The Problem
Trading agents need both news sentiment and market context to make decisions, but these live in completely different systems. News APIs charge separately, market data feeds have their own subscriptions, and social sentiment requires yet another vendor. Aggregating across sources means managing three authentication flows, three rate limit budgets, and three failure modes. The latency of sequential calls across vendors often exceeds the window where the signal is actionable. By the time your agent has assembled the full picture, the opportunity has moved.
The Scavio Solution
Scavio searches Google News, Reddit financial communities, and YouTube market commentary in parallel through a single endpoint. Your trading agent gets news headlines, Reddit sentiment, and video analysis context in one pipeline with one API key. The sub-two-second response time means your agent assembles context fast enough for the signal to still be actionable. You replace three vendor integrations with one, and the structured output feeds directly into your decision logic.
Before
Before Scavio, assembling a market context for a trading agent meant calling three separate APIs sequentially, managing three rate limits, and losing actionable seconds to integration latency.
After
After Scavio, a single pipeline pulls news, social sentiment, and video context in parallel. The trading agent gets a complete picture in under three seconds and acts while the signal is fresh.
Who It Is For
Quant developers and trading agent builders who need multi-source market context assembled fast enough for the signal to remain actionable. Anyone managing three separate data vendor integrations for one trading decision.
Key Benefits
- News, social sentiment, and video context from one API key
- Parallel fetching keeps total latency under three seconds
- Structured JSON feeds directly into trading decision logic
- One rate limit budget instead of three to manage
- Covers Google News, Reddit financial subs, and YouTube commentary
Python Example
import requests
from concurrent.futures import ThreadPoolExecutor
API_KEY = "your_scavio_api_key"
def search_platform(platform: str, query: str) -> dict:
res = requests.post(
"https://api.scavio.dev/api/v1/search",
headers={"x-api-key": API_KEY},
json={"platform": platform, "query": query},
timeout=10,
)
res.raise_for_status()
return {"platform": platform, "results": res.json().get("organic", [])[:5]}
def aggregate_market_context(ticker: str) -> dict:
queries = [
("google", f"{ticker} news today"),
("reddit", f"{ticker} stock discussion"),
("youtube", f"{ticker} market analysis"),
]
with ThreadPoolExecutor(max_workers=3) as pool:
futures = [pool.submit(search_platform, p, q) for p, q in queries]
results = {f.result()["platform"]: f.result()["results"] for f in futures}
return {
"ticker": ticker,
"news": results.get("google", []),
"sentiment": results.get("reddit", []),
"analysis": results.get("youtube", []),
}
context = aggregate_market_context("NVDA")
print(f"News: {len(context['news'])} | Reddit: {len(context['sentiment'])} | YouTube: {len(context['analysis'])}")
for item in context["news"]:
print(f" {item.get('title', '')}")JavaScript Example
const API_KEY = "your_scavio_api_key";
async function searchPlatform(platform, 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, query }),
});
if (!res.ok) throw new Error(`scavio ${res.status}`);
const data = await res.json();
return { platform, results: (data.organic ?? []).slice(0, 5) };
}
async function aggregateMarketContext(ticker) {
const [news, sentiment, analysis] = await Promise.all([
searchPlatform("google", `${ticker} news today`),
searchPlatform("reddit", `${ticker} stock discussion`),
searchPlatform("youtube", `${ticker} market analysis`),
]);
return {
ticker,
news: news.results,
sentiment: sentiment.results,
analysis: analysis.results,
};
}
const context = await aggregateMarketContext("NVDA");
console.log(`News: ${context.news.length} | Reddit: ${context.sentiment.length} | YouTube: ${context.analysis.length}`);
for (const item of context.news) console.log(` ${item.title}`);Platforms Used
Web search with knowledge graph, PAA, and AI overviews
Community, posts & threaded comments from any subreddit
YouTube
Video search with transcripts and metadata