| 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)