| Overall Statistics |
|
Total Trades 3 Average Win 0% Average Loss -0.16% Compounding Annual Return 86.108% Drawdown 1.100% Expectancy -1 Net Profit 1.342% Sharpe Ratio 7.729 Probabilistic Sharpe Ratio 94.245% Loss Rate 100% Win Rate 0% Profit-Loss Ratio 0 Alpha 0.015 Beta -0.217 Annual Standard Deviation 0.073 Annual Variance 0.005 Information Ratio 12.428 Tracking Error 0.248 Treynor Ratio -2.593 Total Fees $3.00 |
# 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(2016, 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):
if self.Portfolio.Invested: return
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))