Ronit Jain
15066ae5e1
* add data count properties * 'add history count property * assert data counts * update missing override * consider override/virtual cases * implement data count * add message handler for regression tests * use regression test message handler * set algorithm manager for regression test message handler * update data count * check if stats are present, check if algo manager is not null * update * add c# algo * make same as c# algo * use new line * logic shifted to RegressionTestMessageHandler * cleanup * auto cleanup * skip non deterministic data count * change data count * use inheritance * improve stats * update couht * add sma indicator to c# and customSMA to python * call base method before executing further * skip test * revert to original * add duplicate sma * skip regression test
74 lines
3.0 KiB
Python
74 lines
3.0 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 AlgorithmImports import *
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### <summary>
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### This example demonstrates how to add options for a given underlying equity security.
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### It also shows how you can prefilter contracts easily based on strikes and expirations, and how you
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### can inspect the option chain to pick a specific option contract to trade.
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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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class BasicTemplateOptionsDailyAlgorithm(QCAlgorithm):
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UnderlyingTicker = "GOOG"
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def Initialize(self):
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self.SetStartDate(2015, 12, 23)
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self.SetEndDate(2016, 1, 20)
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self.SetCash(100000)
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self.optionExpired = False
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equity = self.AddEquity(self.UnderlyingTicker, Resolution.Daily)
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option = self.AddOption(self.UnderlyingTicker, Resolution.Daily)
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self.option_symbol = option.Symbol
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# set our strike/expiry filter for this option chain
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option.SetFilter(lambda u: (u.CallsOnly().Strikes(0, 1).Expiration(0, 30)))
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# use the underlying equity as the benchmark
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self.SetBenchmark(equity.Symbol)
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def OnData(self,slice):
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if self.Portfolio.Invested: return
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chain = slice.OptionChains.GetValue(self.option_symbol)
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if chain is None:
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return
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# Grab us the contract nearest expiry
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contracts = sorted(chain, key = lambda x: x.Expiry)
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# if found, trade it
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if len(contracts) == 0 or not self.IsMarketOpen(contracts[0].Symbol): return
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symbol = contracts[0].Symbol
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self.MarketOrder(symbol, 1)
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def OnOrderEvent(self, orderEvent):
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self.Log(str(orderEvent))
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# Check for our expected OTM option expiry
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if orderEvent.Message == "OTM":
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# Assert it is at midnight 1/16 (5AM UTC)
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if orderEvent.UtcTime.month != 1 and orderEvent.UtcTime.day != 16 and orderEvent.UtcTime.hour != 5:
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raise AssertionError(f"Expiry event was not at the correct time, {orderEvent.UtcTime}")
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self.optionExpired = True
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def OnEndOfAlgorithm(self):
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# Assert we had our option expire and fill a liquidation order
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if not self.optionExpired:
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raise AssertionError("Algorithm did not process the option expiration like expected")
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