Martin-Molinero
d1a35e6281
Build & Test Lean / build (push) Has been cancelled
Regression Tests / build (push) Has been cancelled
* Continuous Futures Refactor. Live Mappings - Adding support for live mappings. LiveTradingDataFeed will handle any symbol mapping at the configuration layer and resubscribe through the IDQH - Refactoring continuous futures adding ContinuousFutureUniverse that will select the currently mapped security * Minor fixes - Remove addition of configurations in UniverseSelection step, leave resposability for universe. - LiveTradingDF future unit test will only assert slice data for non internal feeds. - ContinuousContractUniverse will respect internal option interest subscription * Address review
67 lines
3.2 KiB
Python
67 lines
3.2 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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### Basic Continuous Futures Template Algorithm
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### </summary>
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class BasicTemplateContinuousFutureAlgorithm(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._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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self._fast = self.SMA(self._continuousContract.Symbol, 3, Resolution.Daily)
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self._slow = self.SMA(self._continuousContract.Symbol, 10, Resolution.Daily)
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self._currentContract = None
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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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for changedEvent in data.SymbolChangedEvents.Values:
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if changedEvent.Symbol == self._continuousContract.Symbol:
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self.Log(f"SymbolChanged event: {changedEvent}")
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if not self.Portfolio.Invested:
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if self._fast.Current.Value > self._slow.Current.Value:
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self._currentContract = self.Securities[self._continuousContract.Mapped]
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self.Buy(self._currentContract.Symbol, 1)
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elif self._fast.Current.Value < self._slow.Current.Value:
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self.Liquidate()
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if self._currentContract is not None and self._currentContract.Symbol != self._continuousContract.Mapped:
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self.Log(f"{Time} - rolling position from {self._currentContract.Symbol} to {self._continuousContract.Mapped}")
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currentPositionSize = self._currentContract.Holdings.Quantity
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self.Liquidate(self._currentContract.Symbol)
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self.Buy(self._continuousContract.Mapped, currentPositionSize)
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self._currentContract = self.Securities[self._continuousContract.Mapped]
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def OnOrderEvent(self, orderEvent):
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self.Debug("Purchased Stock: {0}".format(orderEvent.Symbol))
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def OnSecuritiesChanged(self, changes):
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self.Debug(f"{self.Time}-{changes}")
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