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POST
/
quantlib
/
portfolio
/
black-litterman
/
posterior
Black-Litterman Posterior Returns & Weights
curl --request POST \
  --url https://finceptbackend.share.zrok.io/quantlib/portfolio/black-litterman/posterior \
  --header 'Content-Type: application/json' \
  --header 'X-API-Key: <api-key>' \
  --data '
{
  "covariance_matrix": [
    [
      0.04,
      0.006,
      0.008,
      0.01
    ],
    [
      0.006,
      0.09,
      0.012,
      0.015
    ],
    [
      0.008,
      0.012,
      0.0625,
      0.018
    ],
    [
      0.01,
      0.015,
      0.018,
      0.16
    ]
  ],
  "market_caps": [
    5000000000,
    3000000000,
    2000000000,
    1000000000
  ],
  "risk_aversion": 2.5,
  "views": [
    {
      "type": "absolute",
      "asset": 0,
      "value": 0.09,
      "confidence": 0.7
    },
    {
      "type": "relative",
      "asset_long": 1,
      "asset_short": 2,
      "value": 0.03,
      "confidence": 0.6
    }
  ]
}
'
{
  "success": true,
  "data": {
    "posterior_returns": [
      0.085,
      0.12,
      0.098,
      0.142
    ],
    "optimal_weights": [
      0.32,
      0.28,
      0.25,
      0.15
    ]
  }
}

Authorizations

​
X-API-Key
string
header
required

API key for authentication. Get your key at https://finceptbackend.share.zrok.io/auth/register

Body

application/json
​
covariance_matrix
number[][]
required

Asset covariance matrix (annualized)

Example:
[
  [0.04, 0.006, 0.008, 0.01],
  [0.006, 0.09, 0.012, 0.015],
  [0.008, 0.012, 0.0625, 0.018],
  [0.01, 0.015, 0.018, 0.16]
]
​
market_caps
number[]
required

Market capitalizations for each asset

Example:
[
  5000000000,
  3000000000,
  2000000000,
  1000000000
]
​
risk_aversion
number
default:2.5

Market risk aversion parameter

Example:

2.5

​
views
object[] | null

List of investor views to incorporate. Can be empty for pure equilibrium.

Example:
[
  {
    "type": "absolute",
    "asset": 0,
    "value": 0.09,
    "confidence": 0.7
  },
  {
    "type": "relative",
    "asset_long": 1,
    "asset_short": 2,
    "value": 0.03,
    "confidence": 0.6
  }
]

Response

Black-Litterman posterior successfully computed

​
success
boolean
Example:

true

​
data
object