Overall Statistics Total Trades2Average Win0%Average Loss-0.24%Compounding Annual Return-72.803%Drawdown0.300%Expectancy-1Net Profit-0.240%Sharpe Ratio0Loss Rate100%Win Rate0%Profit-Loss Ratio0Alpha0Beta0Annual Standard Deviation0Annual Variance0Information Ratio0Tracking Error0Treynor Ratio0Total Fees\$0.50
```# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
#
# you may not use this file except in compliance with the License.
#
# Unless required by applicable law or agreed to in writing, software
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and

from System import *
from QuantConnect import *
from QuantConnect.Algorithm import *
from datetime import timedelta

class BasicTemplateOptionsAlgorithm(QCAlgorithm):
'''This example demonstrates how to add options for a given underlying equity security.
It also shows how you can prefilter contracts easily based on strikes and expirations.
It also shows how you can inspect the option chain to pick a specific option contract to trade.'''

def Initialize(self):
self.SetStartDate(2015, 12, 24)
self.SetEndDate(2015, 12, 24)
self.SetCash(100000)

self.symbol = option.Symbol

# set our strike/expiry filter for this option chain
option.SetFilter(-2, +2, timedelta(0), timedelta(180))

# use the underlying equity as the benchmark
self.SetBenchmark(equity.Symbol)

def OnData(self,slice):
if self.Portfolio.Invested: return

for kvp in slice.OptionChains:
if kvp.Key != self.symbol: continue
chain = kvp.Value

# we sort the contracts to find at the money (ATM) contract with farthest expiration
contracts = sorted(sorted(chain, \
key = lambda x: abs(chain.Underlying.Price - x.Strike)), \
key = lambda x: x.Expiry, reverse=True)