# 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. from clr import AddReference AddReference("System") AddReference("QuantConnect.Common") AddReference("QuantConnect.Indicators") AddReference("QuantConnect.Algorithm") AddReference("QuantConnect.Algorithm.Framework") from System import * from QuantConnect import * from QuantConnect.Indicators import * from QuantConnect.Data import * from QuantConnect.Data.Market import * from QuantConnect.Orders import * from QuantConnect.Algorithm import * from QuantConnect.Algorithm.Framework import * from QuantConnect.Algorithm.Framework.Execution import * from QuantConnect.Algorithm.Framework.Portfolio import * import numpy as np class StandardDeviationExecutionModel(ExecutionModel): '''Execution model that submits orders while the current market prices is at least the configured number of standard deviations away from the mean in the favorable direction (below/above for buy/sell respectively)''' def __init__(self, period = 60, deviations = 2, resolution = Resolution.Minute): '''Initializes a new instance of the StandardDeviationExecutionModel class Args: period: Period of the standard deviation indicator deviations: The number of deviations away from the mean before submitting an order resolution: The resolution of the STD and SMA indicators''' self.period = period self.deviations = deviations self.resolution = resolution self.targetsCollection = PortfolioTargetCollection() self.symbolData = {} # Gets or sets the maximum order value in units of the account currency. # This defaults to $20,000. For example, if purchasing a stock with a price # of $100, then the maximum order size would be 200 shares. self.MaximumOrderValue = 20000 def Execute(self, algorithm, targets): '''Executes market orders if the standard deviation of price is more than the configured number of deviations in the favorable direction. Args: algorithm: The algorithm instance targets: The portfolio targets''' self.targetsCollection.AddRange(targets) # for performance we check count value, OrderByMarginImpact and ClearFulfilled are expensive to call if self.targetsCollection.Count > 0: for target in self.targetsCollection.OrderByMarginImpact(algorithm): symbol = target.Symbol # calculate remaining quantity to be ordered unorderedQuantity = OrderSizing.GetUnorderedQuantity(algorithm, target) # fetch our symbol data containing our STD/SMA indicators data = self.symbolData.get(symbol, None) if data is None: return # check order entry conditions if data.STD.IsReady and self.PriceIsFavorable(data, unorderedQuantity): # get the maximum order size based on total order value maxOrderSize = OrderSizing.Value(data.Security, self.MaximumOrderValue) orderSize = np.min([maxOrderSize, np.abs(unorderedQuantity)]) remainder = orderSize % data.Security.SymbolProperties.LotSize missingForLotSize = data.Security.SymbolProperties.LotSize - remainder # if the amount we are missing for +1 lot size is 1M part of a lot size # we suppose its due to floating point error and round up # Note: this is required to avoid a diff with C# equivalent if missingForLotSize < (data.Security.SymbolProperties.LotSize / 1000000): remainder -= data.Security.SymbolProperties.LotSize # round down to even lot size orderSize -= remainder if orderSize != 0: algorithm.MarketOrder(symbol, np.sign(unorderedQuantity) * orderSize) self.targetsCollection.ClearFulfilled(algorithm) def OnSecuritiesChanged(self, algorithm, changes): '''Event fired each time the we add/remove securities from the data feed Args: algorithm: The algorithm instance that experienced the change in securities changes: The security additions and removals from the algorithm''' for added in changes.AddedSecurities: if added.Symbol not in self.symbolData: self.symbolData[added.Symbol] = SymbolData(algorithm, added, self.period, self.resolution) for removed in changes.RemovedSecurities: # clean up data from removed securities symbol = removed.Symbol if symbol in self.symbolData: if self.IsSafeToRemove(algorithm, symbol): data = self.symbolData.pop(symbol) algorithm.SubscriptionManager.RemoveConsolidator(symbol, data.Consolidator) def PriceIsFavorable(self, data, unorderedQuantity): '''Determines if the current price is more than the configured number of standard deviations away from the mean in the favorable direction.''' sma = data.SMA.Current.Value deviations = self.deviations * data.STD.Current.Value if unorderedQuantity > 0: return data.Security.BidPrice < sma - deviations else: return data.Security.AskPrice > sma + deviations def IsSafeToRemove(self, algorithm, symbol): '''Determines if it's safe to remove the associated symbol data''' # confirm the security isn't currently a member of any universe return not any([kvp.Value.ContainsMember(symbol) for kvp in algorithm.UniverseManager]) class SymbolData: def __init__(self, algorithm, security, period, resolution): symbol = security.Symbol self.Security = security self.Consolidator = algorithm.ResolveConsolidator(symbol, resolution) smaName = algorithm.CreateIndicatorName(symbol, f"SMA{period}", resolution) self.SMA = SimpleMovingAverage(smaName, period) algorithm.RegisterIndicator(symbol, self.SMA, self.Consolidator) stdName = algorithm.CreateIndicatorName(symbol, f"STD{period}", resolution) self.STD = StandardDeviation(stdName, period) algorithm.RegisterIndicator(symbol, self.STD, self.Consolidator) # warmup our indicators by pushing history through the indicators history = algorithm.History(symbol, period, resolution) if 'close' in history: history = history.close.unstack(0).squeeze() for time, value in history.iteritems(): self.SMA.Update(time, value) self.STD.Update(time, value)