| Overall Statistics |
|
Total Trades 63 Average Win 0.69% Average Loss -0.41% Compounding Annual Return 12.989% Drawdown 15.300% Expectancy 0.306 Net Profit 8.650% Sharpe Ratio 0.828 Probabilistic Sharpe Ratio 42.381% Loss Rate 52% Win Rate 48% Profit-Loss Ratio 1.71 Alpha 0.115 Beta 0.008 Annual Standard Deviation 0.14 Annual Variance 0.02 Information Ratio -0.157 Tracking Error 0.383 Treynor Ratio 14.44 Total Fees $90.95 |
from System import *
from clr import AddReference
AddReference("QuantConnect.Algorithm")
from QuantConnect 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 *
from Selection.FundamentalUniverseSelectionModel import FundamentalUniverseSelectionModel
from QuantConnect.Algorithm.Framework.Portfolio import PortfolioConstructionModel
class NadionUncoupledPrism(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2020, 1, 1)
self.SetEndDate(2020, 12, 1)
self.SetCash(100000)
self.AddEquity("SPY", Resolution.Daily)
self.AddEquity("BND", Resolution.Daily)
self.SetBenchmark("SPY")
#self.SetAlpha()
self.AddUniverseSelection(TechnologyUniverseModule())
self.AddRiskManagement(NullRiskManagementModel())
self.SetPortfolioConstruction(NullPortfolioConstructionModel())
self.SetExecution(ImmediateExecutionModel())
self.UniverseSettings.Resolution = Resolution.Daily
self.ema = self.EMA("SPY", 100, Resolution.Daily)
self.ema_BND = self.EMA("BND", 100, Resolution.Daily)
self.SetWarmUp(100)
def OnSecuritiesChanged(self, changes):
self.changes = changes
self.BND = self.Securities["SPY"].Close < self.ema.Current.Value and \
self.Securities["BND"].Close > self.ema_BND.Current.Value
self.SPY = self.Securities["SPY"].Close > self.ema.Current.Value
if not self.Portfolio.Invested:
if self.SPY == True:
for security in self.changes.RemovedSecurities:
if security.Invested:
self.Liquidate(security.Symbol)
for security in self.changes.AddedSecurities:
if not security.Invested:
self.SetHoldings(security.Symbol, .05)
elif self.BND == True:
self.SetHoldings([PortfolioTarget("BND", 1.00)])
elif self.Portfolio.Invested:
if self.Portfolio.Invested and not self.Portfolio["BND"].Invested:
if self.BND == True:
self.Liquidate()
self.SetHoldings([PortfolioTarget("BND", 1.00)])
elif self.Portfolio["BND"].Invested:
if self.SPY == True:
self.Liquidate("BND")
for security in self.changes.RemovedSecurities:
if security.Invested:
self.Liquidate(security.Symbol)
for security in self.changes.AddedSecurities:
if not security.Invested:
self.SetHoldings(security.Symbol, .05)
if self.BND == True:
self.Liquidate()
self.SetHoldings([PortfolioTarget("BND", 1.00)])
else:
return
class TechnologyUniverseModule(FundamentalUniverseSelectionModel):
#This module selects the most liquid stocks listed on the Nasdaq Stock Exchange.
def __init__(self, filterFineData = True, universeSettings = None, securityInitializer = None):
#Initializes a new default instance of the TechnologyUniverseModule
super().__init__(filterFineData, universeSettings, securityInitializer)
self.numberOfSymbolsCoarse = 1000
self.numberOfSymbolsFine = 100
self.dollarVolumeBySymbol = {}
self.lastMonth = -1
def SelectCoarse(self, algorithm, coarse):
'''
Performs a coarse selection:
-The stock must have fundamental data
-The stock must have positive previous-month close price
-The stock must have positive volume on the previous trading month
'''
if algorithm.Time.month == self.lastMonth:
return Universe.Unchanged
sortedByDollarVolume = sorted([x for x in coarse if x.HasFundamentalData and x.Volume > 0 and x.Price > 0],
key = lambda x: x.DollarVolume, reverse=True)[:self.numberOfSymbolsCoarse]
self.dollarVolumeBySymbol = {x.Symbol:x.DollarVolume for x in sortedByDollarVolume}
# If no security has met the QC500 criteria, the universe is unchanged.
if len(self.dollarVolumeBySymbol) == 0:
return Universe.Unchanged
return list(self.dollarVolumeBySymbol.keys())
def SelectFine(self, algorithm, fine):
sortedByDollarVolume = sorted([x for x in fine if x.CompanyReference.CountryId == "USA"
and x.CompanyReference.PrimaryExchangeID == "NAS"
and x.CompanyReference.IndustryTemplateCode == "N"],
key = lambda x: self.dollarVolumeBySymbol[x.Symbol], reverse=True)
if len(sortedByDollarVolume) == 0:
return Universe.Unchanged
self.lastMonth = algorithm.Time.month
return [x.Symbol for x in sortedByDollarVolume[:20]]
class NullPortfolioConstructionModel(PortfolioConstructionModel):
def CreateTargets(self, algorithm, insights):
return []
class ImmediateExecutionModel(ExecutionModel):
def __init__(self):
self.targetsCollection = PortfolioTargetCollection()
def Execute(self, algorithm, targets):
# for performance we check count value, OrderByMarginImpact and ClearFulfilled are expensive to call
self.targetsCollection.AddRange(targets)
if self.targetsCollection.Count > 0:
for target in self.targetsCollection.OrderByMarginImpact(algorithm):
# calculate remaining quantity to be ordered
quantity = OrderSizing.GetUnorderedQuantity(algorithm, target)
if quantity != 0:
algorithm.MarketOrder(target.Symbol, quantity)
self.targetsCollection.ClearFulfilled(algorithm)