# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals. # Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ''' The motivating idea for this Alpha Model is that a large price gap (here we use true outliers -- price gaps that whose absolutely values are greater than 3 * Volatility) is due to rebound back to an appropriate price or at least retreat from its brief extreme. Using a Coarse Universe selection function, the algorithm selects the top x-companies by Dollar Volume (x can be any number you choose) to trade with, and then uses the Standard Deviation of the 100 most-recent closing prices to determine which price movements are outliers that warrant emitting insights. This alpha is part of the Benchmark Alpha Series created by QuantConnect which are open sourced so the community and client funds can see an example of an alpha. ''' from clr import AddReference AddReference("System") AddReference("QuantConnect.Algorithm") AddReference("QuantConnect.Common") AddReference("QuantConnect.Indicators") from System import * from QuantConnect import * from QuantConnect.Algorithm import * from QuantConnect.Indicators import * from QuantConnect.Data.Market import TradeBar from QuantConnect.Algorithm.Framework import * from QuantConnect.Algorithm.Framework.Risk import * from QuantConnect.Algorithm.Framework.Alphas import * from QuantConnect.Orders.Fees import ConstantFeeModel from QuantConnect.Algorithm.Framework.Selection import * from QuantConnect.Algorithm.Framework.Execution import * from QuantConnect.Algorithm.Framework.Portfolio import PortfolioTarget, EqualWeightingPortfolioConstructionModel import numpy as np from datetime import timedelta, datetime class PriceGapMeanReversionAlpha(QCAlgorithm): def Initialize(self): self.SetStartDate(2018, 1, 1) #Set Start Date self.SetCash(100000) #Set Strategy Cash ## Initialize variables to be used in controlling frequency of universe selection self.week = None self.symbols = None self.SetWarmUp(100) ## Manual Universe Selection self.UniverseSettings.Resolution = Resolution.Minute self.SetUniverseSelection(CoarseFundamentalUniverseSelectionModel(self.CoarseSelectionFunction)) ## Set trading fees to $0 self.SetSecurityInitializer(lambda security: security.SetFeeModel(ConstantFeeModel(0))) ## Set custom Alpha Model self.SetAlpha(PriceGapMeanReversionAlphaModel()) ## Set equal-weighting Portfolio Construction Model self.SetPortfolioConstruction(EqualWeightingPortfolioConstructionModel()) ## Set Execution Model self.SetExecution(ImmediateExecutionModel()) ## Set Risk Management Model self.SetRiskManagement(NullRiskManagementModel()) def CoarseSelectionFunction(self, coarse): ## If it isn't a new week, return the same symbols current_week = self.Time.isocalendar()[1] if current_week == self.week: return self.symbols self.week = current_week ## If its a new month, then re-filter stocks by Dollar Volume sortedByDollarVolume = sorted(coarse, key=lambda x: x.DollarVolume, reverse=True) self.symbols = [ x.Symbol for x in sortedByDollarVolume[:25] ] return self.symbols class PriceGapMeanReversionAlphaModel: def __init__(self, *args, **kwargs): ''' Initialize variables and dictionary for Symbol Data to support algorithm's function ''' self.resolution = kwargs['resolution'] if 'resolution' in kwargs else Resolution.Minute self.prediction_interval = Time.Multiply(Extensions.ToTimeSpan(self.resolution), 5) ## Arbitrary self.symbolDataBySymbol = {} def Update(self, algorithm, data): insights = [] ## Loop through all Symbol Data objects for symbol, symbolData in self.symbolDataBySymbol.items(): if symbol not in data.Keys: ## Skip this slice if the data dictionary doesn't contain the symbol continue security = algorithm.Securities[symbol] ## Update the symbolData properties if not symbolData.Update(data, security): return insights ## Evaluate whether or not the price jump is expected to rebound up or return down, and emit insights accordingly if symbolData.DownTrend: insights.append(Insight(symbol, self.prediction_interval, InsightType.Price, InsightDirection.Down, symbolData.PriceJump, None)) elif symbolData.UpTrend: insights.append(Insight(symbol, self.prediction_interval, InsightType.Price, InsightDirection.Up, symbolData.PriceJump, None)) return insights def OnSecuritiesChanged(self, algorithm, changes): for security in changes.RemovedSecurities: if security.Symbol in self.symbolDataBySymbol.keys(): self.symbolDataBySymbol.pop(security.Symbol) algorithm.Log(f'{security.Symbol.Value} removed from Universe') history_request_symbols = [ x.Symbol for x in changes.AddedSecurities ] history_df = algorithm.History(history_request_symbols, 100, self.resolution) for security in changes.AddedSecurities: algorithm.Log(f'{security.Symbol.Value} added to Universe') if str(security.Symbol) not in history_df.index.get_level_values(0): continue history = history_df.loc[str(security.Symbol)] ## Create and initialize SymbolData objects symbolData = SymbolData(algorithm, security) self.symbolDataBySymbol[security.Symbol] = symbolData for tuple in history.itertuples(): bar = TradeBar(tuple.Index, security.Symbol, tuple.open, tuple.high, tuple.low, tuple.close, tuple.volume) symbolData.Initialize(bar, security) class SymbolData: def __init__(self, algorithm, security): self.symbol = security.Symbol self.close = 0 self.last_price = 0 self.volatility = algorithm.STD(self.symbol, 100) self.price_jump = 0 def Update(self, data, security): ## Check for any data events that would return a NoneBar in the Alpha Model Update() method if not data.Bars.ContainsKey(self.symbol) or data.Bars[self.symbol].Close == 0: return False price = data.Bars[self.symbol].Close self.last_price = self.close self.close = price self.price_jump = (self.close / self.last_price) - 1 return True def Initialize(self, data, security): self.volatility.Update(data.Time, data.Close) price = data.Close if self.last_price == 0: self.last_price = price self.close = price else: self.last_price = self.close self.close = price @property def PriceJump(self): return (self.close / self.last_price) - 1 @property def DownTrend(self): return (abs(100*self.price_jump) > 3*self.volatility.Current.Value) and (self.price_jump > 0) @property def UpTrend(self): return (abs(100*self.price_jump) > 3*self.volatility.Current.Value) and (self.price_jump < 0)