| Overall Statistics |
|
Total Trades 0 Average Win 0% Average Loss 0% Compounding Annual Return 0% Drawdown 0% Expectancy 0 Net Profit 0% Sharpe Ratio 0 Probabilistic Sharpe Ratio 0% Loss Rate 0% Win Rate 0% Profit-Loss Ratio 0 Alpha 0 Beta 0 Annual Standard Deviation 0 Annual Variance 0 Information Ratio -5.809 Tracking Error 0.114 Treynor Ratio 0 Total Fees $0.00 |
class TestAlgo(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2018, 5, 28)
self.SetEndDate(2018, 6, 9)
self.SetWarmUp(10)
self.SetCash(10000)
# Universe selection settings
self.UniverseSettings.Resolution = Resolution.Daily
self.UniverseSettings.DataNormalizationMode = DataNormalizationMode.Adjusted
self.UniverseSettings.ExtendedMarketHours = False
self.AddUniverseSelection(
FineFundamentalUniverseSelectionModel(self.SelectCoarse, self.SelectFine)
)
# Other initialization code
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
'''
def SelectCoarse(self, coarse):
return Universe.Unchanged
def SelectFine(self, fine):
return self.symbolsfrom clr import AddReference
AddReference("System")
AddReference("QuantConnect.Common")
AddReference("QuantConnect.Algorithm.Framework")
from QuantConnect.Data.UniverseSelection import *
from QuantConnect.Algorithm.Framework import *
from QuantConnect.Algorithm.Framework.Selection import FineFundamentalUniverseSelectionModel
class MyUniverseModel(FineFundamentalUniverseSelectionModel):
def __init__(self):
super().__init__(coarseSelector=self.SelectCoarse, fineSelector=self.SelectFine)
def SelectCoarse(self, coarse):
tickers = ["AAPL", "AIG", "IBM"]
return [Symbol.Create(x, SecurityType.Equity, Market.USA) for x in tickers]
def SelectFine(self, fine):
return [f.Symbol for f in fine]