| Overall Statistics |
|
Total Trades 69 Average Win 0.61% Average Loss -0.24% Compounding Annual Return 9.110% Drawdown 7.600% Expectancy 1.323 Net Profit 12.335% Sharpe Ratio 0.916 Probabilistic Sharpe Ratio 44.255% Loss Rate 34% Win Rate 66% Profit-Loss Ratio 2.54 Alpha 0.077 Beta 0.01 Annual Standard Deviation 0.085 Annual Variance 0.007 Information Ratio -0.201 Tracking Error 0.264 Treynor Ratio 7.894 Total Fees $71.73 Estimated Strategy Capacity $230000000.00 |
from datetime import timedelta
from QuantConnect.Data.UniverseSelection import *
from Selection.FundamentalUniverseSelectionModel import FundamentalUniverseSelectionModel
class UglyAsparagusSheep(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2019, 1, 1)
self.SetEndDate(2020, 5, 1)
self.SetCash(100000)
self.SetWarmUp(200)
self.UniverseSettings.Resolution = Resolution.Daily
self.UniverseSettings.Leverage = 1 #self.GetParameter('Leverage')
#self.AddUniverseSelection(LiquidValueUniverseSelectionModel())
self.AddAlpha(LongShortEYAlphaModel())
self.SetPortfolioConstruction(EqualWeightingPortfolioConstructionModel())
self.SetExecution(ImmediateExecutionModel())
self.AddEquity("IWM", Resolution.Daily)
self.AddEquity("SPY", Resolution.Daily)
class LongShortEYAlphaModel(AlphaModel):
def __init__(self):
self.lastChange = None
self.warmedUp = False
self.bearMarket = False
self.hedgeSignal = False
self.ema_slow = None
self.ema_fast = None
self.ema_faster = None
self.symbols = []
def Update(self, algorithm, data):
insights = []
if self.ema_slow is None or not self.ema_slow.IsReady:
return insights
for symbol in self.symbols:
if not (data.ContainsKey(symbol) and data[symbol] is not None):
return []
#2. If else statement to emit signals once a day
if self.lastChange == algorithm.Time.day:
return insights
self.lastChange = algorithm.Time.day
self.bearMarket = data["IWM"].Close < self.ema_slow.Current.Value
self.hedgeSignal = self.bearMarket and self.ema_faster.Current.Value < self.ema_fast.Current.Value
# Not using the LiquidValueUniverseSelectionModel output for testing purpose
insights.append(Insight.Price("SPY", timedelta(days=2), InsightDirection.Up))
if self.hedgeSignal:
insights.append(Insight.Price("IWM", timedelta(days=2), InsightDirection.Down))
return insights
def OnSecuritiesChanged(self, algorithm, changes):
for security in changes.AddedSecurities:
if str(security.Symbol) == 'IWM':
self.ema_slow = algorithm.EMA("IWM", 200, Resolution.Daily)
self.ema_fast = algorithm.EMA("IWM", 10, Resolution.Daily)
self.ema_faster = algorithm.EMA("IWM", 5, Resolution.Daily)
self.symbols.append(security.Symbol)