| Overall Statistics |
|
Total Trades 28 Average Win 0.47% Average Loss -0.51% Compounding Annual Return 37.414% Drawdown 2.700% Expectancy 0.438 Net Profit 5.547% Sharpe Ratio 2.586 Probabilistic Sharpe Ratio 72.601% Loss Rate 25% Win Rate 75% Profit-Loss Ratio 0.92 Alpha 0.28 Beta 0.096 Annual Standard Deviation 0.119 Annual Variance 0.014 Information Ratio 0.088 Tracking Error 0.145 Treynor Ratio 3.222 Total Fees $33.71 |
from Selection.FundamentalUniverseSelectionModel import FundamentalUniverseSelectionModel
class NadionUncoupledPrism(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2012, 8, 1)
self.SetEndDate(2012, 10, 1)
self.SetCash(100000)
self.symbols = [Symbol.Create("SPY", SecurityType.Equity, Market.USA) for t in ['SPY', 'QQQ', 'TLT'] ]
self.AddUniverseSelection(TechnologyUniverseModule())
self.UniverseSettings.Resolution = Resolution.Daily
self.SetWarmUp(200)
def OnData(self, data):
if self.IsWarmingUp:
return
self.Plot("Removed", "Len", len([s for s in self.changes.RemovedSecurities]))
for security in self.removed:
if security.Invested:
self.Liquidate(security.Symbol)
self.removed = []
for security in self.added:
if not security.Invested and security.Symbol not in self.symbols:
self.SetHoldings(security.Symbol, 0.05)
self.added = []
def OnSecuritiesChanged(self, changes):
self.Log("OnSecuritiesChanged")
self.added = changes.AddedSecurities
self.removed = changes.RemovedSecurities
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 = 20
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
# 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
#algorithm.Log()
return [x.Symbol for x in sortedByDollarVolume[:self.numberOfSymbolsFine]]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]]