Changed news engine code a little

news_engine.py

@@ -1,6 +1,4 @@
 """
-Forex Signal Intelligence Platform - News & Sentiment Engine
-=============================================================
 Monitors economic calendar events and news sentiment to provide
 fundamental context for technical signals.
 
@@ -16,6 +14,9 @@ from typing import Dict, List, Optional, Tuple
 
 import requests
 
+# Import the transformers pipeline for FinBERT
+from transformers import pipeline
+
 from config import NewsConfig
 
 logger = logging.getLogger(__name__)
@@ -78,7 +79,7 @@ class NewsEngine:
     Provides fundamental context that modifies technical signal confidence.
     """
 
-    # Keywords that indicate directional sentiment
+    # Keywords that indicate directional sentiment (kept for fallback and keyword extraction)
     BULLISH_KEYWORDS = {
         "rate hike", "hawkish", "strong growth", "beat expectations",
         "above forecast", "surplus", "expansion", "employment gains",
@@ -120,6 +121,16 @@ class NewsEngine:
         self._session.headers.update({
             "User-Agent": "Mozilla/5.0 (compatible; ForexIntel/1.0)"
         })
+        
+        # Initialize the FinBERT LLM Pipeline
+        try:
+            logger.info("Initializing FinBERT NLP model. This may take a moment on startup...")
+            # We use ProsusAI/finbert as it is specifically fine-tuned on financial tone
+            self.nlp_pipeline = pipeline("sentiment-analysis", model="ProsusAI/finbert")
+            logger.info("FinBERT successfully loaded.")
+        except Exception as e:
+            logger.error(f"Failed to load FinBERT model: {e}. Defaulting to basic keyword analysis.")
+            self.nlp_pipeline = None
 
     def get_fundamental_context(
         self, currencies: Optional[List[str]] = None
@@ -348,14 +359,33 @@ class NewsEngine:
 
     def _analyze_headline_sentiment(self, headline: str) -> float:
         """
-        Simple keyword-based sentiment analysis.
-
+        Analyze headline sentiment using the FinBERT NLP model.
         Returns -1.0 (very bearish) to +1.0 (very bullish).
-
-        Note: This is intentionally simple. NLP models like FinBERT
-        would be more accurate but require large dependencies. This catches
-        the most impactful sentiment signals from headlines.
         """
+        if self.nlp_pipeline is None:
+            # Fallback to simple keyword logic if the LLM failed to load
+            return self._fallback_keyword_sentiment(headline)
+
+        try:
+            # The pipeline returns a list with a dict, e.g.: [{'label': 'positive', 'score': 0.85}]
+            result = self.nlp_pipeline(headline)[0]
+            label = result['label'].lower()
+            confidence = result['score']
+
+            if label == 'positive':
+                return confidence  # e.g., +0.85
+            elif label == 'negative':
+                return -confidence # e.g., -0.85
+            else:
+                # Neutral label
+                return 0.0
+
+        except Exception as e:
+            logger.debug(f"FinBERT sentiment analysis failed for '{headline}': {e}")
+            return self._fallback_keyword_sentiment(headline)
+
+    def _fallback_keyword_sentiment(self, headline: str) -> float:
+        """Original keyword-based fallback if FinBERT is unavailable."""
         headline_lower = headline.lower()
         bullish_score = sum(1 for kw in self.BULLISH_KEYWORDS if kw in headline_lower)
         bearish_score = sum(1 for kw in self.BEARISH_KEYWORDS if kw in headline_lower)
@@ -424,4 +454,4 @@ class NewsEngine:
             "CAD": ["USDCAD=X", "CADJPY=X"],
             "NZD": ["NZDUSD=X"],
         }
-        return pairs.get(currency, [])
+        return pairs.get(currency, [])