def tuning():
times = 5
metric = 'RMSE'
optimizer = differential_evolution
actual_puts = np.ndarray([])
spot = 1200
t = .76
r = .008
q = r
is_call = True
logfile = open(f'{root_dir}/params/time_benchmark_{datetime.now()}.log', 'w')
for n in [1, 10, 25, 50, 100, 200]:
strikes = np.array([i * 50 + 500 for i in range(n)])
args = (spot, strikes, t, r, q, is_call)
market = EvalArgs.from_tuple((spot, strikes, t, r, q, is_call))
call_prices = FFT(model='vg', args=args).price((0.838288226409, -0.188041460262, 0.179096605713))
def bs_func():
GenPricer(model='bs', market=market, use_fft=False) \
.optimize_pars(metric=metric, optimizer=optimizer, bounds=par_bounds['bs'],
actual_puts=actual_puts, actual_calls=call_prices,
polish=True)
t0 = time()
unit = td_decorator(func=bs_func, times=times, seconds=True, log_each=True)()
hf.log_print(f'BS unit time for {n} strike(s): {unit}, real time: {(time() - t0) / times}', logfile)
for model in ['heston', 'vg', 'ls']:
def func():
GenPricer(model=model, market=market, use_fft=True) \
.optimize_pars(metric=metric, optimizer=optimizer, bounds=par_bounds[model],
actual_puts=actual_puts, actual_calls=call_prices,
polish=True, disp=True, maxiter=50)
t0 = time()
hf.log_print(f"{model}: {td_decorator(func=func, times=times, seconds=True, log_each=True)() / unit}"
f" units, real time: {time() - t0}", logfile)
hf.log_print("\n", logfile)
logfile.close()
def test_r():
# noinspection PyShadowingNames
def prepare_args(a: List[str]) -> tuple:
spot = float(a[0])
strike = float(a[1])
tau = float(a[2])
r = float(a[3])
q = float(a[4])
is_call = True if a[5] == 'TRUE' else False
return spot, strike, tau, r, q, is_call
def params2tuple(params: tuple) -> tuple:
return params + tuple(None for _ in range(5 - len(params)))
cases = read_r_data()
names = ['model', 'p1', 'p2', 'p3', 'p4', 'p5',
'spot', 'strike', 't', 'r', 'q', 'is call',
'R answer', 'Python answer', 'diff', 'rel diff', 'is correct']
with open('sanity_log.txt', 'w') as log, open('sanity.csv', 'w') as out:
out.write('; '.join(names) + "\n")
for i, case in enumerate(cases):
correct = True
model = str(case[0])
data = case[1:-1]
answer = float(case[-1])
if model.lower() == "heston":
# func = he.price_heston
pars = tuple(map(lambda x: float(x), data[:5]))
args = prepare_args(data[5:])
elif model.lower() == "vg":
# func = vg.price_vg
pars = tuple(map(lambda x: float(x), data[:3]))
args = prepare_args(data[3:])
elif model.lower() == "ls":
# func = ls.price_ls
pars = tuple(map(lambda x: float(x), data[:2]))
args = prepare_args(data[2:])
else:
raise Exception(f"Can't recognise model {model}")
market = EvalArgs.from_tuple(args)
pricer = GenPricer(model=model.lower(), market=market, use_fft=True)
# calculated = float(func(pars, args))
calculated = pricer.price_call(pars) if market.is_call else pricer.price_put(pars)
assert len(calculated) == 1
diff = abs(answer - calculated)
if diff > 1e-2 * answer and diff > 1e-3 and not (answer < 0 and calculated == 0):
correct = False
log.write(f"Sanity test failed with case: {', '.join(case)}\n"
f"\tR answer: {answer}\n"
f"\tPython answer: {calculated}\n"
f"\tDiff: {diff}\n\n")
log.flush()
p1, p2, p3, p4, p5 = params2tuple(params=pars)
spot, strike, t, r, q, is_call = args
row = f'{model};{p1};{p2};{p3};{p4};{p5};{spot};{strike};{t};{r};{q};' \
f'{is_call};{answer};{calculated};{diff};' \
f'{diff / answer if answer != 0 else "Inf"};{"+" if correct else "-"}\n'
out.write(row)
out.flush()
if i % 1000 == 0:
print(f"{i / len(cases):.{3}}")