| Overall Statistics |
|
Total Trades 326 Average Win 0.40% Average Loss -0.51% Compounding Annual Return -49.492% Drawdown 45.500% Expectancy -0.097 Net Profit -15.809% Sharpe Ratio -0.268 Probabilistic Sharpe Ratio 23.572% Loss Rate 50% Win Rate 50% Profit-Loss Ratio 0.80 Alpha -0.449 Beta -0.458 Annual Standard Deviation 0.795 Annual Variance 0.631 Information Ratio 0.285 Tracking Error 1.066 Treynor Ratio 0.465 Total Fees $327.48 |
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.AddEquity("QQQ", Resolution.Daily)
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.SetWarmUp(100)
self.symbols = [Symbol.Create("QQQ", SecurityType.Equity, Market.USA), \
Symbol.Create("SPY", SecurityType.Equity, Market.USA)]
def OnData(self, data):
#self.SPY = self.Securities["SPY"].Close > self.sma.Current.Value
if self.IsWarmingUp:
return
if not self.Portfolio.Invested:
#if self.BND == True:
for security in self.ActiveSecurities.Values:
if security.Symbol not in self.symbols:
self.SetHoldings(security.Symbol, 0.20)
elif self.Portfolio.Invested:
for security in self.changes.RemovedSecurities:
if security.Invested:
self.Liquidate(security.Symbol)
for security in self.ActiveSecurities.Values:
if security.Symbol not in self.symbols:
self.SetHoldings(security.Symbol, 0.20)
def OnSecuritiesChanged(self, changes):
self.changes = changes
self.Log(f"OnSecuritiesChanged({self.UtcTime}):: {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):
'''
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]]