def model_prices(pars: tuple,
args: EvalArgs,
model: str,
strict=False,
check=False,
bounds_only=True) -> np.ndarray:
return optimization.models[model](pars=pars,
args=args.as_tuple(),
strict=strict,
check=check,
bounds_only=bounds_only)
def estimate_model(pars: tuple, args: EvalArgs, model: str, metric: str, prices: np.ndarray) -> float:
"""
:param pars: model parameters
:param args: market parameters with strikes as args[1]
:param model: pricing model
:param metric: quality metric
:param prices: actual prices
:return: value of quality metric on passed parameters
"""
k = args.get_strikes()
if model not in models.keys():
raise Exception("Cannot use model " + model)
if metric not in metrics.keys():
raise Exception("Cannot use metric " + metric)
if (type(prices) is not np.ndarray) | (type(k) is not np.ndarray) | (len(prices) != len(k)):
raise Exception("strikes and prices should be np.arrays with same length")
return apply_metric(models[model](pars=pars, args=args.as_tuple()), prices, metric)