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Vextorium Market Sentiment Analyzer
x402
Vextorium Market Sentiment Analyzer is a paid API for AI agents from api.vextorium.com, paid per call via x402, $0.006/call, status unknown (last checked 2026-10-01).
Analyzes any text (news, tweets, posts, any language) for financial market sentiment using a local Qwen2.5-7B AI model, returning bullish/bearish/neutral verdict, score from -1 to 1, confidence, mentioned assets, and a short reason.
Price / call$0.006
Success—
StatusUnknown
How this service works
Market sentiment of any text (news, posts, tweets; any language) via local AI (Qwen3-8B): bullish / bearish / neutral, score -1 to 1, confidence, mentioned assets and a short reason.
Output
Returns a JSON object with: 'sentimiento' (bullish/bearish/neutral string), 'puntuacion' (float score from -1 to 1), 'confianza' (confidence float 0 to 1), 'activos_mencionados' (array of mentioned asset names), 'motivo' (short text reason for the classification), 'modelo' (model name used), and 'aviso' (optional warning string if applicable).
Example use cases
Crypto news sentiment triage
“Analyze the sentiment of this news headline for me: 'SEC delays Ethereum spot ETF decision amid regulatory uncertainty' — is it bullish, bearish, or neutral, what's the score, and how confident is the model?”
Social media signal extraction
“Check this tweet for market sentiment: 'Solana is crashing, whales are dumping hard, get out now' — give me the sentiment, score from -1 to 1, confidence, and any assets mentioned.”
Multilingual financial post analysis
“I have this Spanish financial article excerpt: 'El precio del Bitcoin sigue subiendo impulsado por la demanda institucional.' Can you tell me the sentiment, confidence, and which assets are mentioned?”
Technical details
Endpoint
POSThttps://api.vextorium.com/sentimiento-pago?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need financial market sentiment analysis on arbitrary text in any language, especially when you want a structured output including score, confidence, and asset extraction in a single call. It is well-suited for crypto and DeFi-focused sentiment pipelines, multilingual content, and when you want local AI inference (Qwen2.5-7B) rather than a cloud LLM dependency.
Side effects
- Empty or missing 'texto' field returns an error
- Extremely long text may exceed model context window
- Ambiguous or non-financial text may return neutral with low confidence
- Network or inference timeout if local model is busy
- Non-JSON body type may cause parsing errors
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Tags
x402
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