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)