| 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 -32.78 Tracking Error 0.051 Treynor Ratio 0 Total Fees $0.00 |
from datetime import timedelta
from QuantConnect.Data.UniverseSelection import *
from Selection.FundamentalUniverseSelectionModel import FundamentalUniverseSelectionModel
class B_Universe(FundamentalUniverseSelectionModel):
def __init__(self):
super().__init__(True, None, None)
# ovverride
def SelectCoarse(self, algorithm, coarse):
tickers = ["IBM"]
symbols = [Symbol.Create(x, SecurityType.Equity, Market.USA) for x in tickers]
return symbolsfrom QuantConnect.Data.UniverseSelection import *
from Selection.FundamentalUniverseSelectionModel import FundamentalUniverseSelectionModel
class ResistanceMultidimensionalGearbox(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2020, 8, 1) # Set Start Date
self.SetCash(100000) # Set Strategy Cash
tickers = ["AAPL", "AIG", "IBM"]
symbols = [Symbol.Create(x, SecurityType.Equity, Market.USA) for x in tickers]
self.AddUniverseSelection(QC500UniverseSelectionModel())
self.AddUniverseSelection(ManualUniverseSelectionModel(symbols))
self.AddUniverseSelection(A_Universe())
self.AddUniverseSelection(B_Universe())
self.AddAlpha(MyAlphaModel())
class MyAlphaModel:
def __init__(self):
self.flag = True
def Update(self, algorithm, slice):
if self.flag:
for kvp in algorithm.UniverseManager:
universe = kvp.Value
algorithm.Debug("universe symbol: {}".format(kvp.Key))
for kvp2 in universe.Members:
symbol = kvp2.Key
security = kvp2.Value
algorithm.Debug("security symbol: {}".format(symbol))
break
self.flag = False
insights = []
return insights
def OnSecuritiesChanged(self, algorithm, changes):
pass
class A_Universe(FundamentalUniverseSelectionModel):
def __init__(self):
super().__init__(False, None, None)
# ovverride
def SelectCoarse(self, algorithm, coarse):
tickers = ["AAPL"]
symbols = [Symbol.Create(x, SecurityType.Equity, Market.USA) for x in tickers]
return symbols
def SelectFine(self, algorithm, fine):
return [f.Symbol for f in fine]
class B_Universe(FundamentalUniverseSelectionModel):
def __init__(self):
super().__init__(False, None, None)
# ovverride
def SelectCoarse(self, algorithm, coarse):
tickers = ["IBM"]
symbols = [Symbol.Create(x, SecurityType.Equity, Market.USA) for x in tickers]
return symbols
def SelectFine(self, algorithm, fine):
return [f.Symbol for f in fine]from datetime import timedelta
from QuantConnect.Data.UniverseSelection import *
from Selection.FundamentalUniverseSelectionModel import FundamentalUniverseSelectionModel
class A_Universe(FundamentalUniverseSelectionModel):
def __init__(self):
super().__init__(True, None, None)
# ovverride
def SelectCoarse(self, algorithm, coarse):
tickers = ["AAPL"]
symbols = [Symbol.Create(x, SecurityType.Equity, Market.USA) for x in tickers]
return symbols