Reddit Stock Daily Scanner

import requests
import json
from pathlib import Path
from datetime import datetime

API_KEY = "your_scavio_api_key"
TICKERS = ["AAPL", "NVDA", "TSLA", "GME", "AMD", "PLTR", "AMZN", "MSFT", "META", "GOOG"]
SUBREDDITS = ["wallstreetbets", "stocks", "investing"]

BULLISH_WORDS = ["moon", "buy", "calls", "long", "bullish", "undervalued", "breakout"]
BEARISH_WORDS = ["puts", "short", "crash", "overvalued", "sell", "bearish", "dump"]

def search_ticker(ticker: str) -> list[dict]:
    results = []
    for sub in SUBREDDITS:
        res = requests.post(
            "https://api.scavio.dev/api/v1/search",
            headers={"x-api-key": API_KEY},
            json={"platform": "reddit", "query": f"{ticker} {sub}"},
            timeout=15,
        )
        res.raise_for_status()
        for post in res.json().get("organic", []):
            results.append({
                "title": post.get("title", ""),
                "subreddit": post.get("subreddit", sub),
                "score": post.get("score", 0),
                "link": post.get("link", ""),
            })
    return results

def classify_sentiment(title: str) -> str:
    lower = title.lower()
    bull = sum(1 for w in BULLISH_WORDS if w in lower)
    bear = sum(1 for w in BEARISH_WORDS if w in lower)
    if bull > bear:
        return "bullish"
    elif bear > bull:
        return "bearish"
    return "neutral"

def run():
    date = datetime.utcnow().strftime("%Y-%m-%d")
    ticker_data = {}

    for ticker in TICKERS:
        posts = search_ticker(ticker)
        sentiments = [classify_sentiment(p["title"]) for p in posts]
        ticker_data[ticker] = {
            "mentions": len(posts),
            "total_score": sum(p["score"] for p in posts),
            "bullish": sentiments.count("bullish"),
            "bearish": sentiments.count("bearish"),
            "neutral": sentiments.count("neutral"),
            "top_posts": sorted(posts, key=lambda x: x["score"], reverse=True)[:3],
        }

    # Sort by mention count
    ranked = sorted(ticker_data.items(), key=lambda x: x[1]["mentions"], reverse=True)

    report = {"date": date, "tickers_scanned": len(TICKERS), "tickers": dict(ranked)}
    Path(f"reddit_stocks_{date}.json").write_text(json.dumps(report, indent=2))

    print(f"Reddit Stock Scan {date}")
    for ticker, data in ranked[:5]:
        print(f"  {ticker}: {data['mentions']} mentions, {data['bullish']}B/{data['bearish']}b/{data['neutral']}N")

if __name__ == "__main__":
    run()

const API_KEY = "your_scavio_api_key";
const TICKERS = ["AAPL", "NVDA", "TSLA", "GME", "AMD"];

async function searchTicker(ticker) {
  const res = await fetch("https://api.scavio.dev/api/v1/search", {
    method: "POST",
    headers: { "x-api-key": API_KEY, "content-type": "application/json" },
    body: JSON.stringify({ platform: "reddit", query: `${ticker} wallstreetbets stocks` }),
  });
  if (!res.ok) throw new Error(`scavio ${res.status}`);
  return (await res.json()).organic ?? [];
}

const report = {};
for (const ticker of TICKERS) {
  const posts = await searchTicker(ticker);
  report[ticker] = { mentions: posts.length, topScore: Math.max(0, ...posts.map((p) => p.score ?? 0)) };
}
const sorted = Object.entries(report).sort((a, b) => b[1].mentions - a[1].mentions);
for (const [ticker, data] of sorted) console.log(`${ticker}: ${data.mentions} mentions, top score ${data.topScore}`);