| Overall Statistics |
|
Total Orders 13 Average Win 0.01% Average Loss -0.01% Compounding Annual Return -0.238% Drawdown 0.000% Expectancy -0.601 Start Equity 10000000 End Equity 9998191 Net Profit -0.018% Sharpe Ratio -36.357 Sortino Ratio -27.769 Probabilistic Sharpe Ratio 1.146% Loss Rate 80% Win Rate 20% Profit-Loss Ratio 1.00 Alpha -0.014 Beta -0.002 Annual Standard Deviation 0 Annual Variance 0 Information Ratio -8.93 Tracking Error 0.146 Treynor Ratio 8.398 Total Fees $9.00 Estimated Strategy Capacity $8000.00 Lowest Capacity Asset GOOCV 30IZW4DA2M6KM|GOOCV VP83T1ZUHROL Portfolio Turnover 0.06% |
#region imports
from AlgorithmImports import *
#endregion
# 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 datetime import timedelta
class IronButterflyAlgorithm(QCAlgorithm):
def initialize(self):
self.set_start_date(2017, 4, 1)
self.set_end_date(2017, 4, 30)
self.set_cash(10000000)
equity = self.add_equity("GOOG", Resolution.MINUTE)
option = self.add_option("GOOG", Resolution.MINUTE)
self.symbol = option.symbol
option.set_filter(-10, 10, timedelta(0), timedelta(30))
# use the underlying equity GOOG as the benchmark
self.set_benchmark(equity.symbol)
def on_data(self,slice):
if self.portfolio["GOOG"].quantity != 0:
self.liquidate()
if not self.portfolio.invested and self.time.hour != 0 and self.time.minute != 0:
for i in slice.option_chains:
if i.key != self.symbol: continue
chain = i.value
contract_list = [x for x in chain]
# if there is no optionchain or no contracts in this optionchain, pass the instance
if (slice.option_chains.count == 0) or (len(contract_list) == 0):
return
# sorted the optionchain by expiration date and choose the furthest date
expiry = sorted(chain,key = lambda x: x.expiry)[-1].expiry
# filter the call and put options from the contracts
call = [i for i in chain if i.right == 0 and i.expiry == expiry]
put = [i for i in chain if i.right == 1 and i.expiry == expiry]
# sorted the contracts according to their strike prices
call_contracts = sorted(call,key = lambda x: x.strike)
put_contracts = sorted(put,key = lambda x: x.strike)
if len(call_contracts) == 0 or len(put_contracts) == 0 : continue
# Sell 1 ATM Put
atm_put = sorted(put_contracts,key = lambda x: abs(chain.underlying.price - x.strike))[0]
self.sell(atm_put.symbol ,1)
# Sell 1 ATM Call
atm_call = sorted(call_contracts,key = lambda x: abs(chain.underlying.price - x.strike))[0]
self.sell(atm_call.symbol ,1)
# Buy 1 OTM Call
otm_call = call_contracts[-1]
self.buy(otm_call.symbol ,1)
# Buy 1 OTM Put
otm_put = put_contracts[0]
self.buy(otm_put.symbol ,1)
self.trade_contracts = [atm_put, atm_call, otm_call, otm_put]
def on_order_event(self, orderEvent):
self.log(str(orderEvent))