# 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.Framework") from System import * from QuantConnect import * from QuantConnect.Indicators import * from QuantConnect.Data.Market import TradeBar from QuantConnect.Orders import * from QuantConnect.Algorithm.Framework.Execution import OrderSizing from QuantConnect.Algorithm.Framework.Portfolio import PortfolioTargetCollection import numpy as np class StandardDeviationExecutionModel: '''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 target in self.targetsCollection: 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 # ensure we're receiving price data before submitting orders if data.Security.Price == 0: 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)]) # round down to even lot size orderSize -= orderSize % data.Security.SymbolProperties.LotSize if orderSize != 0: algorithm.MarketOrder(symbol, np.sign(unorderedQuantity) * orderSize) # check to see if we're done with this target unorderedQuantity = OrderSizing.GetUnorderedQuantity(algorithm, target) if unorderedQuantity == 0: self.targetsCollection.Remove(target.Symbol) 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 removed in changes.RemovedSecurities: # clean up data from removed securities if removed.Symbol in self.symbolData: if self.IsSafeToRemove(algorithm, removed.Symbol): data = self.symbolData.pop(removed.Symbol) algorithm.SubscriptionManager.RemoveConsolidator(removed.Symbol, data.Consolidator) addedSymbols = [] for added in changes.AddedSecurities: if added.Symbol not in self.symbolData: self.symbolData[added.Symbol] = SymbolData(algorithm, added, self.period, self.resolution) addedSymbols.append(added.Symbol) if len(addedSymbols) > 0: # warmup our indicators by pushing history through the consolidators history = algorithm.History(addedSymbols, self.period, self.resolution) if history.empty: return tickers = history.index.levels[0] for ticker in tickers: symbol = SymbolCache.GetSymbol(ticker) symbolData = self.symbolData[symbol] for tuple in history.loc[ticker].itertuples(): bar = TradeBar(tuple.Index, symbol, tuple.open, tuple.high, tuple.low, tuple.close, tuple.volume) symbolData.Consolidator.Update(bar) 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.''' deviations = self.deviations * data.STD.Current.Value if unorderedQuantity > 0: if data.Security.BidPrice < data.SMA.Current.Value - deviations: return True else: if data.Security.AskPrice > data.SMA.Current.Value + deviations: return True return False 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): self.Security = security self.Consolidator = algorithm.ResolveConsolidator(security.Symbol, resolution) smaName = algorithm.CreateIndicatorName(security.Symbol, "SMA{}".format(period), resolution) self.SMA = SimpleMovingAverage(smaName, period) algorithm.RegisterIndicator(security.Symbol, self.SMA, self.Consolidator) stdName = algorithm.CreateIndicatorName(security.Symbol, "STD{}".format(period), resolution) self.STD = StandardDeviation(stdName, period) algorithm.RegisterIndicator(security.Symbol, self.STD, self.Consolidator)