Jing Wu
84 lines
3.8 KiB
Python
84 lines
3.8 KiB
Python
# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
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# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from clr import AddReference
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AddReference("System")
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AddReference("QuantConnect.Algorithm")
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AddReference("QuantConnect.Common")
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from System import *
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from QuantConnect import *
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from QuantConnect.Algorithm import *
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from QuantConnect.Securities.Option import OptionPriceModels
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from QuantConnect.Data.UniverseSelection import *
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from datetime import timedelta
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import decimal as d
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### <summary>
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### Example demonstrating how to access to options history for a given underlying equity security.
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### </summary>
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### <meta name="tag" content="using data" />
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### <meta name="tag" content="options" />
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### <meta name="tag" content="filter selection" />
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### <meta name="tag" content="history" />
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class BasicTemplateOptionsHistoryAlgorithm(QCAlgorithm):
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''' This example demonstrates how to get access to options history for a given underlying equity security.'''
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def Initialize(self):
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# this test opens position in the first day of trading, lives through stock split (7 for 1), and closes adjusted position on the second day
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self.SetStartDate(2015, 12, 24)
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self.SetEndDate(2015, 12, 24)
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self.SetCash(1000000)
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option = self.AddOption("GOOG")
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option.PriceModel = OptionPriceModels.CrankNicolsonFD()
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option.SetFilter(-2,2, timedelta(0), timedelta(180))
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self.underlying = self.AddEquity(option.Symbol.Underlying.Value)
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self.underlying.SetDataNormalizationMode(DataNormalizationMode.Raw)
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self.SetBenchmark(self.underlying.Symbol)
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def OnData(self,slice):
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if not self.Portfolio.Invested:
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for chain in slice.OptionChains:
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for contract in chain.Value:
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self.Log("{0},Bid={1} Ask={2} Last={3} OI={4} sigma={5:.3f} NPV={6:.3f} \
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delta={7:.3f} gamma={8:.3f} vega={9:.3f} beta={10:.2f} theta={11:.2f} IV={12:.2f}".format(
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contract.Symbol.Value,
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contract.BidPrice,
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contract.AskPrice,
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contract.LastPrice,
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contract.OpenInterest,
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self.underlying.VolatilityModel.Volatility,
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contract.TheoreticalPrice,
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contract.Greeks.Delta,
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contract.Greeks.Gamma,
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contract.Greeks.Vega,
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contract.Greeks.Rho,
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contract.Greeks.Theta / 365,
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contract.ImpliedVolatility))
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def OnOrderEvent(self, orderEvent):
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# Order fill event handler. On an order fill update the resulting information is passed to this method.
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# Order event details containing details of the events
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self.Log(str(orderEvent))
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def OnSecuritiesChanged(self, changes):
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if changes == SecurityChanges.None: return
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for change in changes.AddedSecurities:
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history = self.History(change.Symbol, 10, Resolution.Hour).sort_index(level='time', ascending=False)[:3]
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for i in range(len(history)):
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self.Log("History: " + str(history.iloc[i].name[0])
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+ ": " + str(history.iloc[i].name[1])
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+ " > " + str(history.iloc[i]['close'])) |