Overall Statistics |
Total Trades 185 Average Win 0.60% Average Loss -0.37% Compounding Annual Return -19.223% Drawdown 20.400% Expectancy -0.018 Net Profit -1.245% Sharpe Ratio 0.126 Probabilistic Sharpe Ratio 41.763% Loss Rate 63% Win Rate 37% Profit-Loss Ratio 1.63 Alpha 0.421 Beta 0.631 Annual Standard Deviation 0.821 Annual Variance 0.675 Information Ratio 0.751 Tracking Error 0.807 Treynor Ratio 0.164 Total Fees $185.00 |
# 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 datetime import timedelta ### <summary> ### 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, 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="filter selection" /> class BasicTemplateOptionsAlgorithm(QCAlgorithm): def Initialize(self): self.SetStartDate(2016, 1, 1) self.SetEndDate(2018, 1, 10) self.SetCash(100000) option = self.AddOption("GOOG") self.option_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("GOOG") def OnData(self,slice): for kvp in slice.OptionChains: if kvp.Key != self.option_symbol: continue chain = kvp.Value # we sort the contracts to find at the money (ATM) contract with farthest expiration contracts = sorted(sorted(sorted(chain, \ key = lambda x: abs(chain.Underlying.Price - x.Strike)), \ key = lambda x: x.Expiry, reverse=True), \ key = lambda x: x.Right, reverse=True) # if found, trade it if len(contracts) == 0: continue symbol = contracts[0].Symbol self.MarketOrder(symbol, 1) self.MarketOnCloseOrder(symbol, -1) def OnOrderEvent(self, orderEvent): self.Log(str(orderEvent))