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@Martin-Molinero @AlexCatarino @StefanoRaggi
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# 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.
from clr import AddReference
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
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.
algorithm: The algorithm instance
targets: The portfolio 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)
def OnSecuritiesChanged(self, algorithm, changes):
'''Event fired each time the we add/remove securities from the data feed
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
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)
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