| Overall Statistics |
|
Total Trades 19502 Average Win 0.02% Average Loss 0.00% Compounding Annual Return 27.022% Drawdown 27.900% Expectancy 2.970 Net Profit 250.761% Sharpe Ratio 1.221 Probabilistic Sharpe Ratio 59.099% Loss Rate 35% Win Rate 65% Profit-Loss Ratio 5.08 Alpha 0.286 Beta -0.179 Annual Standard Deviation 0.198 Annual Variance 0.039 Information Ratio -0.022 Tracking Error 0.304 Treynor Ratio -1.35 Total Fees $19755.05 Estimated Strategy Capacity $0 Lowest Capacity Asset LVNTA V8Z89IPL1MCL |
# Creating our own Index Fund
# https://www.quantconnect.com/forum/discussion/12347/creating-our-own-index-fund
# ----------------------
ETF = "QQQ"; LEV = 1.00;
# ----------------------
class IndexInvesting(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2016, 6, 24)
self.SetCash(1000000)
self.SetBenchmark(ETF)
self.UniverseSettings.Resolution = Resolution.Daily
self.etf = self.AddEquity(ETF, Resolution.Hour).Symbol
self.AddUniverse(self.Universe.ETF(self.etf, self.UniverseSettings, self.ETFConstituentsFilter))
self.weights = {}
self.Schedule.On(self.DateRules.WeekStart(self.etf), self.TimeRules.AfterMarketOpen(self.etf, 31),
self.Rebalance)
def ETFConstituentsFilter(self, constituents):
self.weights = {c.Symbol: c.Weight for c in constituents}
return list(self.weights.keys())
def OnSecuritiesChanged(self, changes):
for security in changes.RemovedSecurities:
if security.Invested:
self.Liquidate(security.Symbol, 'No longer in universe')
if security.Symbol in self.weights.keys(): del self.weights[security.Symbol]
def Rebalance(self):
for symbol, weight in self.weights.items():
if symbol in self.ActiveSecurities:
if weight is not None:
self.SetHoldings(symbol, weight) # Market cap weighted
# self.SetHoldings(symbol, LEV / len(self.weights)) # Equally weighted