| Overall Statistics |
|
Total Trades 102 Average Win 0.94% Average Loss -0.52% Compounding Annual Return 61.225% Drawdown 10.400% Expectancy 1.069 Net Profit 61.225% Sharpe Ratio 2.777 Probabilistic Sharpe Ratio 91.872% Loss Rate 26% Win Rate 74% Profit-Loss Ratio 1.80 Alpha 0.55 Beta -0.186 Annual Standard Deviation 0.181 Annual Variance 0.033 Information Ratio 1.099 Tracking Error 0.224 Treynor Ratio -2.698 Total Fees $133.38 |
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(2020,1 ,1)
self.SetCash(100000)
self.AddUniverseSelection(LiquidValueUniverseSelectionModel())
self.UniverseSettings.Resolution = Resolution.Daily
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)
class LiquidValueUniverseSelectionModel(FundamentalUniverseSelectionModel):
def __init__(self):
super().__init__(True, None, None)
self.lastMonth = -1
def SelectCoarse(self,algorithm, coarse):
if self.lastMonth == algorithm.Time.month:
return Universe.Unchanged
self.lastMonth = algorithm.Time.month
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)
filteredByYields = [f for f in fine if f.ValuationRatios.EarningYield > 0]
universe = filteredByYields[:10]
return [f.Symbol for f in universe]