Martin-Molinero
b8b0d18993
* Continuous Future Contracts * Mapping approach * Tweaks WIP * Live mapping * Live mapping - Add support for live mapping, refreshing mapfiles - Fix future expiration functions - Adding unit tests * Update moq test package * Continuous futures price scaling * Refactor price factors scaling * Factor file related renames * Address reviews
72 lines
3.4 KiB
Python
72 lines
3.4 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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### Continuous Futures Regression algorithm. Asserting and showcasing the behavior of adding a continuous future
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### </summary>
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class ContinuousFutureRegressionAlgorithm(QCAlgorithm):
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'''Basic template algorithm simply initializes the date range and cash'''
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def Initialize(self):
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'''Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.'''
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self.SetStartDate(2013, 7, 1)
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self.SetEndDate(2014, 1, 1)
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self._mappings = []
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self._lastDateLog = -1
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self._continuousContract = self.AddFuture(Futures.Indices.SP500EMini,
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dataNormalizationMode = DataNormalizationMode.BackwardsRatio,
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dataMappingMode = DataMappingMode.LastTradingDay,
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contractDepthOffset= 0)
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def OnData(self, data):
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'''OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.
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Arguments:
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data: Slice object keyed by symbol containing the stock data
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'''
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if len(data.Keys) != 1:
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raise ValueError(f"We are getting data for more than one symbols! {','.join(data.Keys)}")
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for changedEvent in data.SymbolChangedEvents.Values:
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if changedEvent.Symbol == self._continuousContract.Symbol:
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self._mappings.append(changedEvent)
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self.Log(f"SymbolChanged event: {changedEvent}")
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if self._lastDateLog != self.Time.month:
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self._lastDateLog = self.Time.month
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self.Log(f"{self.Time}- {self._continuousContract.GetLastData()}")
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if self.Portfolio.Invested:
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self.Liquidate()
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elif self._continuousContract.HasData:
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# This works because we set this contract as tradable, even if it's a canonical security
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self.Buy(self._continuousContract.Symbol, 1)
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if self.Time.month == 1 and self.Time.year == 2013:
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response = self.History( [ self._continuousContract.Symbol ], 60 * 24 * 90)
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if response.empty:
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raise ValueError("Unexpected empty history response")
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def OnOrderEvent(self, orderEvent):
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if orderEvent.Status == OrderStatus.Filled:
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self.Debug("Purchased Stock: {0}".format(orderEvent.Symbol))
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def OnEndOfAlgorithm(self):
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expectedMappingCounts = 2
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if len(self._mappings) != expectedMappingCounts:
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raise ValueError(f"Unexpected symbol changed events: {self._mappings.count()}, was expecting {expectedMappingCounts}")
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