AlexCatarino
9e6e2f9b22
In the Coarse Universe Selection of the following algorithms - ContingentClaimsAnalysisDefaultPredictionAlpha - GreenblattMagicFormulaAlpha - PriceGapMeanReversionAlpha - SykesShortMicroCapAlpha Universe.Unchanged is now used when the universe is not changed instead of saving a list of symbol and returning it. Other minor refactoring.
156 lines
6.6 KiB
Python
156 lines
6.6 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.Common")
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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.Algorithm.Framework import *
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from QuantConnect.Algorithm.Framework.Risk import *
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from QuantConnect.Algorithm.Framework.Alphas import *
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from QuantConnect.Orders.Fees import ConstantFeeModel
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from QuantConnect.Algorithm.Framework.Selection import *
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from QuantConnect.Algorithm.Framework.Execution import *
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from QuantConnect.Algorithm.Framework.Portfolio import *
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class PriceGapMeanReversionAlpha(QCAlgorithm):
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'''The motivating idea for this Alpha Model is that a large price gap (here we use true outliers --
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price gaps that whose absolutely values are greater than 3 * Volatility) is due to rebound
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back to an appropriate price or at least retreat from its brief extreme. Using a Coarse Universe selection
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function, the algorithm selects the top x-companies by Dollar Volume (x can be any number you choose)
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to trade with, and then uses the Standard Deviation of the 100 most-recent closing prices to determine
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which price movements are outliers that warrant emitting insights.
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This alpha is part of the Benchmark Alpha Series created by QuantConnect which are open
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sourced so the community and client funds can see an example of an alpha.'''
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def Initialize(self):
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self.SetStartDate(2018, 1, 1) #Set Start Date
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self.SetCash(100000) #Set Strategy Cash
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## Initialize variables to be used in controlling frequency of universe selection
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self.week = -1
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## Manual Universe Selection
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self.UniverseSettings.Resolution = Resolution.Minute
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self.SetUniverseSelection(CoarseFundamentalUniverseSelectionModel(self.CoarseSelectionFunction))
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## Set trading fees to $0
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self.SetSecurityInitializer(lambda security: security.SetFeeModel(ConstantFeeModel(0)))
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## Set custom Alpha Model
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self.SetAlpha(PriceGapMeanReversionAlphaModel())
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## Set equal-weighting Portfolio Construction Model
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self.SetPortfolioConstruction(EqualWeightingPortfolioConstructionModel())
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## Set Execution Model
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self.SetExecution(ImmediateExecutionModel())
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## Set Risk Management Model
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self.SetRiskManagement(NullRiskManagementModel())
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def CoarseSelectionFunction(self, coarse):
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## If it isn't a new week, return the same symbols
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current_week = self.Time.isocalendar()[1]
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if current_week == self.week:
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return Universe.Unchanged
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self.week = current_week
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## If its a new week, then re-filter stocks by Dollar Volume
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sortedByDollarVolume = sorted(coarse, key=lambda x: x.DollarVolume, reverse=True)
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return [ x.Symbol for x in sortedByDollarVolume[:25] ]
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class PriceGapMeanReversionAlphaModel:
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def __init__(self, *args, **kwargs):
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''' Initialize variables and dictionary for Symbol Data to support algorithm's function '''
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self.lookback = 100
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self.resolution = kwargs['resolution'] if 'resolution' in kwargs else Resolution.Minute
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self.prediction_interval = Time.Multiply(Extensions.ToTimeSpan(self.resolution), 5) ## Arbitrary
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self.symbolDataBySymbol = {}
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def Update(self, algorithm, data):
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insights = []
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## Loop through all Symbol Data objects
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for symbol, symbolData in self.symbolDataBySymbol.items():
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## Evaluate whether or not the price jump is expected to rebound
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if not symbolData.IsTrend(data):
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continue
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## Emit insights accordingly to the price jump sign
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direction = InsightDirection.Down if symbolData.PriceJump > 0 else InsightDirection.Up
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insights.append(Insight.Price(symbol, self.prediction_interval, direction, symbolData.PriceJump, None))
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return insights
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def OnSecuritiesChanged(self, algorithm, changes):
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# Clean up data for removed securities
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for removed in changes.RemovedSecurities:
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symbolData = self.symbolDataBySymbol.pop(removed.Symbol, None)
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if symbolData is not None:
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symbolData.RemoveConsolidators(algorithm)
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symbols = [x.Symbol for x in changes.AddedSecurities
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if x.Symbol not in self.symbolDataBySymbol]
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history = algorithm.History(symbols, self.lookback, self.resolution)
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if history.empty: return
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## Create and initialize SymbolData objects
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for symbol in symbols:
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symbolData = SymbolData(algorithm, symbol, self.lookback, self.resolution)
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symbolData.WarmUpIndicators(history.loc[symbol])
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self.symbolDataBySymbol[symbol] = symbolData
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class SymbolData:
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def __init__(self, algorithm, symbol, lookback, resolution):
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self.symbol = symbol
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self.close = 0
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self.last_price = 0
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self.PriceJump = 0
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self.consolidator = algorithm.ResolveConsolidator(symbol, resolution)
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self.volatility = StandardDeviation(f'{symbol}.STD({lookback})', lookback)
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algorithm.RegisterIndicator(symbol, self.volatility, self.consolidator)
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def RemoveConsolidators(self, algorithm):
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algorithm.SubscriptionManager.RemoveConsolidator(self.symbol, self.consolidator)
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def WarmUpIndicators(self, history):
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self.close = history.iloc[-1].close
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for tuple in history.itertuples():
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self.volatility.Update(tuple.Index, tuple.close)
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def IsTrend(self, data):
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## Check for any data events that would return a NoneBar in the Alpha Model Update() method
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if not data.Bars.ContainsKey(self.symbol):
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return False
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self.last_price = self.close
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self.close = data.Bars[self.symbol].Close
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self.PriceJump = (self.close / self.last_price) - 1
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return abs(100*self.PriceJump) > 3*self.volatility.Current.Value |