| Overall Statistics |
|
Total Trades 227 Average Win 0.20% Average Loss -0.12% Compounding Annual Return 449.861% Drawdown 2.200% Expectancy 1.040 Net Profit 16.118% Sharpe Ratio 13.028 Probabilistic Sharpe Ratio 96.033% Loss Rate 24% Win Rate 76% Profit-Loss Ratio 1.68 Alpha 3.599 Beta -0.421 Annual Standard Deviation 0.242 Annual Variance 0.058 Information Ratio 6.327 Tracking Error 0.328 Treynor Ratio -7.476 Total Fees $319.31 |
from datetime import timedelta
from QuantConnect.Data.UniverseSelection import *
from Selection.FundamentalUniverseSelectionModel import FundamentalUniverseSelectionModel
class NadionUncoupledPrism(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2019, 1, 1)
self.SetEndDate(2019,2 ,1)
self.SetCash(100000)
self.AddUniverseSelection(LiquidValueUniverseSelectionModel())
self.UniverseSettings.Resolution = Resolution.Daily
self.AddAlpha(ConstantAlphaModel(InsightType.Price, InsightDirection.Up, timedelta(days=1), None, None))
self.SetExecution(ImmediateExecutionModel())
self.SetPortfolioConstruction(EqualWeightingPortfolioConstructionModel())
class LiquidValueUniverseSelectionModel(FundamentalUniverseSelectionModel):
def __init__(self):
super().__init__(True, None, None)
def SelectCoarse(self,algorithm, coarse):
sortedByDollarVolume = sorted([x for x in coarse if x.HasFundamentalData], \
key=lambda x: x.DollarVolume, reverse=True)
return [x.Symbol for x in sortedByDollarVolume[:100]]
def SelectFine(self, algorithm, fine):
sortedByYields = sorted(fine, key=lambda f: f.ValuationRatios.EarningYield, reverse=True)
universe = sortedByYields[:10]
return [f.Symbol for f in universe]
#def OnSecuritiesChanged(self, changes):
# self.changes = changes
#
# for security in self.changes.RemovedSecurities:
# if security.Invested:
# self.Liquidate(security.Symbol)
#
# for security in self.changes.AddedSecurities:
# if not security.Invested:
# self.SetHoldings(security.Symbol, .10)