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KiHustle Covariance Calculator
x402
KiHustle Covariance Calculator is a paid API for AI agents from kihustle.tech, paid per call via x402, $0.002/call, status unknown (last checked 2026-09-14).
Computes the statistical covariance between two numerical data series
Price / call$0.002
Success—
StatusUnknown
How this service works
Kostenlose Guides, Solo-Playbooks und Artikel zu KI, Automation und Side Hustles — für Menschen, die mit echten Systemen online Einkommen aufbauen wollen. Transparent finanziert über faire Affiliate-Links.
Output
Returns a JSON object with a 'result' field containing the computed covariance value and a 'status' field confirming success. The covariance scalar indicates the direction and magnitude of linear co-movement between the two input series.
Example use cases
Portfolio risk correlation analysis
“I have monthly returns for two stocks over the past year. Series A is [0.02, 0.05, -0.01, 0.03, 0.07, -0.02, 0.04, 0.01, 0.06, -0.03, 0.02, 0.05] and series B is [0.01, 0.04, -0.02, 0.02, 0.06, -0.01, 0.03, 0.00, 0.05, -0.02, 0.01, 0.04]. Can you compute the covariance between them so I can assess portfolio risk?”
Machine learning feature relationship check
“Before training my model, I want to check how two features co-vary. Feature A values are [10, 20, 30, 40, 50] and feature B values are [15, 25, 28, 45, 52]. What is the covariance between these two feature arrays?”
Sales and ad spend co-movement
“I have weekly sales figures [500, 520, 610, 580, 700, 750] and corresponding ad spend numbers [100, 110, 140, 130, 160, 180]. Compute the covariance between these two series so I can see how closely they move together.”
Technical details
Endpoint
POSThttps://kihustle.tech/agents/api/v1/covariance-calculatorWhen to prefer
Choose this endpoint when you need a lightweight, pay-per-call statistical covariance computation without setting up a local math library or data science environment. It is ideal for agents embedded in workflows that occasionally need a quick covariance scalar between two numeric arrays, such as finance bots checking asset co-movement, ML pipelines evaluating feature relationships, or analytics agents comparing paired data sequences.
Side effects
- Mismatched array lengths between series_a and series_b may produce an error or undefined result
- Empty arrays may cause a computation error
- Non-numeric values in either series will likely cause a validation failure
- Very large arrays may increase latency beyond typical fast response times
- Payment failure (insufficient USDC balance) will block the request entirely
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| series_a | array | |
| series_b | array |
Output
Schema · Response
{
"type": "json",
"example": {
"result": "processed",
"status": "success"
}
}Tags
x402
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