def iv(series,S, cd_price,option_type=None):
if option_type is None:
if series['期权类型'] == 'C':
option_type = c.OptionType.CALL
elif series['期权类型'] == 'P':
option_type = c.OptionType.PUT
else:
option_type = None
black = QlBlackFormula(eval_date, series['到期日'], option_type, S, series['行权价'], rf=rf)
iv = black.estimate_vol(series[cd_price])
return iv
def _get_pricing_engine(self, spot):
dt_maturity = self.maturitydt()
strike = self.applicable_strike()
option_type = self.option_type()
if self.exercise_type == OptionExerciseType.EUROPEAN:
black_formula = QlBlackFormula(
dt_eval=self.eval_date,
dt_maturity=dt_maturity,
option_type=option_type,
spot=spot,
strike=strike,
rf=self.rf
)
return black_formula
else:
binomial = QlBinomial(
dt_eval=self.eval_date,
dt_maturity=dt_maturity,
option_type=option_type,
option_exercise_type=OptionExerciseType.AMERICAN,
spot=spot,
strike=strike,
rf=self.rf,
n=800
)
return binomial
def _set_pricing_engine(self):
if self.pricing_engine is None:
dt_maturity = self.maturitydt()
strike = self.applicable_strike()
option_type = self.option_type()
spot = self.underlying_close()
if self.exercise_type == OptionExerciseType.EUROPEAN:
black_formula = QlBlackFormula(
dt_eval=self.eval_date,
dt_maturity=dt_maturity,
option_type=option_type,
spot=spot,
strike=strike,
rf=self.rf
)
self.pricing_engine = black_formula
else:
binomial = QlBinomial(
dt_eval=self.eval_date,
dt_maturity=dt_maturity,
option_type=option_type,
option_exercise_type=OptionExerciseType.AMERICAN,
spot=spot,
strike=strike,
rf=self.rf,
n=800
)
self.pricing_engine = binomial
def fun_htb_rate_adjusted_iv(self, df_series: pd.DataFrame,
option_type: OptionType, htb_r: float):
ttm = df_series[Util.AMT_TTM]
K = df_series[Util.AMT_APPLICABLE_STRIKE]
S = df_series[Util.AMT_UNDERLYING_CLOSE] * math.exp(-htb_r * ttm)
dt_eval = df_series[Util.DT_DATE]
dt_maturity = df_series[Util.DT_MATURITY]
if option_type == OptionType.CALL:
C = df_series[Util.AMT_CALL_QUOTE]
if self.exercise_type == OptionExerciseType.EUROPEAN:
pricing_engine = QlBlackFormula(dt_eval, dt_maturity,
OptionType.CALL, S, K, self.rf)
else:
# pricing_engine = QlBinomial(dt_eval, dt_maturity, OptionType.CALL, OptionExerciseType.AMERICAN, S, K,
# rf=self.rf)
pricing_engine = QlBAW(dt_eval,
dt_maturity,
OptionType.CALL,
OptionExerciseType.AMERICAN,
S,
K,
rf=self.rf)
iv = pricing_engine.estimate_vol(C)
else:
P = df_series[Util.AMT_PUT_QUOTE]
if self.exercise_type == OptionExerciseType.EUROPEAN:
pricing_engine = QlBlackFormula(dt_eval, dt_maturity,
OptionType.PUT, S, K, self.rf)
else:
# pricing_engine = QlBinomial(dt_eval, dt_maturity, OptionType.PUT, OptionExerciseType.AMERICAN, S, K,
# rf=self.rf)
pricing_engine = QlBAW(dt_eval,
dt_maturity,
OptionType.PUT,
OptionExerciseType.AMERICAN,
S,
K,
rf=self.rf)
iv = pricing_engine.estimate_vol(P)
return iv
import math
import datetime
from PricingLibrary.BlackFormular import BlackFormula
from PricingLibrary.EngineQuantlib import QlBlackFormula
from PricingLibrary.BlackCalculator import BlackCalculator
import back_test.model.constant as c
dt_eval = datetime.date(2017, 1, 1)
dt_maturity = datetime.date(2017, 4, 1)
spot_price = 120
strike_price = 100
volatility = 0.3 # the historical vols or implied vols
dividend_rate = 0
risk_free_rate = 0.03
option_price = 30.0
black_formula = BlackFormula(dt_eval, dt_maturity, c.OptionType.CALL,
spot_price, strike_price, option_price)
iv1 = black_formula.ImpliedVolApproximation()
estimated_price1 = BlackCalculator(dt_eval, dt_maturity, strike_price,
c.OptionType.CALL, spot_price, iv1).NPV()
ql_black = QlBlackFormula(dt_eval, dt_maturity, c.OptionType.CALL, spot_price,
strike_price)
iv2, estimated_price2 = ql_black.estimate_vol(option_price)
print(iv1, estimated_price1)
print(iv2, estimated_price2)
binomial_tree = QlBinomial(base_option_call.eval_date,
base_option_call.maturitydt(),
base_option_call.option_type(),
exercise_type,
spot,
base_option_call.strike(),
vol=init_vol,
rf=rf,
n=1000)
iv_call = binomial_tree.estimate_vol(base_option_call.mktprice_close())
else:
cd_source = 'quantlib'
black_formula = QlBlackFormula(
dt_eval=base_option_call.eval_date,
dt_maturity=base_option_call.maturitydt(),
option_type=base_option_call.option_type(),
spot=spot,
strike=base_option_call.strike(),
rf=rf)
try:
iv_call = black_formula.estimate_vol(
base_option_call.mktprice_close())
except:
iv_call = 0.0
print(base_option_call.id_instrument(), base_option_call.eval_date,
iv_call)
res = {
'dt_date': base_option_call.eval_date,
'name_code': name_code,
'id_underlying': base_option_call.id_underlying(),
'cd_option_type': 'call',
strike = 1000.0 # 行权价
option_exercise_type = c.OptionExerciseType.EUROPEAN # 行权方式
option_type = c.OptionType.CALL # 期权类型
buy_write = c.BuyWrite.BUY # 持仓头寸
vol = 0.2 # 波动率
rf = 0.03 # 利率
dt_eval = datetime.date(2018, 1, 6) # 估值日
dt_maturity = dt_eval + datetime.timedelta(days=50)
spot = 1000.0 # 标的价格
black_formula = QlBlackFormula(dt_eval=dt_eval,
dt_maturity=dt_maturity,
option_type=option_type,
spot=spot,
strike=strike,
vol=vol,
rf=rf)
delta = black_formula.Delta(vol)
last_delta = delta
last_spot = spot
res = []
print('ww model,spot从1000到2000调仓信息')
print('-' * 100)
print("%20s %20s %20s" % ('spot', 'dS', 'gamma'))
print('-' * 100)
for spot in np.arange(1000.0, 2000.0, 0.5):
black_formula = QlBlackFormula(dt_eval=dt_eval,
dt_maturity=dt_maturity,
option_type=option_type,