Overall Statistics |
Total Trades 200 Average Win 0.46% Average Loss -0.58% Compounding Annual Return 7.978% Drawdown 12.300% Expectancy 0.025 Net Profit 1.270% Sharpe Ratio 0.448 Probabilistic Sharpe Ratio 40.204% Loss Rate 43% Win Rate 57% Profit-Loss Ratio 0.78 Alpha 0.088 Beta 0.056 Annual Standard Deviation 0.2 Annual Variance 0.04 Information Ratio 0.228 Tracking Error 0.247 Treynor Ratio 1.601 Total Fees $2568.85 |
class CalibratedVentralThrustAssembly(QCAlgorithm): def Initialize(self): '''Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.''' self.SetStartDate(2010,1,1) #Set Start Date self.SetEndDate(2010,3,1) #Set End Date self.SetCash(100000) #Set Strategy Cash self.UniverseSettings.Resolution = Resolution.Daily self.UniverseSettings.Leverage = 2 self.coarse_count = 10 self.averages = { }; # this add universe method accepts two parameters: # - coarse selection function: accepts an IEnumerable<CoarseFundamental> and returns an IEnumerable<Symbol> self.AddUniverse(self.CoarseSelectionFunction) # sort the data by daily dollar volume and take the top 'NumberOfSymbols' def CoarseSelectionFunction(self, coarse): # We are going to use a dictionary to refer the object that will keep the moving averages for cf in coarse: if cf.Symbol not in self.averages: self.averages[cf.Symbol] = SymbolData(cf.Symbol) # Updates the SymbolData object with current EOD price avg = self.averages[cf.Symbol] avg.update(cf.EndTime, cf.Volume) # Filter the values of the dict: we only want up-trending securities values = list(filter(lambda x: x.is_uptrend, self.averages.values())) # Sorts the values of the dict: we want those with greater difference between the moving averages values.sort(key=lambda x: x.scale, reverse=True) for x in values[:self.coarse_count]: self.Log('symbol: ' + str(x.symbol.Value) + ' scale: ' + str(x.scale)) # we need to return only the symbol objects return [ x.symbol for x in values[:self.coarse_count] ] # this event fires whenever we have changes to our universe def OnSecuritiesChanged(self, changes): # liquidate removed securities for security in changes.RemovedSecurities: if security.Invested: self.Liquidate(security.Symbol) # we want 20% allocation in each security in our universe for security in changes.AddedSecurities: self.SetHoldings(security.Symbol, 0.1) self.Log(f"Symbols: {changes.AddedSecurities}") class SymbolData(object): def __init__(self, symbol): self.symbol = symbol self.tolerance = 1.01 self.fast = ExponentialMovingAverage(10) self.slow = ExponentialMovingAverage(15) self.is_uptrend = False self.scale = 0 def update(self, time, value): if self.fast.Update(time, value) and self.slow.Update(time, value): fast = self.fast.Current.Value slow = self.slow.Current.Value self.is_uptrend = fast > slow * self.tolerance if self.is_uptrend: self.scale = (fast - slow) / ((fast + slow) / 2.0)