Axel HocksandClaude Opus 4.8
75d28827e8
Headless FastAPI-Backend (server.py + core/engine.py) mit Mobile-PWA (web/), Strategie-/Backtest-Suite und Doku. Secrets, DB, Logs und Laufzeit-State sind via .gitignore ausgeschlossen; Config-Vorlage: oil_widget_config.ini.example. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
77 lines
3.2 KiB
Python
77 lines
3.2 KiB
Python
"""
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core/analysis/news.py — Keyword-basiertes News-Sentiment
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"""
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from __future__ import annotations
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# Bullische Phrasen für WTI (Angebot ↓ / Nachfrage ↑ / Risiko ↑)
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NEWS_BULLISH_KW = {
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"supply cut", "production cut", "opec cut", "opec+ cut", "output cut",
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"sanction", "embargo", "ban on", "blockade", "shutdown", "outage",
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"disruption", "halt", "force majeure", "pipeline attack",
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"attack", "strike on", "missile", "drone strike", "tension escalat",
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"iran tension", "houthi", "red sea", "strait of hormuz", "war",
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"conflict escalat", "retaliat", "threat",
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"demand growth", "demand surge", "demand rise", "demand strong",
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"stockpile draw", "inventory draw", "stocks drop", "stocks fall",
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"stockpiles fall", "crude draw", "cushing draw", "eia draw",
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"pipeline shutdown", "refinery fire", "gulf of mexico storm",
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"hurricane", "winter storm", "cold snap",
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"oil surge", "oil rally", "oil jump", "oil soar", "oil spike",
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"crude rise", "crude rally", "wti rise", "wti surge", "wti rally",
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}
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# Bärische Phrasen für WTI (Angebot ↑ / Nachfrage ↓ / Entspannung)
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NEWS_BEARISH_KW = {
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"supply glut", "oversupply", "production increase", "output rise",
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"production boost", "opec boost", "opec+ unwind", "spr release",
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"strategic reserve release", "saudi increase",
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"us production record", "shale boom", "permian growth",
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"demand drop", "demand fall", "demand weak", "demand slump",
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"recession", "slowdown", "weak economy", "china slowdown",
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"stockpile build", "inventory build", "stocks rise", "stocks build",
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"crude build", "stockpiles rise", "cushing build", "eia build",
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"ceasefire", "truce", "deal reached", "agreement", "diplomatic",
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"talks resume", "easing tension", "sanction lift", "sanction relief",
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"oil drop", "oil plunge", "oil slide", "oil fall", "oil decline",
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"crude drop", "crude plunge", "wti fall", "wti slide", "oil crash",
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}
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def calc_news_sentiment(headlines: list, half_life_hours: float = 8.0) -> dict:
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"""
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Bewertet Headlines per Keyword-Matching mit altersgewichtetem Decay.
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score: -1.0 (klar bärisch) … +1.0 (klar bullisch)
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"""
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import time as _time
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if not headlines:
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return {"score": 0.0, "n_bull": 0.0, "n_bear": 0.0, "samples": []}
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now = _time.time()
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bull_w = bear_w = 0.0
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samples = []
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for h in headlines:
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text = (h.get("title_original") or h.get("title") or "").lower()
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if not text:
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continue
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age_h = max(0.0, (now - h.get("ts", now)) / 3600.0)
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weight = 0.5 ** (age_h / max(1.0, half_life_hours))
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b = sum(1 for kw in NEWS_BULLISH_KW if kw in text)
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s = sum(1 for kw in NEWS_BEARISH_KW if kw in text)
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if b > s:
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bull_w += weight * (1 + 0.3 * (b - 1))
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samples.append(("bull", h.get("title", "")[:70]))
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elif s > b:
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bear_w += weight * (1 + 0.3 * (s - 1))
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samples.append(("bear", h.get("title", "")[:70]))
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total = bull_w + bear_w
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score = 0.0 if total < 0.1 else (bull_w - bear_w) / total
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return {
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"score": max(-1.0, min(1.0, score)),
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"n_bull": round(bull_w, 1),
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"n_bear": round(bear_w, 1),
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"samples": samples[:5],
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}
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