Scavio for WSB Sentiment Backtesting Pipeline

The Problem

Traders acting on Reddit sentiment signals have no way to validate whether those signals have actual predictive value without building a historical dataset and running backtests.

How Scavio Helps

• Historical Reddit sentiment data collection for backtesting
• Correlation analysis between mention spikes and price movements
• Signal quality validation separates predictive patterns from noise
• Daily data collection costs under $2 for comprehensive coverage
• Cross-reference with Google News for signal confirmation

Relevant Platforms

Quick Start: Python Example

Here is a quick example searching Reddit for "WSB sentiment backtesting Reddit trading signal validation 2026":

import requests

API_KEY = "your_scavio_api_key"

response = requests.post(
    "https://api.scavio.dev/api/v1/reddit/search",
    headers={
        "x-api-key": API_KEY,
        "Content-Type": "application/json",
    },
    json={"query": query, "sort": "new"},
)

data = response.json()
for post in data["data"].get("posts", [])[:5]:
    print(f"r/{post['subreddit']} — {post['title']}")
    print(f"   by u/{post['author']}")

Built for Quantitative traders, data scientists building alternative data models, and fintech teams evaluating Reddit as a signal source

Scavio handles the search infrastructure — proxies, CAPTCHAs, rate limits, and anti-bot detection — so you can focus on building your wsb sentiment backtesting pipeline solution. The API returns structured JSON that is ready for processing, analysis, or feeding into AI agents.

Start with the free tier (50 credits on signup, no credit card required) and scale to paid plans when you need higher volume.