feat: add GNews sentiment signal for politics/tech/events markets

bot/data/news.py (new):
- NewsClient with in-memory cache (TTL=4h) to stay within 100 req/day limit
- _build_query(): strips dates, punctuation and stopwords from market question
- _score_headlines(): keyword-based pos/neg vote per article, averaged ∈ [-1, +1]
- Degrades to 0.0 on missing key, 403 quota, or network error

bot/strategy/bayesian.py:
- BayesianStrategy(news=NewsClient) — optional, backwards compatible
- Signal 4: GNews sentiment applied as direct log-odds shift (weight=1.5)
  so a ±1.0 sentiment score moves a 50% prior to 82%/18%
- +0.10 confidence boost when news signal is present
- NEWS_LOGODDS_WEIGHT constant documented at module level

bot/main.py:
- Instantiate NewsClient, pass to BayesianStrategy, close in finally block

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

bot/strategy/bayesian.py

@@ -17,6 +17,7 @@ from typing import Optional
 
 from bot.data.polymarket import Market
 from bot.data.external import ExternalSignals
+from bot.data.news import NewsClient
 
 log = logging.getLogger(__name__)
 
@@ -26,6 +27,11 @@ log = logging.getLogger(__name__)
 MIN_EDGE = 0.10  # 10% edge minimum
 MIN_CONFIDENCE = 0.55  # Minimum confidence in our estimate
 
+# Log-odds weight applied to the GNews sentiment score (range ±1.0).
+# A weight of 1.5 means a fully negative/positive signal shifts log-odds by ±1.5,
+# which moves a 50% prior to ~18%/82% — strong but not overwhelming.
+NEWS_LOGODDS_WEIGHT = 1.5
+
 
 @dataclass
 class TradingSignal:
@@ -53,8 +59,9 @@ class BayesianStrategy:
     to justify the fee + slippage cost (MIN_EDGE).
     """
 
-    def __init__(self) -> None:
+    def __init__(self, news: Optional[NewsClient] = None) -> None:
         self._signal_count = 0
+        self._news = news  # Optional; degrades gracefully when None or key missing
 
     async def evaluate(
         self,
@@ -165,16 +172,29 @@ class BayesianStrategy:
             adjustments.append(dom_adj)
             sources.append(f"BTC dom: {ext.btc_dominance:.1f}% (low → alt season)")
 
+        # Signal 4: GNews sentiment (politics / tech / events only)
+        # Applied as a direct log-odds shift — stronger signal than macro proxies.
+        # Weight NEWS_LOGODDS_WEIGHT=1.5 means a ±1.0 sentiment score shifts
+        # log-odds by ±1.5 (e.g. 50% prior → ~82% / ~18%).
+        news_log_adj = 0.0
+        if (is_politics or is_tech or is_events) and self._news is not None:
+            sentiment = await self._news.get_sentiment(market.question)
+            if abs(sentiment) > 0.05:
+                news_log_adj = sentiment * NEWS_LOGODDS_WEIGHT
+                sources.append(f"GNews: {sentiment:+.2f}")
+
         # Macro/politics/tech/events: cap confidence lower to reflect weaker signal quality
         if is_macro or is_politics or is_tech or is_events:
             confidence_cap = 0.65
         else:
             confidence_cap = 0.90
 
-        # Compute posterior using log-odds updating
+        # Compute posterior using log-odds updating.
+        # total_adj (BTC/F&G/dominance) is amplified ×2 because those are weak proxies.
+        # news_log_adj is applied at face value — it IS a direct log-odds signal.
         log_odds_prior = math.log(prior / (1 - prior))
         total_adj = sum(adjustments)
-        estimated_prob = _sigmoid(log_odds_prior + total_adj * 2)
+        estimated_prob = _sigmoid(log_odds_prior + total_adj * 2 + news_log_adj)
         estimated_prob = max(0.05, min(0.95, estimated_prob))
 
         # Compute edge
@@ -185,6 +205,9 @@ class BayesianStrategy:
         # Confidence based on signal agreement
         agreement = sum(1 for a in adjustments if (a > 0) == (total_adj > 0))
         confidence = min(confidence_cap, 0.4 + (agreement / max(len(adjustments), 1)) * 0.5)
+        # News signal available → boost confidence by 0.10 (news corroborates macro signals)
+        if news_log_adj != 0.0:
+            confidence = min(confidence_cap, confidence + 0.10)
 
         # Log evaluation result for every market
         action = "TRADE" if (abs_edge >= MIN_EDGE and confidence >= MIN_CONFIDENCE) else "SKIP"