0ee9cf5707
Hook ai_score_news_batch() into /api/geo/news so every news fetch is enriched by GPT-4o-mini: corrected impact_score, ai_dir_energy/metals/indices, ai_resolution (ceasefire/peace deal flag), ai_insight (1 French sentence). Gracefully no-ops when OpenAI is not configured. Add _compute_ai_alignment() in geo_analyzer: for each pattern compares the news AI directional signals against the pattern's expected_move direction and produces a -25..+25 bonus injected into similarity/relevance scores. Contra-signals (e.g. peace deal → oil bearish while pattern expects oil spike) are flagged. Frontend GeoRadar: PatternRelevanceCard shows AI alignment badge (green = aligned, red = contra-signal) + base relevance diff + AI insights. NewsCard shows ai_insight, directional arrows per asset class (⛽↑ 🥇↓) and resolution badge when expanded. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
81 lines
2.7 KiB
Python
81 lines
2.7 KiB
Python
from fastapi import APIRouter, Query
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from typing import Optional, List
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from services.data_fetcher import fetch_geo_news, get_economic_calendar
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from services.geo_analyzer import compute_geo_risk_score, match_patterns, generate_trade_ideas, compute_pattern_relevance
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from services.database import get_custom_patterns
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from datetime import datetime, timezone, timedelta
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router = APIRouter(prefix="/api/geo", tags=["geopolitical"])
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_news_cache: dict = {"data": [], "ts": 0}
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@router.get("/news")
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def geo_news(force_refresh: bool = False):
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import time
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now = time.time()
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if not force_refresh and _news_cache["data"] and (now - _news_cache["ts"]) < 3600:
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return _news_cache["data"]
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news = fetch_geo_news()
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# Enrich with AI: corrects impact_score, adds ai_dir_energy/metals/indices,
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# ai_resolution (ceasefire/peace deal), ai_insight (1 French sentence).
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# Gracefully skips if OpenAI not configured.
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try:
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from services.ai_analyzer import ai_score_news_batch
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news = ai_score_news_batch(news)
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except Exception:
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pass
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_news_cache["data"] = news
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_news_cache["ts"] = now
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return news
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@router.get("/risk-score")
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def risk_score():
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news = _news_cache["data"] or fetch_geo_news()
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return compute_geo_risk_score(news)
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@router.get("/pattern-matches")
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def pattern_matches():
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news = _news_cache["data"] or fetch_geo_news()
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all_patterns = get_custom_patterns()
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return match_patterns(news, patterns=all_patterns)
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@router.get("/pattern-relevance")
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def pattern_relevance(days: int = 2):
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"""Return ALL active patterns with news-keyword relevance over the last N days."""
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all_news = _news_cache["data"] or fetch_geo_news()
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# Filter news to last N days
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if days > 0:
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cutoff = datetime.now(timezone.utc) - timedelta(days=days)
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recent: list = []
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for n in all_news:
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try:
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from email.utils import parsedate_to_datetime
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d = parsedate_to_datetime(str(n.get("date", "")))
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if d >= cutoff:
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recent.append(n)
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except Exception:
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recent.append(n)
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news = recent if recent else all_news
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else:
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news = all_news
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all_patterns = get_custom_patterns()
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return compute_pattern_relevance(news, patterns=all_patterns)
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@router.get("/trade-ideas")
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def trade_ideas():
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news = _news_cache["data"] or fetch_geo_news()
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all_patterns = get_custom_patterns()
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matches = match_patterns(news, patterns=all_patterns)
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geo_score = compute_geo_risk_score(news)
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return generate_trade_ideas(matches, geo_score)
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@router.get("/calendar")
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def calendar():
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return get_economic_calendar()
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