| Overall Statistics |
|
Total Trades 324 Average Win 0.52% Average Loss -0.45% Compounding Annual Return 24.213% Drawdown 20.600% Expectancy 0.265 Net Profit 91.762% Sharpe Ratio 1.199 Probabilistic Sharpe Ratio 56.461% Loss Rate 41% Win Rate 59% Profit-Loss Ratio 1.16 Alpha 0.227 Beta -0.083 Annual Standard Deviation 0.179 Annual Variance 0.032 Information Ratio 0.297 Tracking Error 0.241 Treynor Ratio -2.587 Total Fees $459.33 |
from Selection.FundamentalUniverseSelectionModel import FundamentalUniverseSelectionModel
class NadionUncoupledPrism(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2011, 1, 1)
self.SetEndDate(2014, 1, 1)
self.SetCash(100000)
self.AddEquity("SPY", Resolution.Daily)
self.AddEquity("TLT", Resolution.Daily)
self.AddEquity("QQQ", Resolution.Daily)
self.symbols = [Symbol.Create("SPY", SecurityType.Equity, Market.USA), \
Symbol.Create("TLT", SecurityType.Equity, Market.USA), Symbol.Create("QQQ", SecurityType.Equity, Market.USA)]
self.averages = {}
self.AddUniverseSelection(TechnologyUniverseModule())
self.AddRiskManagement(NullRiskManagementModel())
self.SetPortfolioConstruction(NullPortfolioConstructionModel())
self.SetExecution(ImmediateExecutionModel())
self.UniverseSettings.Resolution = Resolution.Daily
self.SetWarmUp(200)
def OnData(self, data):
if self.IsWarmingUp:
return
for security in self.changes.RemovedSecurities:
if security.Invested:
self.Liquidate(security.Symbol)
for security in self.changes.AddedSecurities:
if not security.Invested and security.Symbol not in self.symbols:
self.SetHoldings(security.Symbol, 0.05)
else:
return
def OnSecuritiesChanged(self, changes):
self.changes = changes
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):
if algorithm.Time.month == self.lastMonth:
return Universe.Unchanged
coarse = sorted([x for x in coarse if x.HasFundamentalData and x.Price > 10],
key = lambda x: x.DollarVolume, reverse=True)[:self.numberOfSymbolsCoarse]
for security in coarse:
symbol = security.Symbol
self.dollarVolumeBySymbol[symbol] = security.DollarVolume
#self.dollarVolumeBySymbol = {x.Symbol:x.DollarVolume for x in coarse}
# 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" \
and (algorithm.Time - x.SecurityReference.IPODate).days > 180], \
key = lambda x: self.dollarVolumeBySymbol[x.Symbol], reverse=True)
#Industry Template Codes: N=Normal (Manufacturing), M=Mining, U=Utility, T=Transportation, B=Bank, I=Insurance
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)from Selection.FundamentalUniverseSelectionModel import FundamentalUniverseSelectionModel
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-day close price
-The stock must have positive volume on the previous trading day
'''
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):
'''
Performs a fine selection:
-The company's headquarter must in the U.S.
-The stock must be traded on the NASDAQ stock exchange
-The stock must be in the Industry Template Code catagory N
-At least half a year since its initial public offering
'''
# Filter stocks and sort on dollar volume
sortedByDollarVolume = sorted([x for x in fine if x.CompanyReference.CountryId == "USA"
and x.CompanyReference.PrimaryExchangeID == "NAS"
and x.CompanyReference.IndustryTemplateCode == "N"
and (algorithm.Time - x.SecurityReference.IPODate).days > 180],
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[:self.numberOfSymbolsFine]]