feat: productionize local service — fix signal pipeline, lower thresholds, add company-name ticker extraction

- Point Ollama to local instance via host.docker.internal, use gemma3 model
- Remove Docker Ollama service (using host's Ollama instead)
- Add company-name-to-ticker mapping (Apple→AAPL, Tesla→TSLA, etc.) for RSS articles
- Lower signal thresholds for faster feedback with paper trading:
  - FinBERT confidence: 0.6→0.4, signal strength: 0.3→0.15
  - News strategy: article_count 2→1, confidence 0.5→0.3, score ±0.3→±0.15
- Fix market data BarSet access bug (BarSet.__contains__ returns False incorrectly)
- Fix market data SIP feed error by switching to IEX feed for free Alpaca accounts
- Fix nginx proxy routing for /api/auth/* to api-gateway /auth/*
- Add seed_sample_data script
- Update tests for new thresholds and alpaca mock modules

services/api_gateway/routes/news.py

@@ -20,10 +20,13 @@ async def list_news(
     max_score: float | None = Query(default=None, ge=-1.0, le=1.0),
     page: int = Query(default=1, ge=1),
     per_page: int = Query(default=20, ge=1, le=100),
+    page_size: int | None = Query(default=None, ge=1, le=100),
 ) -> dict:
     """Recent scored articles with optional filters."""
     from shared.models.news import Article, ArticleSentiment
 
+    effective_per_page = page_size if page_size is not None else per_page
+
     db = request.app.state.db_session_factory
     async with db() as session:
         # Base query joining articles with sentiments
@@ -54,34 +57,35 @@ async def list_news(
             count_query = count_query.where(ArticleSentiment.score <= max_score)
 
         total = (await session.execute(count_query)).scalar() or 0
-        offset = (page - 1) * per_page
-        query = query.offset(offset).limit(per_page)
+        offset = (page - 1) * effective_per_page
+        query = query.offset(offset).limit(effective_per_page)
 
         result = await session.execute(query)
         rows = result.all()
 
-    return {
-        "articles": [
-            {
-                "id": str(article.id),
-                "source": article.source,
-                "url": article.url,
-                "title": article.title,
-                "published_at": (
-                    article.published_at.isoformat()
-                    if article.published_at
-                    else None
-                ),
-                "fetched_at": article.fetched_at.isoformat(),
-                "ticker": sentiment.ticker,
-                "sentiment_score": sentiment.score,
-                "confidence": sentiment.confidence,
-                "model_used": sentiment.model_used,
-            }
-            for article, sentiment in rows
-        ],
-        "total": total,
-        "page": page,
-        "per_page": per_page,
-        "pages": (total + per_page - 1) // per_page if per_page else 0,
-    }
+        return {
+            "articles": [
+                {
+                    "id": str(article.id),
+                    "source": article.source,
+                    "url": article.url,
+                    "title": article.title,
+                    "published_at": (
+                        article.published_at.isoformat()
+                        if article.published_at
+                        else None
+                    ),
+                    "fetched_at": article.fetched_at.isoformat(),
+                    "ticker": sentiment.ticker,
+                    "sentiment_score": sentiment.score,
+                    "confidence": sentiment.confidence,
+                    "model_used": sentiment.model_used,
+                }
+                for article, sentiment in rows
+            ],
+            "total": total,
+            "page": page,
+            "page_size": effective_per_page,
+            "per_page": effective_per_page,
+            "pages": (total + effective_per_page - 1) // effective_per_page if effective_per_page else 0,
+        }