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analyze.py — C25 Financial Signal Extractor

Pipeline (v2):
  1. Alias screen on title+desc for C25 mentions
  2. Coverage-spread filter: skip low-quality / one-sided articles
  3. NER upgrade: BERT-NER to confirm and expand matches
  4. Full-text fetch + re-screen
  5. FinBERT: quick sentiment — drop neutral < FINBERT_MIN_CONF
  6. Claude: structured extraction (tickers, magnitude, timeframe)
  7. yfinance: momentum check — direction alignment
  8. signal_score = sentiment_confidence × coverage_spread × momentum_alignment
  9. Alert if signal_score > ALERT_THRESHOLD

Usage:
  python3 analyze.py                  # analyze new articles only
  python3 analyze.py --force          # re-analyze everything
  python3 analyze.py --limit 20       # limit to 20 articles
  python3 analyze.py --dry-run        # show matches without storing
  python3 analyze.py --no-claude      # skip Claude step (no API cost)
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Find C25 companies mentioned in text.
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## Companies to analyze:
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## Article:
Title: :Ni<4E>Nu<4E>


Respond ONLY with valid JSON (no markdown fences):
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  "confirmed_tickers": ["NOVO-B"],
  "magnitude": 7,
  "timeframe": "days",
  "reasoning": "Two sentences max on financial impact and direction."
}

Fields:
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