| Overall Statistics |
|
Total Trades 404 Average Win 0.90% Average Loss -0.56% Compounding Annual Return 3.598% Drawdown 15.900% Expectancy 0.170 Net Profit 19.343% Sharpe Ratio 0.458 Probabilistic Sharpe Ratio 9.013% Loss Rate 55% Win Rate 45% Profit-Loss Ratio 1.60 Alpha 0.033 Beta -0.01 Annual Standard Deviation 0.07 Annual Variance 0.005 Information Ratio -0.317 Tracking Error 0.139 Treynor Ratio -3.221 Total Fees $1736.66 |
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(22), InsightDirection.Up), Insight.Price(ordered[1]['symbol'], timedelta(22), InsightDirection.Flat) ])
class FrameworkAlgorithm(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2014, 1, 1)
self.SetEndDate(2019, 1, 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(rebalancingParam = timedelta(22)))
self.SetRiskManagement(MaximumDrawdownPercentPerSecurity(0.02))
#1. Set the Execution Model to an Immediate Execution Model
self.SetExecution(ImmediateExecutionModel())