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# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.

# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.

#

# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0

#

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.

from clr import AddReference

AddReference("System")

AddReference("QuantConnect.Algorithm")

AddReference("QuantConnect.Common")

from System import *

from QuantConnect import *

from QuantConnect.Algorithm import *

from QuantConnect.Securities.Option import OptionPriceModels

### <summary>

### Example demonstrating how to define an option price model.

### </summary>

### <meta name="tag" content="using data" />

### <meta name="tag" content="options" />

### <meta name="tag" content="filter selection" />

### <meta name="tag" content="option price model" />

class BasicTemplateOptionsPriceModel(QCAlgorithm):

'''Example demonstrating how to define an option price model.'''

def Initialize(self):

self.SetStartDate(2020, 1, 1)

self.SetEndDate(2020, 1, 5)

self.SetCash(100000)

# Add the option

option = self.AddOption("AAPL")

self.optionSymbol = option.Symbol

# Add the initial contract filter

option.SetFilter(-3, +3, 0, 31)

# Define the Option Price Model

option.PriceModel = OptionPriceModels.CrankNicolsonFD()

#option.PriceModel = OptionPriceModels.BlackScholes()

#option.PriceModel = OptionPriceModels.AdditiveEquiprobabilities()

#option.PriceModel = OptionPriceModels.BaroneAdesiWhaley()

#option.PriceModel = OptionPriceModels.BinomialCoxRossRubinstein()

#option.PriceModel = OptionPriceModels.BinomialJarrowRudd()

#option.PriceModel = OptionPriceModels.BinomialJoshi()

#option.PriceModel = OptionPriceModels.BinomialLeisenReimer()

#option.PriceModel = OptionPriceModels.BinomialTian()

#option.PriceModel = OptionPriceModels.BinomialTrigeorgis()

#option.PriceModel = OptionPriceModels.BjerksundStensland()

#option.PriceModel = OptionPriceModels.Integral()

# Set warm up with 30 trading days to warm up the underlying volatility model

self.SetWarmUp(30, Resolution.Daily)

def OnData(self,slice):

'''OnData will test whether the option contracts has a non-zero Greeks.Delta'''

if self.IsWarmingUp or not slice.OptionChains.ContainsKey(self.optionSymbol):

return

chain = slice.OptionChains[self.optionSymbol]

if not any([x for x in chain if x.Greeks.Delta != 0]):

self.Log(f'No contract with Delta != 0')