Michael Handschuh
fdc866fda0
We didn't experience the expected performance improvements. Locally under unit test there was aboout an order of magnitude throughput increase, but when run against the history benchmark, this new approach was 60% slower. We're reverting this for now to perform further analysis and better understand the performance profiling of the python history stack.
85 lines
3.9 KiB
Python
85 lines
3.9 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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# add the initial contract filter
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option.SetFilter(-2,2, timedelta(0), timedelta(180))
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# set the pricing model for Greeks and volatility
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# find more pricing models https://www.quantconnect.com/lean/documentation/topic27704.html
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option.PriceModel = OptionPriceModels.CrankNicolsonFD()
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# set the warm-up period for the pricing model
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self.SetWarmUp(TimeSpan.FromDays(4))
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# set the benchmark to be the initial cash
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self.SetBenchmark(lambda x: 1000000)
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def OnData(self,slice):
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if self.IsWarmingUp: return
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if not self.Portfolio.Invested:
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for chain in slice.OptionChains:
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volatility = self.Securities[chain.Key.Underlying].VolatilityModel.Volatility
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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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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 OnSecuritiesChanged(self, changes):
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for change in changes.AddedSecurities:
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# only print options price
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if change.Symbol.Value == "GOOG": return
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history = self.History(change.Symbol, 10, Resolution.Minute).sort_index(level='time', ascending=False)[:3]
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for index, row in history.iterrows():
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self.Log("History: " + str(index[3])
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+ ": " + index[4].strftime("%m/%d/%Y %I:%M:%S %p")
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+ " > " + str(row.close)) |