class VolumeWeightedAveragePriceExecutionModel:
'''Execution model that submits orders while the current market price is more favorable that the current volume weighted average price.'''
def __init__(self):
'''Initializes a new instance of the VolumeWeightedAveragePriceExecutionModel class'''
self.targetsCollection = PortfolioTargetCollection()
self.symbolData = {}
# Gets or sets the maximum order quantity as a percentage of the current bar's volume.
# This defaults to 0.01m = 1%. For example, if the current bar's volume is 100,
# then the maximum order size would equal 1 share.
self.MaximumOrderQuantityPercentVolume = 0.01
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'''
# update the complete set of portfolio targets with the new 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 VWAP indicator
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 self.PriceIsFavorable(data, unorderedQuantity):
# get the maximum order size based on total order value
maxOrderSize = OrderSizing.PercentVolume(
data.Security, self.MaximumOrderQuantityPercentVolume)
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 removed security data
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)
for added in changes.AddedSecurities:
if added.Symbol not in self.symbolData:
self.symbolData[added.Symbol] = SymbolData(algorithm, added)
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.'''
if unorderedQuantity > 0:
if data.Security.BidPrice < data.VWAP:
return True
else:
if data.Security.AskPrice > data.VWAP:
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 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 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
# 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
])