| 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)