## pricing.price()


Risk-loaded price of a cover from its simulated losses.


Usage


``` python
pricing.price(
    losses,
    assets,
    *,
    cost_of_capital=None,
    distortion=None,
)
```


The assets backing the loss are a distortion risk measure of it. The premium is either a pricing distortion of the loss, or set by a constant cost of capital [r](distributions.NegativeBinomial.md#prospicio.distributions.NegativeBinomial.r) on the capital `a - P`, which gives `P = (E[X] + r a) / (1 + r)`.


## Parameters


`losses: Sampled or PredictiveDistribution`  
Loss draws; for a [PredictiveDistribution](distributions.PredictiveDistribution.md#prospicio.distributions.PredictiveDistribution), its total.

`assets: Distortion`  
The measure that sets the assets, for example `Distortion.tvar(0.99)`.

`cost_of_capital: float = None`  
Positive rate. Give this or `distortion`.

`distortion: Distortion = None`  
Pricing distortion; it must load less than [assets](pricing.Price.md#prospicio.pricing.Price.assets).


## Returns


`Price`  


## Raises


`ValueError`  
Unless exactly one rule is given, or if the premium exceeds the assets.


## Examples

``` python
>>> from prospicio.distributions import Sampled
>>> from prospicio.pricing import price
>>> from prospicio.risk import Distortion
>>> p = price(Sampled([0.0, 0.0, 2.0, 6.0]), Distortion.tvar(0.5), cost_of_capital=0.25)
>>> p.premium, p.capital
```

(2.4, 1.6)