Calculate Shannon Entropy

Calculates the Shannon entropy of a discrete probability distribution, measuring the average information content or uncertainty. Shannon entropy is fundamental in information theory and is used to quantify the uncertainty in portfolio returns, market regimes, or trading signals. Higher entropy indicates greater uncertainty or diversity in outcomes.

Use Cases:

• Measure uncertainty in portfolio return distributions
• Quantify information content in market signals
• Assess diversity in asset allocations
• Evaluate predictability of financial time series
• Compare information efficiency across markets

Formula: H(X) = -Σ p(x) log(p(x))

Credits: 5 credits per request (Pro Tier) [Tier: ENTERPRISE, Credits: 10]

POST /quantlib/physics/entropy/shannon

cURL

portfolio_states

curl --request POST \ --url https://api.fincept.in/quantlib/physics/entropy/shannon \ --header 'Content-Type: application/json' \ --header 'X-API-Key: <api-key>' \ --data ' { "probabilities": [ 0.4, 0.3, 0.2, 0.1 ], "base": 2.718281828459045 } '

200

{
  "success": true,
  "data": {
    "shannon_entropy": 1.2799,
    "base": 2.718281828459045
  }
}

Authorizations

X-API-Key · string · header · required

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

Body

application/json

probabilities · number[] · required

Discrete probability distribution (must sum to 1)

Example:

[0.25, 0.25, 0.25, 0.25]

base · number · default: 2.718281828459045

Logarithm base for entropy calculation (e=2.718 for nats, 2 for bits, 10 for dits)

Example:

2.718281828459045

Response

200

application/json

Shannon entropy calculated successfully

success · boolean

Example:

true

data · object

Show child attributes