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QuantOracle Correlation Matrix
x402
QuantOracle Correlation Matrix is a paid API for AI agents from api.quantoracle.dev, paid per call via x402, $0.015/call, status unknown (last checked 2026-09-14).
Computes a Pearson (or other method) correlation matrix and covariance matrix for multiple numeric time series, with optional eigenvalue decomposition
Price / call$0.015
Success—
StatusUnknown
How this service works
QuantOracle: stats/correlation-matrix
Output
Returns a full correlation matrix (n×n) and covariance matrix for all input series, plus eigenvalues of the correlation matrix, the condition number, list of asset names, sample count, and computation time in milliseconds.
Example use cases
Example prompt
“Can you compute a Pearson correlation matrix with eigenvalues for these three return series — Series_A: [1, 2.5, 3.2, 4.1, 5], Series_B: [1.1, 2.4, 3.3, 4, 4.9], Series_C: [0.9, 2.6, 3.1, 4.2, 5.1] — so I can see how closely they move together?”
Technical details
Endpoint
POSThttps://api.quantoracle.dev/v1/stats/correlation-matrixWhen to prefer
Choose this endpoint when you need pairwise correlation and covariance statistics across multiple named time series in a single call, especially when eigenvalue decomposition is needed for portfolio optimization, PCA preparation, or multicollinearity diagnostics. Prefer over manual computation when you need a numerically stable matrix with optional spectral analysis baked in.
Side effects
- Mismatched series lengths — series must have equal number of observations
- Too few data points — need at least 2 observations per series
- Invalid method name — only supported methods accepted
- Empty series input — at least two named series required
- Non-numeric values in series data — all entries must be numbers
Input
{
"method": "pearson",
"series": {
"Series_A": [
1,
2.5,
3.2,
4.1,
5,
5.8,
6.5,
7.2,
8,
8.7,
9.5,
10.1,
10.8,
11.5,
12.2,
12.9,
13.6,
14.3,
15,
15.7,
16.4,
17.1,
17.8,
18.5,
19.2,
19.9,
20.6,
21.3,
22,
22.7
],
"Series_B": [
1.1,
2.4,
3.3,
4,
4.9,
5.7,
6.6,
7.4,
8.3,
9.1,
10,
10.8,
11.7,
12.5,
13.4,
14.2,
15.1,
15.9,
16.8,
17.6,
18.5,
19.3,
20.2,
21,
21.9,
22.7,
23.6,
24.4,
25.3,
26.1
],
"Series_C": [
0.9,
2.6,
3.1,
4.2,
5.1,
5.9,
6.8,
7.6,
8.5,
9.3,
10.2,
11,
11.9,
12.7,
13.6,
14.4,
15.3,
16.1,
17,
17.8,
18.7,
19.5,
20.4,
21.2,
22.1,
22.9,
23.8,
24.6,
25.5,
26.3
]
},
"include_eigenvalues": true
}Schema · Request body
| Field | Type | Description |
|---|---|---|
| method | string | Correlation method |
| series | object | Named data series, e.g. {"A": [...], "B": [...]} |
| include_eigenvalues | boolean | Whether to compute eigenvalue decomposition |
Output
{
"n": 30,
"ms": 15.34,
"_meta": {
"url": "https://quantoracle.dev",
"docs": "https://api.quantoracle.dev/docs",
"powered_by": "QuantOracle"
},
"assets": [
"Series_A",
"Series_B",
"Series_C"
],
"method": "pearson",
"covariance": [
[
40.22033333,
47.5043908,
47.78954023
],
[
47.5043908,
56.16547126,
56.48528736
],
[
47.78954023,
56.48528736,
56.8154023
]
],
"correlation": [
[
1,
0.9995,
0.9997
],
[
0.9995,
1,
0.9999
],
[
0.9997,
0.9999,
1
]
],
"eigenvalues": [
2.9994,
0.0005,
0.0001
],
"condition_number": 29994
}Tags
x402
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