought piece (600 words) for marketing leaders.\n\n"
            f"COMPANY: {first.get('name','')} ({first.get('symbol','')})\n"
            f"REPORT DATE: {first.get('reportDate','')}\n"
            f"EPS ESTIMATE: ${first.get('estimate','N/A')}\n"
            f"BRAND: {BRAND}\n\n"
            "Angle: What this company's earnings signal about marketing technology spend.\n"
            "Include: What to watch, implications for marketing budgets, our unique take.\n"
            "Tone: analytical, not financial advice. End with a question for engagement.",
    }).json()
    print(f"\n{'='*60}\nPRE-EARNINGS PIECE: {first.get('symbol','')}\n{'='*60}")
    print(gen.get("output", gen.get("content", ""))[:1000])

const AV_KEY = process.env.ALPHA_VANTAGE_KEY;
const AV_BASE = "https://www.alphavantage.co/query";
const MV = process.env.MAVERA_API_KEY;
const MV_BASE = "https://app.mavera.io/api/v1";
const MV_H = { Authorization: `Bearer ${MV}`, "Content-Type": "application/json" };

const BRAND = "B2B marketing platform. Audience: Marketing leaders. Tone: analytical, forward-looking.";

// 1. Earnings calendar (CSV)
const csvText = await (await fetch(
  `${AV_BASE}?function=EARNINGS_CALENDAR&horizon=3month&apikey=${AV_KEY}`)).text();
const lines = csvText.trim().split("\n");
const headers = lines[0].split(",");
const allEarnings = lines.slice(1).map(line => {
  const vals = line.split(",");
  return Object.fromEntries(headers.map((h, i) => [h, vals[i] || ""]));
});
console.log(`Total earnings events: ${allEarnings.length}`);

// 2. Filter relevant
const watchlist = ["AAPL","MSFT","GOOGL","META","AMZN","SHOP","CRM","ADBE","TWLO"];
const relevant = allEarnings
  .filter(e => watchlist.some(s => (e.symbol||"").includes(s)) || parseFloat(e.estimate||"0") > 1.0)
  .sort((a, b) => (a.reportDate||"").localeCompare(b.reportDate||""));
console.log(`Relevant earnings: ${relevant.length}`);

// 3. Timeline
const timeline = relevant.slice(0, 20).map(e =>
  `- ${e.reportDate} | ${e.symbol} (${e.name}) | Est: $${e.estimate || "N/A"}`).join("\n");

// 4. Content plan
const plan = await fetch(`${MV_BASE}/mave/chat`, { method: "POST", headers: MV_H,
  body: JSON.stringify({ message: `Earnings-cycle content strategist.\n\nBRAND: ${BRAND}\n\nEARNINGS:\n${timeline}\n\nTop 8 events: PRE (thought piece), DAY (LinkedIn template), POST (analysis). Include date, format, headline, hook.` }),
}).then(r => r.json());
console.log((plan.content || "").slice(0, 2000));

// 5. Generate first piece
if (relevant.length) {
  const first = relevant[0];
  const gen = await fetch(`${MV_BASE}/generations`, { method: "POST", headers: MV_H,
    body: JSON.stringify({ prompt: `Pre-earnings thought piece (600 words) for marketing leaders.\n\nCOMPANY: ${first.name} (${first.symbol})\nDATE: ${first.reportDate}\nEST: $${first.estimate||"N/A"}\n\nAngle: What earnings signal about martech spend.\nTone: analytical, not financial advice.` }),
  }).then(r => r.json());
  console.log(`\n${"=".repeat(60)}\nPRE-EARNINGS: ${first.symbol}`);
  console.log((gen.output || gen.content || "").slice(0, 1000));
}