| Overall Statistics |
|
Total Trades 902 Average Win 0.04% Average Loss -0.01% Compounding Annual Return 9.812% Drawdown 3.700% Expectancy 0.713 Net Profit 1.577% Sharpe Ratio 0.731 Probabilistic Sharpe Ratio 44.323% Loss Rate 72% Win Rate 28% Profit-Loss Ratio 5.09 Alpha 0.109 Beta -0.412 Annual Standard Deviation 0.081 Annual Variance 0.006 Information Ratio -0.5 Tracking Error 0.123 Treynor Ratio -0.143 Total Fees $934.31 Estimated Strategy Capacity $6000000.00 Lowest Capacity Asset TTD WE3561IA1KKL |
from datetime import timedelta
from QuantConnect.Data.UniverseSelection import *
from Selection.FundamentalUniverseSelectionModel import FundamentalUniverseSelectionModel
class LiquidValueStocks(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2017, 5, 15)
self.SetEndDate(2017, 7, 15)
self.SetCash(100000)
self.UniverseSettings.Resolution = Resolution.Hour
self.AddUniverseSelection(LiquidValueUniverseSelectionModel())
#1. Create and instance of the LongShortEYAlphaModel
self.AddAlpha(LongShortEYAlphaModel())
self.SetPortfolioConstruction(EqualWeightingPortfolioConstructionModel())
self.SetExecution(ImmediateExecutionModel())
class LiquidValueUniverseSelectionModel(FundamentalUniverseSelectionModel):
def __init__(self):
super().__init__(True, 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)
universe = sortedByYields[:10] + sortedByYields[-10:]
return [f.Symbol for f in universe]
# Define the LongShortAlphaModel class
class LongShortEYAlphaModel(AlphaModel):
def __init__(self):
self.lastMonth = -1
def Update(self, algorithm, data):
insights = []
#2. If else statement to emit signals once a month
if self.lastMonth == algorithm.Time.month:
return insights
self.lastMonth = algorithm.Time.month
#3. For loop to emit insights with insight directions
# based on whether earnings yield is greater or less than zero once a month
for security in algorithm.ActiveSecurities.Values:
direction = 1 if security.Fundamentals.ValuationRatios.EarningYield > 0 else -1
insights.append(Insight.Price(security.Symbol, timedelta(28), direction))
return insights