Overall Statistics |

Total Trades 1 Average Win 0% Average Loss 0% Compounding Annual Return 10.880% Drawdown 18.200% Expectancy 0 Net Profit 70.372% Sharpe Ratio 0.9 Probabilistic Sharpe Ratio 41.185% Loss Rate 0% Win Rate 0% Profit-Loss Ratio 0 Alpha 0 Beta 0.925 Annual Standard Deviation 0.123 Annual Variance 0.015 Information Ratio -0.927 Tracking Error 0.01 Treynor Ratio 0.12 Total Fees $1.00 |

from Execution.ImmediateExecutionModel import ImmediateExecutionModel from Portfolio.EqualWeightingPortfolioConstructionModel import EqualWeightingPortfolioConstructionModel class MultidimensionalTachyonReplicator(QCAlgorithm): def Initialize(self): # Set Start Date so that backtest has 5+ years of data self.SetStartDate(2014, 11, 1) # No need to set End Date as the final submission will be tested # up until the review date # Set $1m Strategy Cash to trade significant AUM self.SetCash(1000) # Add a relevant benchmark, with the default being SPY self.stock = self.AddEquity('SPY', Resolution.Daily).Symbol self.SetBenchmark('SPY') ''' # Use the Alpha Streams Brokerage Model, developed in conjunction with # funds to model their actual fees, costs, etc. # Please do not add any additional reality modelling, such as Slippage, Fees, Buying Power, etc. self.SetBrokerageModel(AlphaStreamsBrokerageModel()) self.SetExecution(ImmediateExecutionModel()) self.SetPortfolioConstruction(EqualWeightingPortfolioConstructionModel()) self.UniverseSettings.Resolution = Resolution.Minute self.SetUniverseSelection(LiquidETFUniverse()) ''' def OnData(self, data): '''OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here. Arguments: data: Slice object keyed by symbol containing the stock data ''' self.SetHoldings(self.stock, 1)