Overall Statistics |
Total Trades 124 Average Win 0.25% Average Loss -0.32% Compounding Annual Return 13.213% Drawdown 3.900% Expectancy 0.157 Net Profit 3.100% Sharpe Ratio 1.611 Loss Rate 35% Win Rate 65% Profit-Loss Ratio 0.79 Alpha -0.003 Beta 0.694 Annual Standard Deviation 0.064 Annual Variance 0.004 Information Ratio -0.959 Tracking Error 0.052 Treynor Ratio 0.149 Total Fees $124.00 |
# 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 OptionStrategies from datetime import datetime, timedelta ### <summary> ### This algorithm demonstrate how to use Option Strategies (e.g. OptionStrategies.Straddle) helper classes to batch send orders for common strategies. ### It also shows how you can prefilter contracts easily based on strikes and expirations, and how you can inspect the ### option chain to pick a specific option contract to trade. ### </summary> ### <meta name="tag" content="using data" /> ### <meta name="tag" content="options" /> ### <meta name="tag" content="option strategies" /> ### <meta name="tag" content="filter selection" /> class BasicTemplateOptionStrategyAlgorithm(QCAlgorithm): def Initialize(self): # Set the cash we'd like to use for our backtest self.SetCash(10000) # Start and end dates for the backtest. self.SetStartDate(2017,1,1) self.SetEndDate(2017,4,1) # Add assets you'd like to see option = self.AddOption("SPY") self.option_symbol = option.Symbol self.AddEquity("SPY", Resolution.Minute) # set our strike/expiry filter for this option chain #option.SetFilter(-2, +2, timedelta(2), timedelta(30)) option.SetFilter(lambda universe: universe.IncludeWeeklys().Strikes(-2, 2).Expiration(timedelta(2), timedelta(30))) # use the underlying equity as the benchmark self.SetBenchmark("SPY") self.Schedule.On(self.DateRules.EveryDay("SPY"), \ self.TimeRules.AfterMarketOpen("SPY", 30), \ Action(self.MarketOpenPut)) self.Schedule.On(self.DateRules.EveryDay("SPY"), \ self.TimeRules.BeforeMarketClose("SPY", 10), \ Action(self.MarketClose)) def OnData(self,slice): self.option_data = slice #if not self.Portfolio.Invested: # for kvp in slice.OptionChains: # chain = kvp.Value # contracts = sorted(sorted(chain, key = lambda x: abs(chain.Underlying.Price - x.Strike)), # key = lambda x: x.Expiry, reverse=False) # if len(contracts) == 0: continue # self.Log(contracts[0]) # atmStraddle = contracts[0] # if atmStraddle != None: # self.Sell(OptionStrategies.Straddle(self.option_symbol, atmStraddle.Strike, atmStraddle.Expiry), 2) #else: # self.Liquidate() def MarketOpenCall(self): self.Log(self.option_data.OptionChains) for i in self.option_data.OptionChains: self.Log("Option Chain") #self.Log(i) #if i.Key != self.underlyingsymbol: continue chain = i.Value call = [x for x in chain if x.Right == 0] # sorted the contracts according to their expiration dates and choose the ATM options contracts = sorted(sorted(call, \ key = lambda x: abs(chain.Underlying.Price - x.Strike)), \ key = lambda x: x.Expiry) self.Log(contracts) if len(contracts) == 0: continue self.contract = contracts[0] self.MarketOrder(self.contract.Symbol, 1) return def MarketOpenPut(self): self.Log(self.option_data.OptionChains) for i in self.option_data.OptionChains: self.Log("Option Chain") #self.Log(i) #if i.Key != self.underlyingsymbol: continue chain = i.Value put = [x for x in chain if x.Right == 1] # sorted the contracts according to their expiration dates and choose the ATM options contracts = sorted(sorted(put, \ key = lambda x: abs(chain.Underlying.Price - x.Strike)), \ key = lambda x: x.Expiry) self.Log(contracts) if len(contracts) == 0: continue self.contract = contracts[0] self.MarketOrder(self.contract.Symbol, -1) return def MarketClose(self): #if self.contract is not None and self.Portfolio[self.contract].Invested: # self.Sell(self.contract, 1) self.Liquidate() def OnOrderEvent(self, orderEvent): self.Log(str(orderEvent))