AlexCatarino
e5ed09924b
This algorithm shows how to implement a futures strategy in a framework algorithm. `FutureUniverseSelectionModel` portfolio selection model was implemented to provide a base class to help create other futures universe selection models.
97 lines
4.3 KiB
Python
97 lines
4.3 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.Algorithm.Framework")
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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.Orders import *
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from QuantConnect.Securities import *
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from QuantConnect.Algorithm import *
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from QuantConnect.Algorithm.Framework import *
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from QuantConnect.Algorithm.Framework.Alphas import *
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from QuantConnect.Algorithm.Framework.Portfolio import *
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from QuantConnect.Algorithm.Framework.Selection import *
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from Alphas.ConstantAlphaModel import ConstantAlphaModel
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from Selection.FutureUniverseSelectionModel import FutureUniverseSelectionModel
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from Execution.ImmediateExecutionModel import ImmediateExecutionModel
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from Risk.NullRiskManagementModel import NullRiskManagementModel
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from datetime import date, timedelta
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### <summary>
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### Basic template futures framework algorithm uses framework components
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### to define an algorithm that trades futures.
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### </summary>
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class BasicTemplateFuturesFrameworkAlgorithm(QCAlgorithmFramework):
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def Initialize(self):
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self.UniverseSettings.Resolution = Resolution.Minute
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self.SetStartDate(2013, 10, 7)
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self.SetEndDate(2013, 10, 11)
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self.SetCash(100000)
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# set framework models
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self.SetUniverseSelection(FrontMonthFutureUniverseSelectionModel(self.SelectFutureChainSymbols))
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self.SetAlpha(ConstantFutureContractAlphaModel(InsightType.Price, InsightDirection.Up, timedelta(1)))
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self.SetPortfolioConstruction(SingleSharePortfolioConstructionModel())
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self.SetExecution(ImmediateExecutionModel())
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self.SetRiskManagement(NullRiskManagementModel())
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def OnOrderEvent(self, fill):
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self.Log(f"{self.UtcTime}:: {fill}")
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def OnSecuritiesChanged(self, changes):
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pass#self.Log(f"{self.UtcTime}:: {changes}")
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def SelectFutureChainSymbols(self, utcTime):
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newYorkTime = Extensions.ConvertFromUtc(utcTime, TimeZones.NewYork)
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ticker = Futures.Indices.SP500EMini if newYorkTime.date() < date(2013, 10, 9) else Futures.Metals.Gold
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return [ Symbol.Create(ticker, SecurityType.Future, Market.USA) ]
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class FrontMonthFutureUniverseSelectionModel(FutureUniverseSelectionModel):
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'''Creates futures chain universes that select the front month contract and runs a user
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defined futureChainSymbolSelector every day to enable choosing different futures chains'''
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def __init__(self, select_future_chain_symbols):
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super().__init__(timedelta(1), select_future_chain_symbols)
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def Filter(self, filter):
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'''Defines the futures chain universe filter'''
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return (filter.FrontMonth()
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.OnlyApplyFilterAtMarketOpen())
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class ConstantFutureContractAlphaModel(ConstantAlphaModel):
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'''Implementation of a constant alpha model that only emits insights for future symbols'''
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def __init__(self, type, direction, period):
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super().__init__(type, direction, period)
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def ShouldEmitInsight(self, utcTime, symbol):
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# only emit alpha for future symbols and not underlying equity symbols
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if symbol.SecurityType != SecurityType.Future:
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return False
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return super().ShouldEmitInsight(utcTime, symbol)
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class SingleSharePortfolioConstructionModel(PortfolioConstructionModel):
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'''Portoflio construction model that sets target quantities to 1 for up insights and -1 for down insights'''
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def CreateTargets(self, algorithm, insights):
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targets = []
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for insight in insights:
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targets.append(PortfolioTarget(insight.Symbol, insight.Direction))
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return targets |