Jared Broad
120 lines
5.1 KiB
Python
120 lines
5.1 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.Core")
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AddReference("QuantConnect.Common")
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AddReference("QuantConnect.Algorithm")
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AddReference("QuantConnect.Indicators")
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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.Indicators import *
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from QuantConnect.Data.Market import *
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from datetime import datetime, timedelta
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### <summary>
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### Strategy example using a portfolio of ETF Global Rotation
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### </summary>
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### <meta name="tag" content="strategy example" />
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### <meta name="tag" content="momentum" />
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### <meta name="tag" content="using data" />
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class ETFGlobalRotationAlgorithm(QCAlgorithm):
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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(2007,01,01) #Set Start Date
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self.SetCash(25000) #Set Strategy Cash
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# we'll use this to tell us when the month has ended
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self.__first = True
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self.__lastRotationTime = datetime.min
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self.__rotationInternal = timedelta(days=30)
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# these are the growth symbols we'll rotate through
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self.GrowthSymbols = [
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"MDY", # US S&P mid cap 400
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"IEV", # iShares S&P europe 350
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"EEM", # iShared MSCI emerging markets
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"ILF", # iShares S&P latin america
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"EPP" ] # iShared MSCI Pacific ex-Japan
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# these are the safety symbols we go to when things are looking bad for growth
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self.SafetySymbols = [
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"EDV", # Vangaurd TSY 25yr+
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"SHY" ] # Barclays Low Duration TSY
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# we'll hold some computed data in these guys
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self.SymbolData = [ ]
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for ticker in self.GrowthSymbols + self.SafetySymbols:
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# ideally we would use daily data
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equity = self.AddEquity(ticker)
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oneMonthPerformance = self.MOM(equity.Symbol, 30, Resolution.Daily)
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threeMonthPerformance = self.MOM(equity.Symbol, 90, Resolution.Daily)
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self.SymbolData.append(SymbolData(equity.Symbol, oneMonthPerformance, threeMonthPerformance))
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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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try:
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# the first time we come through here we'll need to do some
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# things such as allocation and initializing our symbol data
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if self.__first:
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self.__first = False
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self.__lastRotationTime = self.Time
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return
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delta = self.Time - self.__lastRotationTime
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if delta > self.__rotationInternal:
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self.__lastRotationTime = self.Time
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for x in self.SymbolData: x.Update()
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# pick which one is best from growth and safety symbols
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orderedObjScores = sorted(self.SymbolData, key=lambda x: x.ObjectiveScore, reverse = True)
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for orderedObjScore in orderedObjScores:
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self.Log(">>SCORE>>{0}>>{1}".format(orderedObjScore.Symbol, orderedObjScore.ObjectiveScore))
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bestGrowth = orderedObjScores[0]
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if bestGrowth.ObjectiveScore > 0:
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if self.Portfolio[bestGrowth.Symbol].Quantity == 0:
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self.Log("PREBUY>>LIQUIDATE>>")
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self.Liquidate()
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qty = int(self.Portfolio.Cash / self.Securities[bestGrowth.Symbol].Close)
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self.Log(">>BUY>>{0}@{1}".format(bestGrowth.Symbol, (100.0 * bestGrowth.OneMonthPerformance.Current.Value)))
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self.MarketOrder(bestGrowth.Symbol, qty)
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else:
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# if no one has a good objective score then let's hold cash this month to be safe
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self.Log(">>LIQUIDATE>>CASH");
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self.Liquidate();
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except:
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self.Error("OnTradeBar: Error")
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class SymbolData:
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def __init__(self, symbol, oneMonthPerformance, threeMonthPerformance):
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self.Symbol = symbol
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self.OneMonthPerformance = oneMonthPerformance
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self.ThreeMonthPerformance = threeMonthPerformance
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self.ObjectiveScore = None
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def Update(self):
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# we weight the one month performance higher
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weight1 = 100
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weight2 = 75
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self.ObjectiveScore = (weight1 * self.OneMonthPerformance.Current.Value + weight2 * self.ThreeMonthPerformance.Current.Value) / (weight1 + weight2) |