feat: technical, news, and social analysis agents

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

agents/news.py

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+import logging
+
+logger = logging.getLogger(__name__)
+
+POSITIVE_WORDS = {"surge", "rally", "bullish", "high", "gain", "profit", "grows", "adoption", "breakout", "soar", "record"}
+NEGATIVE_WORDS = {"crash", "drop", "bearish", "low", "loss", "fear", "down", "dump", "scam", "hack", "ban", "panic", "plunge"}
+
+class NewsAgent:
+    def analyze(self, articles: list[dict]) -> float:
+        if not articles:
+            return 50.0
+        try:
+            scores = []
+            for article in articles:
+                vote_score = self._vote_sentiment(article.get("sentiment_votes", {}))
+                text_score = self._text_sentiment(article.get("title", ""))
+                if vote_score is not None:
+                    scores.append(vote_score * 0.6 + text_score * 0.4)
+                else:
+                    scores.append(text_score)
+            return round(max(0, min(100, sum(scores) / len(scores))), 1)
+        except Exception as e:
+            logger.error(f"News analysis error: {e}")
+            return 50.0
+
+    def _vote_sentiment(self, votes: dict) -> float | None:
+        pos = votes.get("positive", 0)
+        neg = votes.get("negative", 0)
+        total = pos + neg
+        if total == 0:
+            return None
+        return (pos / total) * 100
+
+    def _text_sentiment(self, text: str) -> float:
+        words = set(text.lower().split())
+        pos = len(words & POSITIVE_WORDS)
+        neg = len(words & NEGATIVE_WORDS)
+        total = pos + neg
+        if total == 0:
+            return 50.0
+        return (pos / total) * 100