| Overall Statistics |
|
Total Trades 36 Average Win 2.02% Average Loss -0.58% Compounding Annual Return -34.755% Drawdown 29.000% Expectancy 2.001 Net Profit -10.200% Sharpe Ratio -0.529 Probabilistic Sharpe Ratio 18.894% Loss Rate 33% Win Rate 67% Profit-Loss Ratio 3.50 Alpha -0.28 Beta -0.1 Annual Standard Deviation 0.432 Annual Variance 0.186 Information Ratio 0.407 Tracking Error 0.709 Treynor Ratio 2.277 Total Fees $36.00 |
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.Execution import ExecutionModel
from QuantConnect.Algorithm.Framework.Portfolio import *
from QuantConnect.Algorithm.Framework.Portfolio import PortfolioConstructionModel
from Selection.FundamentalUniverseSelectionModel import FundamentalUniverseSelectionModel
class Algorithm(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2020, 1, 1)
self.SetEndDate(2020, 4, 1)
self.SetCash(100000)
self.AddUniverseSelection(TechnologyUniverseModule())
self.UniverseSettings.Resolution = Resolution.Daily
self.AddEquity("SPY", Resolution.Daily)
self.AddEquity("QQQ", Resolution.Daily)
self.sma = self.SMA("SPY", 200, Resolution.Daily)
self.SetBrokerageModel(BrokerageName.InteractiveBrokersBrokerage)
self.changes = None
self.removedSecurities = None
self.SetWarmUp(100)
self.symbols = [Symbol.Create("QQQ", SecurityType.Equity, Market.USA), \
Symbol.Create("SPY", SecurityType.Equity, Market.USA)]
def OnData(self, data):
if self.IsWarmingUp:
return
numActiveSecurities = len(self.ActiveSecurities)
if self.changes and numActiveSecurities > 0:
for security in self.ActiveSecurities.Values:
if not security.Invested and not security in self.removedSecurities:
self.Log("Setting Holdings " + str(security.Symbol))
self.SetHoldings(security.Symbol, 1/numActiveSecurities, True)
self.changes = None
# Plot the number of active securities
self.Plot("Active", "Securities", numActiveSecurities)
def OnSecuritiesChanged(self, changes):
self.changes = changes
self.removedSecurities = changes.RemovedSecurities
self.Log(f"OnSecuritiesChanged({self.UtcTime}) :: {changes}")
for security in changes.RemovedSecurities:
self.Liquidate(security.Symbol, 'Removed from Universe')
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):
'''
Coarse Filters:
-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 > 1], \
key = lambda x: x.DollarVolume, reverse=True)
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" \
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[:5]]