continue
key_date = datetime.date(evalDate.year(), evalDate.month(), evalDate.dayOfMonth())
maturity_dates = to_dt_dates(expiration_dates)
svi_dataset = cal_vols, put_vols, maturity_dates, spot, rf_months
daily_svi_dataset.update({key_date:svi_dataset})
dividend_ts = ql.YieldTermStructureHandle(ql.FlatForward(evalDate, 0.0, daycounter))
month_indexs = wind_data.get_contract_months(evalDate)
params_months = []
for i in range(4):
nbr_month = month_indexs[i]
data = data_months.get(i)
logMoneynesses = data[0]
totalvariance = data[1]
expiration_date = data[2]
ttm = daycounter.yearFraction(evalDate, expiration_date)
params = svi_util.get_svi_optimal_params(data, ttm, 30)
params_months.append(params)
daily_params.update({key_date:params_months})
dates.append(key_date)
print('Finished : ',evalDate)
print(params_months[0])
print(params_months[1])
print(params_months[2])
print(params_months[3])
#print(daily_params)
timebreak1 = timeit.default_timer()
print('calibration time : ',timebreak1-start)
print('daily_params = ',daily_params)
print('daily_svi_dataset = ',daily_svi_dataset)
svi_data.update_data(mdate, strike, moneyness, implied_vol, ttm,
totalvariance, close, open_price, spot, volum)
svi_dataset.update({to_dt_date(evalDate): svi_data})
calibrered_params = {}
for mdate in svi_data.dataSet.keys():
optimization_data = []
data_mdate = svi_data.dataSet.get(mdate)
logMoneynesses = data_mdate.moneyness
totalvariance = data_mdate.totalvariance
vol = data_mdate.volatility
#print('vols : ',vol)
optimization_data.append(logMoneynesses)
optimization_data.append(data_mdate.totalvariance)
ttm = data_mdate.ttm
params = svi_util.get_svi_optimal_params(optimization_data, ttm, 1)
#print('params : ',params)
calibrered_params.update({mdate: params})
a_star, b_star, rho_star, m_star, sigma_star = params
x_svi = np.arange(
min(logMoneynesses) - 0.005,
max(logMoneynesses) + 0.02, 0.1 / 100) # log_forward_moneyness
tv_svi = np.multiply(
a_star + b_star *
(rho_star *
(x_svi - m_star) + np.sqrt((x_svi - m_star)**2 + sigma_star**2)),
ttm)
vol_svi = np.sqrt(a_star + b_star *
(rho_star * (x_svi - m_star) + np.sqrt(
(x_svi - m_star)**2 + sigma_star**2)))
#plt.figure()