Initial project structure: MarketScanner - Fear-to-Fortune Trading Intelligence

Features:
- FastAPI backend with stocks, news, signals, watchlist, analytics endpoints
- React frontend with TailwindCSS dark mode trading dashboard
- Celery workers for news fetching, sentiment analysis, pattern detection
- TimescaleDB schema for time-series stock data
- Docker Compose setup for all services
- OpenAI integration for sentiment analysis

backend/app/workers/tasks/sentiment_tasks.py

@@ -0,0 +1,137 @@
+"""
+Sentiment analysis tasks
+"""
+
+from typing import Optional
+import structlog
+from openai import OpenAI
+
+from app.workers.celery_app import celery_app
+from app.core.config import settings
+
+logger = structlog.get_logger()
+
+
+def get_openai_client() -> Optional[OpenAI]:
+    """Get OpenAI client if configured."""
+    if settings.OPENAI_API_KEY:
+        return OpenAI(api_key=settings.OPENAI_API_KEY)
+    return None
+
+
+@celery_app.task(name="app.workers.tasks.sentiment_tasks.process_unanalyzed_news")
+def process_unanalyzed_news():
+    """Process all news articles that haven't been sentiment analyzed."""
+    logger.info("Starting sentiment analysis batch")
+    
+    # Placeholder - would query database for unprocessed articles
+    processed_count = 0
+    
+    logger.info("Sentiment analysis complete", processed=processed_count)
+    return {"processed": processed_count}
+
+
+@celery_app.task(name="app.workers.tasks.sentiment_tasks.analyze_sentiment")
+def analyze_sentiment(article_id: str, title: str, content: str):
+    """Analyze sentiment of a single article using OpenAI."""
+    logger.info("Analyzing sentiment", article_id=article_id)
+    
+    client = get_openai_client()
+    if not client:
+        logger.warning("OpenAI not configured, using fallback")
+        return fallback_sentiment_analysis(title, content)
+    
+    try:
+        # Prepare text (limit length)
+        text = f"Title: {title}\n\nContent: {content[:2000]}"
+        
+        response = client.chat.completions.create(
+            model=settings.OPENAI_MODEL,
+            messages=[
+                {
+                    "role": "system",
+                    "content": """You are a financial sentiment analyzer. Analyze the given news article and respond with a JSON object containing:
+                    - score: a number from -100 (extremely negative/panic) to +100 (extremely positive/euphoric)
+                    - label: one of "negative", "neutral", or "positive"
+                    - confidence: a number from 0 to 1 indicating confidence in the analysis
+                    - stocks: list of stock symbols mentioned (if any)
+                    - summary: one-sentence summary of the sentiment
+                    
+                    Focus on:
+                    - Financial impact
+                    - Market reaction implications
+                    - Panic/fear indicators
+                    - Earnings/guidance implications
+                    """
+                },
+                {
+                    "role": "user",
+                    "content": text
+                }
+            ],
+            response_format={"type": "json_object"},
+            temperature=0.3,
+        )
+        
+        result = response.choices[0].message.content
+        logger.info("Sentiment analyzed", article_id=article_id, result=result)
+        return result
+        
+    except Exception as e:
+        logger.error("Sentiment analysis failed", article_id=article_id, error=str(e))
+        return fallback_sentiment_analysis(title, content)
+
+
+def fallback_sentiment_analysis(title: str, content: str) -> dict:
+    """Simple keyword-based sentiment analysis as fallback."""
+    text = f"{title} {content}".lower()
+    
+    negative_words = [
+        "crash", "plunge", "collapse", "scandal", "fraud", "lawsuit",
+        "investigation", "bankruptcy", "layoffs", "miss", "decline",
+        "warning", "downgrade", "sell", "bear", "crisis", "fear",
+        "panic", "loss", "debt", "default", "recession"
+    ]
+    
+    positive_words = [
+        "surge", "rally", "growth", "profit", "beat", "upgrade",
+        "buy", "bull", "record", "breakout", "opportunity",
+        "dividend", "expansion", "innovation", "deal", "acquisition"
+    ]
+    
+    neg_count = sum(1 for word in negative_words if word in text)
+    pos_count = sum(1 for word in positive_words if word in text)
+    
+    total = neg_count + pos_count
+    if total == 0:
+        score = 0
+        label = "neutral"
+    else:
+        score = ((pos_count - neg_count) / total) * 100
+        if score < -20:
+            label = "negative"
+        elif score > 20:
+            label = "positive"
+        else:
+            label = "neutral"
+    
+    return {
+        "score": round(score, 2),
+        "label": label,
+        "confidence": min(0.3 + (total * 0.1), 0.7),  # Low confidence for fallback
+        "stocks": [],
+        "summary": "Analyzed using keyword matching (fallback)",
+    }
+
+
+@celery_app.task(name="app.workers.tasks.sentiment_tasks.batch_analyze")
+def batch_analyze(article_ids: list):
+    """Analyze multiple articles in batch."""
+    logger.info("Starting batch analysis", count=len(article_ids))
+    
+    results = []
+    for article_id in article_ids:
+        # Would fetch article from database and analyze
+        results.append({"article_id": article_id, "status": "pending"})
+    
+    return {"analyzed": len(results), "results": results}