Market Sentiment → Brand Positioning

import os, requests, time

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"}

TICKERS = {"AAPL": "Apple", "MSFT": "Microsoft", "GOOGL": "Google"}
OUR_TICKER = "AAPL"

# 1. Fetch news sentiment per ticker
sentiment_data = {}
for ticker, name in TICKERS.items():
    r = requests.get(AV_BASE, params={
        "function": "NEWS_SENTIMENT", "tickers": ticker,
        "limit": 20, "apikey": AV_KEY,
    })
    r.raise_for_status()
    feed = r.json().get("feed", [])
    
    scores = []
    articles_summary = []
    for item in feed:
        for ts in item.get("ticker_sentiment", []):
            if ts.get("ticker") == ticker:
                score = float(ts.get("ticker_sentiment_score", 0))
                scores.append(score)
                articles_summary.append(
                    f"- {item.get('title','')[:100]} (score: {score:.2f}, "
                    f"relevance: {ts.get('relevance_score','N/A')})"
                )
    
    avg = sum(scores) / len(scores) if scores else 0
    sentiment_data[ticker] = {
        "name": name, "avg_sentiment": avg, "article_count": len(feed),
        "positive": sum(1 for s in scores if s > 0.15),
        "negative": sum(1 for s in scores if s < -0.15),
        "neutral": sum(1 for s in scores if -0.15 <= s <= 0.15),
        "articles": articles_summary[:10],
    }
    print(f"{name} ({ticker}): avg={avg:.3f}, articles={len(feed)}, "
          f"+{sentiment_data[ticker]['positive']}/-{sentiment_data[ticker]['negative']}")
    time.sleep(15)

# 2. Build sentiment landscape
landscape = "\n\n".join(
    f"## {d['name']} ({t})\n"
    f"Average Sentiment: {d['avg_sentiment']:.3f}\n"
    f"Distribution: {d['positive']} positive, {d['neutral']} neutral, {d['negative']} negative\n"
    f"Top Headlines:\n" + "\n".join(d['articles'][:5])
    for t, d in sentiment_data.items()
)

# 3. Mave positioning analysis
analysis = requests.post(f"{MV_BASE}/mave/chat", headers=MV_H, json={
    "message": f"Brand strategist specializing in market-sentiment-aligned positioning.\n\n"
        f"SENTIMENT LANDSCAPE (our ticker: {OUR_TICKER}):\n\n{landscape}\n\n"
        "Analyze:\n"
        "1. **Sentiment Map** — Where does each player sit? Who's gaining/losing favor?\n"
        "2. **Our Position** — Given our sentiment vs competitors, what positioning plays are available?\n"
        "3. **Messaging Recommendations** — 3 specific messaging angles based on the sentiment gap\n"
        "4. **Timing** — Is sentiment trending up/down? Should we act now or wait?\n"
        "5. **Risk** — What happens if sentiment reverses?\n\n"
        "Be specific. Reference actual headline themes."
}).json()

print(f"\n{'='*60}\nSENTIMENT-BASED POSITIONING ANALYSIS\n{'='*60}")
print(analysis.get("content", "")[:2500])

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 TICKERS = { AAPL: "Apple", MSFT: "Microsoft", GOOGL: "Google" };
const OUR_TICKER = "AAPL";

// 1. News sentiment per ticker
const sentimentData = {};
for (const [ticker, name] of Object.entries(TICKERS)) {
  const r = await fetch(`${AV_BASE}?function=NEWS_SENTIMENT&tickers=${ticker}&limit=20&apikey=${AV_KEY}`);
  const feed = (await r.json()).feed || [];
  
  const scores = [];
  const articlesSummary = [];
  for (const item of feed) {
    for (const ts of item.ticker_sentiment || []) {
      if (ts.ticker === ticker) {
        const score = parseFloat(ts.ticker_sentiment_score || 0);
        scores.push(score);
        articlesSummary.push(`- ${(item.title||"").slice(0,100)} (score: ${score.toFixed(2)})`);
      }
    }
  }
  
  const avg = scores.length ? scores.reduce((a,b) => a+b, 0) / scores.length : 0;
  sentimentData[ticker] = {
    name, avg_sentiment: avg, article_count: feed.length,
    positive: scores.filter(s => s > 0.15).length,
    negative: scores.filter(s => s < -0.15).length,
    neutral: scores.filter(s => s >= -0.15 && s <= 0.15).length,
    articles: articlesSummary.slice(0, 10),
  };
  console.log(`${name} (${ticker}): avg=${avg.toFixed(3)}, articles=${feed.length}`);
  await new Promise(r => setTimeout(r, 15000));
}

// 2. Landscape
let landscape = "";
for (const [t, d] of Object.entries(sentimentData))
  landscape += `## ${d.name} (${t})\nAvg Sentiment: ${d.avg_sentiment.toFixed(3)}\nDistribution: ${d.positive}+/${d.neutral}n/${d.negative}-\n${d.articles.slice(0,5).join("\n")}\n\n`;

// 3. Positioning analysis
const analysis = await fetch(`${MV_BASE}/mave/chat`, { method: "POST", headers: MV_H,
  body: JSON.stringify({ message: `Brand strategist. Sentiment-aligned positioning.\n\nLANDSCAPE (our: ${OUR_TICKER}):\n\n${landscape}\n\n1. Sentiment Map\n2. Our Position\n3. Messaging Recommendations (3 angles)\n4. Timing\n5. Risk\n\nReference actual headline themes.` }),
}).then(r => r.json());

console.log(`\n${"=".repeat(60)}\nSENTIMENT-BASED POSITIONING ANALYSIS`);
console.log((analysis.content || "").slice(0, 2500));