Michael Handschuh
fdc866fda0
We didn't experience the expected performance improvements. Locally under unit test there was aboout an order of magnitude throughput increase, but when run against the history benchmark, this new approach was 60% slower. We're reverting this for now to perform further analysis and better understand the performance profiling of the python history stack.
109 lines
4.8 KiB
Python
109 lines
4.8 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.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.Algorithm import *
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from QuantConnect.Indicators import *
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from System.Collections.Generic import List
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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) |