Overall Statistics |
Total Trades 3 Average Win 0% Average Loss 0% Compounding Annual Return 4.174% Drawdown 17.700% Expectancy 0 Net Profit 6.315% Sharpe Ratio 0.306 Probabilistic Sharpe Ratio 15.244% Loss Rate 0% Win Rate 0% Profit-Loss Ratio 0 Alpha 0 Beta 0 Annual Standard Deviation 0.124 Annual Variance 0.015 Information Ratio 0.306 Tracking Error 0.124 Treynor Ratio 0 Total Fees $4.33 Estimated Strategy Capacity $4600000.00 Lowest Capacity Asset BND TRO5ZARLX6JP |
# region imports from AlgorithmImports import * # endregion class CrawlingBlackDogfish(QCAlgorithm): def Initialize(self): self.SetStartDate(2020, 11, 12) # Set Start Date self.SetCash(100000) # Set Strategy Cash # Setup trading framework # Transaction and submit/execution rules will use IB models self.SetBrokerageModel(BrokerageName.InteractiveBrokersBrokerage, AccountType.Margin) # Configure all algorithm securities # Uncomment this line and run to replicate runtime error: # During the algorithm initialization, the following exception has occurred: # Attempted to divide by zero. in BuyingPowerModel.cs:line 110 self.SetSecurityInitializer(self.CustomSecurityInitializer) self.AddEquity("SPY", Resolution.Minute) self.AddEquity("BND", Resolution.Minute) self.AddEquity("AAPL", Resolution.Minute) def OnData(self, data: Slice): if not self.Portfolio.Invested: self.SetHoldings("SPY", 0.33) self.SetHoldings("BND", 0.33) self.SetHoldings("AAPL", 0.33) def CustomSecurityInitializer(self, security): """ Define models to be used for securities as they are added to the algorithm's universe. """ # Set the custom buying power model security.SetBuyingPowerModel(CustomBuyingPowerModel(self)) ################################################################################ class CustomBuyingPowerModel(BuyingPowerModel): """ Custom model to determine if the portfolio has sufficient buying power for a new desired order. """ def __init__(self, algorithm): """Initialize the custom buying power model.""" self.algorithm = algorithm def HasSufficientBuyingPowerForOrder(self, parameters): """Determine if the portfolio has the buying power for an order.""" # This model will assume that there is always enough buying power sufficient_buying_power = HasSufficientBuyingPowerForOrderResult(True) return sufficient_buying_power