AlexCatarino
108 lines
4.7 KiB
Python
108 lines
4.7 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.Indicators")
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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.Data import *
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from QuantConnect.Indicators import *
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from System.Collections.Generic import List
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from QCAlgorithm import QCAlgorithm
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import decimal as d
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from datetime import datetime, timedelta
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from decimal import Decimal
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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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### <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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self.SetCash(25000)
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self.SetStartDate(2007,1,1)
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self.LastRotationTime = datetime.min
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self.RotationInterval = timedelta(days=30)
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self.first = True
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# these are the growth symbols we'll rotate through
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GrowthSymbols =["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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SafetySymbols = ["EDV", "SHY"] # "EDV" Vangaurd TSY 25yr, "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 symbol in list(set(GrowthSymbols) | set(SafetySymbols)):
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self.AddSecurity(SecurityType.Equity, symbol, Resolution.Minute)
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self.oneMonthPerformance = self.MOM(symbol, 30, Resolution.Daily)
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self.threeMonthPerformance = self.MOM(symbol, 90, Resolution.Daily)
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self.SymbolData.append([symbol, self.oneMonthPerformance, self.threeMonthPerformance])
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def OnData(self, data):
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# the first time we come through here we'll need to do some things such as allocation
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# 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.RotationInterval:
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self.LastRotationTime = self.Time
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orderedObjScores = sorted(self.SymbolData, key=lambda x: Score(x[1].Current.Value,x[2].Current.Value).ObjectiveScore(), reverse=True)
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for x in orderedObjScores:
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self.Log(">>SCORE>>" + x[0] + ">>" + str(Score(x[1].Current.Value,x[2].Current.Value).ObjectiveScore()))
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# pick which one is best from growth and safety symbols
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bestGrowth = orderedObjScores[0]
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if Score(bestGrowth[1].Current.Value,bestGrowth[2].Current.Value).ObjectiveScore() > 0:
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if (self.Portfolio[bestGrowth[0]].Quantity == 0):
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self.Log("PREBUY>>LIQUIDATE>>")
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self.Liquidate()
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self.Log(">>BUY>>" + str(bestGrowth[0]) + "@" + str(Decimal(100) * bestGrowth[1].Current.Value))
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qty = self.Portfolio.MarginRemaining / self.Securities[bestGrowth[0]].Close
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self.MarketOrder(bestGrowth[0], int(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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class Score(object):
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def __init__(self,oneMonthPerformanceValue,threeMonthPerformanceValue):
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self.oneMonthPerformance = oneMonthPerformanceValue
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self.threeMonthPerformance = threeMonthPerformanceValue
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def ObjectiveScore(self):
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weight1 = 100
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weight2 = 75
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return (weight1 * self.oneMonthPerformance + weight2 * self.threeMonthPerformance) / (weight1 + weight2) |