Nonlinear Least Squares Fitting

Fit a nonlinear parametric model to data by minimizing the sum of squared residuals. Supports exponential_fit (y = a·e^(bx) + c, useful for decay processes and yield curves) and polynomial_fit (y = p0 + p1·x + p2·x^2 + …, general curve fitting). Uses iterative optimization to find parameters that best fit the observed data. Essential for curve calibration, term structure fitting, volatility smile modeling, and empirical model estimation. [Tier: BASIC, Credits: 1]

POST /quantlib/numerical/least-squares/fit

Nonlinear Least Squares Fitting

cURL

curl --request POST \
  --url https://api.fincept.in/quantlib/numerical/least-squares/fit \
  --header 'Content-Type: application/json' \
  --header 'X-API-Key: <api-key>' \
  --data '
{
  "model": "exponential_fit",
  "x_data": [
    0,
    1,
    2,
    3,
    4
  ],
  "y_data": [
    5.1,
    3.8,
    2.9,
    2.3,
    2
  ],
  "x0": [
    5,
    -0.5,
    1.5
  ]
}
'

200

{
  "success": true,
  "data": {
    "parameters": [
      3.52,
      -0.48,
      1.62
    ],
    "cost": 0.0234,
    "iterations": 12,
    "converged": true
  }
}

Authorizations

X-API-Key · string · header · required

API key for authentication. Get your key at https://api.fincept.in/auth/register

Body

application/json

model · enum<string> · required

Model type to fit

Available options: exponential_fit, polynomial_fit

Example:

"exponential_fit"

x_data · number[] · required

X coordinates of data points

Example:

[0, 1, 2, 3, 4]

y_data · number[] · required

Y coordinates of data points (observations to fit)

Example:

[5.1, 3.8, 2.9, 2.3, 2]

x0 · number[] · required

Initial guess for model parameters (exponential: [a,b,c], polynomial: [p0,p1,p2,...])

Example:

[5, -0.5, 1.5]

Response

200

application/json

Successful Response

success · boolean

Example:

true

data · object

Show child attributes