| Overall Statistics |
|
Total Orders 1579 Average Win 0.31% Average Loss -0.30% Compounding Annual Return 15.910% Drawdown 14.100% Expectancy 0.129 Start Equity 1000000 End Equity 1359330.74 Net Profit 35.933% Sharpe Ratio 0.605 Sortino Ratio 0.473 Probabilistic Sharpe Ratio 41.420% Loss Rate 44% Win Rate 56% Profit-Loss Ratio 1.03 Alpha 0.052 Beta 0.467 Annual Standard Deviation 0.126 Annual Variance 0.016 Information Ratio 0.18 Tracking Error 0.132 Treynor Ratio 0.163 Total Fees $5068.74 Estimated Strategy Capacity $25000000.00 Lowest Capacity Asset MTUM VFUDGZIY8ZMT Portfolio Turnover 12.23% |
#region imports
from AlgorithmImports import *
#endregion
from clr import AddReference
AddReference("System")
AddReference("QuantConnect.Algorithm")
AddReference("QuantConnect.Algorithm.Framework")
AddReference("QuantConnect.Common")
from System import *
from QuantConnect import *
from QuantConnect.Orders import *
from QuantConnect.Algorithm import *
from QuantConnect.Algorithm.Framework import *
from QuantConnect.Algorithm.Framework.Selection import *
from Alphas.HistoricalReturnsAlphaModel import HistoricalReturnsAlphaModel
from QuantConnect.Algorithm.Framework.Execution import *
from QuantConnect.Algorithm.Framework.Risk import *
#from Portfolio.BlackLittermanOptimizationPortfolioConstructionModel import *
#from Portfolio.UnconstrainedMeanVariancePortfolioOptimizer import UnconstrainedMeanVariancePortfolioOptimizer
#from Risk.NullRiskManagementModel import NullRiskManagementModel
#from Alphas.EmaCrossAlphaModel import EmaCrossAlphaModel
#from Alphas.HistoricalReturnsAlphaModel import HistoricalReturnsAlphaModel
#from Alphas.MacdAlphaModel import MacdAlphaModel
#from Alphas.RsiAlphaModel import RsiAlphaModel
#from Alphas.ConstantAlphaModel import ConstantAlphaModel
from collections import deque
from AlgorithmImports import *
class EnergeticSkyBlueFrog(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2022, 3, 1)
self.SetEndDate(2024,3,31)
self._cash= 1000000
self.SetCash(self._cash)
#self.SetWarmUp(5)
self.UniverseSettings.Resolution = Resolution.Daily
symbols = [ # OUR INVESTMENT UNIVERSE
# FACTORS
Symbol.Create("USMV", SecurityType.Equity, Market.USA),
Symbol.Create("DGRO", SecurityType.Equity, Market.USA),
Symbol.Create("QUAL", SecurityType.Equity, Market.USA),
Symbol.Create("DVY", SecurityType.Equity, Market.USA),
Symbol.Create("MTUM", SecurityType.Equity, Market.USA),
Symbol.Create("VLUE", SecurityType.Equity, Market.USA),
Symbol.Create("TLT", SecurityType.Equity, Market.USA)]
self.SetUniverseSelection(ManualUniverseSelectionModel(symbols))
self.AddAlpha(HistoricalReturnsAlphaModel(2, resolution = Resolution.Daily))
self.AddAlpha(EmaCrossAlphaModel(fastPeriod=5, slowPeriod=10, resolution = Resolution.Daily ))
self.AddAlpha(ConstantAlphaModel(InsightType.Price, InsightDirection.Up, timedelta(days = 1), 0.025, None))
optimizer = UnconstrainedMeanVariancePortfolioOptimizer()
self.SetPortfolioConstruction(BlackLittermanOptimizationPortfolioConstructionModel(optimizer = optimizer, tau=0.10))
self.SetExecution(ImmediateExecutionModel())
#self.AddRiskManagement(MaximumDrawdownPercentPortfolio(0.05, isTrailing=False))
self.riskManagement = TrailingPeriodDrawdownRiskManagementModel(0.03, 10) # 10 trading days as trailing period
self.AddRiskManagement(self.riskManagement)
self._benchmark = self.AddEquity("SPY", Resolution.Daily).Symbol
self._benchmarkInitial = self.History(self._benchmark, 1, Resolution.Daily)
self._benchmarkPrice = self._benchmarkInitial['close'][0]
def OnData(self,data):
self.Plot("Relative Performance", "SPY", self._cash*self.Securities["SPY"].Close/self._benchmarkPrice)
self.Plot("Relative Performance", "Portfolio Value", self.Portfolio.TotalPortfolioValue)
class TrailingPeriodDrawdownRiskManagementModel(RiskManagementModel):
def __init__(self, maxDrawdownPercent, trailingPeriod):
self.maxDrawdownPercent = maxDrawdownPercent
self.trailingPeriod = trailingPeriod
self.portfolioHighs = deque(maxlen=trailingPeriod)
def ManageRisk(self, algorithm, targets):
currentPortfolioValue = algorithm.Portfolio.TotalPortfolioValue
self.portfolioHighs.append(currentPortfolioValue)
trailingMax = max(self.portfolioHighs)
if currentPortfolioValue < trailingMax * (1 - self.maxDrawdownPercent):
# Liquidate all positions if drawdown exceeds the limit
return [PortfolioTarget(symbol, 0) for symbol in algorithm.Portfolio.Keys]
return []