Overall Statistics |
Total Trades 134 Average Win 5.04% Average Loss -2.63% Compounding Annual Return 1.910% Drawdown 43.300% Expectancy 0.065 Net Profit 5.844% Sharpe Ratio 0.215 Loss Rate 63% Win Rate 37% Profit-Loss Ratio 1.92 Alpha 0.433 Beta -18.377 Annual Standard Deviation 0.314 Annual Variance 0.099 Information Ratio 0.151 Tracking Error 0.314 Treynor Ratio -0.004 Total Fees $793.66 |
# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals. # Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from clr import AddReference AddReference("System") AddReference("QuantConnect.Algorithm") AddReference("QuantConnect.Indicators") AddReference("QuantConnect.Common") from System import * from QuantConnect import * from QuantConnect.Data import * from QuantConnect.Algorithm import * from QuantConnect.Indicators import * from System.Collections.Generic import List import decimal as d ### <summary> ### In this algorithm we demonstrate how to perform some technical analysis as ### part of your coarse fundamental universe selection ### </summary> ### <meta name="tag" content="using data" /> ### <meta name="tag" content="indicators" /> ### <meta name="tag" content="universes" /> ### <meta name="tag" content="coarse universes" /> class EmaCrossUniverseSelectionAlgorithm(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(2012,1,1) #Set Start Date self.SetEndDate(2015,1,1) #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.Price) # 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) class SymbolData(object): def __init__(self, symbol): self.symbol = symbol self.tolerance = d.Decimal(1.01) self.fast = ExponentialMovingAverage(100) self.slow = ExponentialMovingAverage(300) 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) / d.Decimal(2.0))