| Overall Statistics |
|
Total Trades 14 Average Win 0.09% Average Loss -0.36% Compounding Annual Return 51.545% Drawdown 1.300% Expectancy -0.005 Net Profit 7.078% Sharpe Ratio 4.73 Probabilistic Sharpe Ratio 93.122% Loss Rate 20% Win Rate 80% Profit-Loss Ratio 0.24 Alpha 0.145 Beta 0.61 Annual Standard Deviation 0.073 Annual Variance 0.005 Information Ratio 0.278 Tracking Error 0.059 Treynor Ratio 0.569 Total Fees $66.04 Estimated Strategy Capacity $550000000.00 Lowest Capacity Asset SPY R735QTJ8XC9X |
from datetime import timedelta
class MOMAlphaModel(AlphaModel):
def __init__(self):
self.mom = []
def OnSecuritiesChanged(self, algorithm, changes):
for security in changes.AddedSecurities:
symbol = security.Symbol
self.mom.append({"symbol":symbol, "indicator":algorithm.MOM(symbol, 14, Resolution.Daily)})
def Update(self, algorithm, data):
ordered = sorted(self.mom, key=lambda kv: kv["indicator"].Current.Value, reverse=True)
return Insight.Group([Insight.Price(ordered[0]['symbol'], timedelta(1), InsightDirection.Up), Insight.Price(ordered[1]['symbol'], timedelta(1), InsightDirection.Flat) ])
class FrameworkAlgorithm(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2013, 10, 1)
self.SetEndDate(2013, 12, 1)
self.SetCash(100000)
symbols = [Symbol.Create("SPY", SecurityType.Equity, Market.USA), Symbol.Create("BND", SecurityType.Equity, Market.USA)]
self.UniverseSettings.Resolution = Resolution.Daily
self.SetUniverseSelection(ManualUniverseSelectionModel(symbols))
self.SetAlpha(MOMAlphaModel())
self.SetPortfolioConstruction(EqualWeightingPortfolioConstructionModel())
self.SetRiskManagement(MaximumDrawdownPercentPerSecurity(0.02))
#1. Set the Execution Model to an Immediate Execution Model
self.SetExecution(ImmediateExecutionModel())