| 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 0 Tracking Error 0 Treynor Ratio 0 Total Fees $0.00 |
class SpyTrendAlphaModel(AlphaModel):
def __init__(self):
pass
def OnSecuritiesChanged(self, algorithm, changes):
self.symbols = [x.Symbol for x in changes.AddedSecurities]
def Update(self, algorithm, data):
for x in self.symbols:
history = algorithm.History(x, 7, Resolution.Daily)
price = history["close"]
TF_3 = price.pct_change(3)[-1]
if TF_3 > 0:
return Insight.Price(price.index[0][0], timedelta(1), InsightDirection.Up)
else :
return Insight.Price(price.index[0][0], timedelta(1), InsightDirection.Down)# Inspired by the theory here:
# https://seekingalpha.com/article/4299701-leveraged-etfs-for-long-term-investing
import pandas as pd
from SpyTrendAlphaModel import SpyTrendAlphaModel
class MultidimensionalTransdimensionalPrism(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2020, 2, 1) # Earliest start date for all ETFs in universe 2/1/10
self.SetEndDate(2020, 5, 6)
self.SetCash(10000)
symbols = [ Symbol.Create("UST", SecurityType.Equity, Market.USA), Symbol.Create("TQQQ", SecurityType.Equity, Market.USA), Symbol.Create("UBT", SecurityType.Equity, Market.USA)]
self.SetUniverseSelection(ManualUniverseSelectionModel(symbols))
self.SetAlpha(SpyTrendAlphaModel())
self.SetRiskManagement(CompositeRiskManagementModel(
MaximumUnrealizedProfitPercentPerSecurity(0.2),
MaximumDrawdownPercentPerSecurity(0.3)
))
self.SetPortfolioConstruction(EqualWeightingPortfolioConstructionModel())
self.SetExecution(NullExecutionModel())