The Problem
Trading communities on Reddit (r/wallstreetbets, r/stocks, r/options) generate real-time sentiment signals that move markets. Retail traders who catch WSB momentum early can ride the wave. But monitoring Reddit manually is impossible at scale. Reddit's API rate limits make it hard to poll frequently. By the time a ticker trend appears on mainstream financial news, the WSB-driven move has already happened.
The Scavio Solution
Build a pipeline that queries Scavio's Reddit endpoint for trading-related subreddits, extracts ticker mentions, scores sentiment per ticker, and flags unusual activity. Run every 30 minutes during market hours. Compare current mention volume against a 7-day rolling average to detect spikes. Output a ranked signal feed that highlights tickers with unusual Reddit momentum.
Before
Before the pipeline, the trader scrolled r/wallstreetbets manually each morning and evening. They caught major moves but missed intraday sentiment shifts. By the time a ticker was trending on WSB, the entry point had already passed.
After
After deploying the pipeline, ticker mention spikes are detected within 30 minutes. The trader reviews a ranked signal feed instead of scrolling Reddit. Three signals in the first month led to actionable trades that would have been missed with manual monitoring.
Who It Is For
Retail traders monitoring Reddit sentiment for trading signals. Quant teams building Reddit-based alternative data feeds. Fintech startups building social sentiment products.
Key Benefits
- Monitor Reddit trading subreddits every 30 minutes automatically
- Ticker mention spike detection against 7-day rolling average
- Sentiment scoring per ticker from post titles and snippets
- Ranked signal feed replaces manual Reddit browsing
- Cross-reference Reddit signals with Google News for confirmation
Python Example
import requests
from collections import Counter
from datetime import datetime
import re
API_KEY = "your_scavio_api_key"
TICKER_PATTERN = re.compile(r"\b[A-Z]{2,5}\b")
def scan_reddit_tickers(subreddit_query: str) -> dict:
res = requests.post(
"https://api.scavio.dev/api/v1/search",
headers={"x-api-key": API_KEY},
json={"platform": "reddit", "query": subreddit_query},
timeout=15,
)
res.raise_for_status()
posts = res.json().get("organic", [])
ticker_counts = Counter()
for post in posts:
text = f"{post.get('title', '')} {post.get('snippet', '')}"
tickers = TICKER_PATTERN.findall(text)
# Filter common English words
skip = {"THE", "AND", "FOR", "ARE", "BUT", "NOT", "YOU", "ALL", "CAN", "HAS", "HER", "WAS", "ONE", "OUR", "OUT", "HIS", "HOW", "ITS", "MAY", "NEW", "NOW", "OLD", "SEE", "WAY", "WHO", "DID", "GET", "HIM", "LET", "SAY", "SHE", "TOO", "USE", "DAD", "MOM"}
for t in tickers:
if t not in skip:
ticker_counts[t] += 1
return {"query": subreddit_query, "scanned_at": datetime.utcnow().isoformat(), "top_tickers": ticker_counts.most_common(10)}
signal = scan_reddit_tickers("wallstreetbets stocks today")
print(f"Top tickers at {signal['scanned_at']}:")
for ticker, count in signal["top_tickers"]:
print(f" ${ticker}: {count} mentions")JavaScript Example
const API_KEY = "your_scavio_api_key";
async function scanRedditTickers(query) {
const res = await fetch("https://api.scavio.dev/api/v1/search", {
method: "POST",
headers: { "x-api-key": API_KEY, "content-type": "application/json" },
body: JSON.stringify({ platform: "reddit", query }),
});
const data = await res.json();
const skip = new Set(["THE","AND","FOR","ARE","BUT","NOT","YOU","ALL","CAN","HAS"]);
const counts = {};
for (const post of data.organic ?? []) {
const text = `${post.title ?? ""} ${post.snippet ?? ""}`;
for (const match of text.matchAll(/\b[A-Z]{2,5}\b/g)) {
if (!skip.has(match[0])) counts[match[0]] = (counts[match[0]] ?? 0) + 1;
}
}
return Object.entries(counts).sort((a, b) => b[1] - a[1]).slice(0, 10);
}
const tickers = await scanRedditTickers("wallstreetbets stocks today");
tickers.forEach(([t, c]) => console.log(`$${t}: ${c} mentions`));Platforms Used
Community, posts & threaded comments from any subreddit
Web search with knowledge graph, PAA, and AI overviews