def optimize_model(model: str, info: List[Info], data: Data, metric: str,
day: int, rate: float, local: bool, use_fft: bool,
**kwargs) -> opt.OptimizeResult:
print(f"Optimizing {model} with {metric} on day {day}")
actual_calls = data.prices[True][day]
actual_puts = data.prices[False][day]
pricer = GenPricer(model=model,
market=EvalArgs.from_structure(data=data,
info=info,
rate=rate,
day=day),
use_fft=use_fft)
optimizer = opt.minimize if local else opt.differential_evolution
t0 = time()
result = pricer.optimize_pars(metric=metric,
actual_calls=actual_calls,
actual_puts=actual_puts,
bounds=par_bounds[model],
optimizer=optimizer,
**kwargs)
with open(hf.get_log_file_name(model=model, metric=metric), 'a+') as log:
hf.log_print(
f"Time spent for {model}, day {day}: {timedelta(seconds=(time() - t0))}\n",
out1=log)
return result
def cut_by_bs_delta(data: Data,
info: List[Info],
rate=0.008,
disp=False) -> Tuple[Data, List[Info]]:
for day, is_call in [(d, c) for d in range(len(info))
for c in [True, False]]:
pricer = gp.GenPricer(model='bs',
market=EvalArgs.from_structure(data=data,
info=info,
rate=rate,
day=day),
use_fft=False)
result: opt.OptimizeResult = pricer.optimize_pars(
metric='MAE',
actual_calls=data.prices[True][day],
actual_puts=data.prices[False][day],
bounds=config.par_bounds['bs'],
optimizer=opt.differential_evolution,
polish=True,
disp=disp)
bs_sigma = result.x[0]
bs_sigma = bs_sigma
deltas = bs.bs_delta(spot=info[day].spot,
strikes=data.strikes[is_call][day],
r=rate,
q=rate,
t=info[day].mat,
bs_sigma=bs_sigma,
is_call=is_call)
data.prices[is_call][day] = data.prices[is_call][day][
np.abs(deltas) >= .1]
data.strikes[is_call][day] = data.strikes[is_call][day][
np.abs(deltas) >= .1]
return data, info