Reddit & News Sentiment — Track the Narrative, Not Just the Numbers

name: sentiment_monitor
schedule: "0 */6 * * *"
steps:
  - fetch_reddit:
      subreddits:
        - wallstreetbets
        - investing
        - stocks
      tickers: "{{ watchlist_tickers }}"
      window_hours: 24
  - fetch_gdelt:
      companies: "{{ watchlist_names }}"
      window_hours: 24
  - calculate_velocity:
      baseline_days: 30
      alert_threshold_multiplier: 3
  - llm:
      prompt: |
        Analyze these Reddit posts and news mentions for my watchlist.
        Score overall sentiment (-1 to +1) for each ticker.
        Flag any ticker with mention velocity 3x above its 30-day baseline.
        Reddit data: {{ reddit_data }}
        News data: {{ gdelt_data }}
  - notify:
      subject: "💬 Sentiment Alert: {{ ticker }} — {{ velocity_note }}"
      condition: velocity_spike_detected

import httpx
import time

class RedditMonitor:
    def __init__(self, client_id: str, client_secret: str, user_agent: str):
        self.base = "https://oauth.reddit.com"
        token_r = httpx.post(
            "https://www.reddit.com/api/v1/access_token",
            data={"grant_type": "client_credentials"},
            auth=(client_id, client_secret),
            headers={"User-Agent": user_agent}
        )
        self.token = token_r.json()["access_token"]
        self.headers = {"Authorization": f"bearer {self.token}", "User-Agent": user_agent}

    def search_ticker(self, ticker: str, subreddit: str = "wallstreetbets", limit: int = 25) -> list:
        url = f"{self.base}/r/{subreddit}/search"
        params = {"q": ticker, "sort": "new", "limit": limit, "restrict_sr": "true"}
        r = httpx.get(url, params=params, headers=self.headers)
        posts = r.json().get("data", {}).get("children", [])
        return [{"title": p["data"]["title"], "score": p["data"]["score"],
                 "created": p["data"]["created_utc"], "url": p["data"]["url"]} for p in posts]

def query_gdelt_mentions(company_name: str, days: int = 1) -> list:
    """
    Query GDELT 2.0 GKG (Global Knowledge Graph) for company mentions.
    Uses the free GDELT API endpoint.
    """
    import httpx, urllib.parse
    query = urllib.parse.quote(f'"{company_name}"')
    url = f"https://api.gdeltproject.org/api/v2/doc/doc?query={query}&mode=artlist&maxrecords=25×pan={days}d&format=json"
    r = httpx.get(url)
    if r.status_code == 200:
        data = r.json()
        return data.get("articles", [])
    return []

from collections import defaultdict
from datetime import datetime, timedelta

def calculate_velocity(current_mentions: int, historical: list, baseline_days: int = 30) -> float:
    """Returns ratio of current mentions vs historical daily average."""
    if not historical:
        return 1.0
    avg = sum(historical) / len(historical)
    return current_mentions / avg if avg > 0 else float("inf")