feat: implement CNN Fear & Greed Index collector

1 files changed, 62 insertions, 0 deletions

diff --git a/services/news-collector/src/news_collector/collectors/fear_greed.py b/services/news-collector/src/news_collector/collectors/fear_greed.py
new file mode 100644
index 0000000..305d416
--- /dev/null
+++ b/services/news-collector/src/news_collector/collectors/fear_greed.py
@@ -0,0 +1,62 @@
+"""CNN Fear & Greed Index collector."""
+
+import logging
+from dataclasses import dataclass
+from typing import Optional
+
+import aiohttp
+
+from news_collector.collectors.base import BaseCollector
+from shared.models import NewsItem
+
+logger = logging.getLogger(__name__)
+
+FEAR_GREED_URL = "https://production.dataviz.cnn.io/index/fearandgreed/graphdata"
+
+
+@dataclass
+class FearGreedResult:
+    fear_greed: int
+    fear_greed_label: str
+
+
+class FearGreedCollector(BaseCollector):
+    name = "fear_greed"
+    poll_interval = 3600  # 1 hour
+
+    async def is_available(self) -> bool:
+        return True
+
+    async def _fetch_index(self) -> Optional[dict]:
+        headers = {"User-Agent": "Mozilla/5.0"}
+        try:
+            async with aiohttp.ClientSession() as session:
+                async with session.get(FEAR_GREED_URL, headers=headers, timeout=aiohttp.ClientTimeout(total=10)) as resp:
+                    if resp.status != 200:
+                        return None
+                    return await resp.json()
+        except Exception:
+            return None
+
+    def _classify(self, score: int) -> str:
+        if score <= 20:
+            return "Extreme Fear"
+        if score <= 40:
+            return "Fear"
+        if score <= 60:
+            return "Neutral"
+        if score <= 80:
+            return "Greed"
+        return "Extreme Greed"
+
+    async def collect(self) -> Optional[FearGreedResult]:
+        data = await self._fetch_index()
+        if data is None:
+            return None
+        try:
+            fg = data["fear_and_greed"]
+            score = int(fg["score"])
+            label = fg.get("rating", self._classify(score))
+            return FearGreedResult(fear_greed=score, fear_greed_label=label)
+        except (KeyError, ValueError, TypeError):
+            return None