Overall Statistics
Total Trades
1
Average Win
0%
Average Loss
0%
Compounding Annual Return
10.819%
Drawdown
19.500%
Expectancy
0
Net Profit
67.325%
Sharpe Ratio
0.754
Probabilistic Sharpe Ratio
27.420%
Loss Rate
0%
Win Rate
0%
Profit-Loss Ratio
0
Alpha
0.095
Beta
-0.028
Annual Standard Deviation
0.122
Annual Variance
0.015
Information Ratio
-0.002
Tracking Error
0.175
Treynor Ratio
-3.309
Total Fees
$27.29
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(1000000)

        # Add a relevant benchmark, with the default being SPY
        self.AddEquity('SPY')
        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
        '''

        if not self.Portfolio.Invested:
          self.SetHoldings("SPY", 1)