Overall Statistics |
Total Trades 2785 Average Win 1.81% Average Loss -1.42% Compounding Annual Return 4.887% Drawdown 46.400% Expectancy 0.045 Net Profit 61.482% Sharpe Ratio 0.28 Loss Rate 54% Win Rate 46% Profit-Loss Ratio 1.27 Alpha 0.082 Beta -0.055 Annual Standard Deviation 0.268 Annual Variance 0.072 Information Ratio -0.156 Tracking Error 0.304 Treynor Ratio -1.353 Total Fees $122654.21 |
# 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.Common") from System import * from QuantConnect import * from QuantConnect.Algorithm import QCAlgorithm from QuantConnect.Data.UniverseSelection import * import base64 ### <summary> ### In this algortihm we show how you can easily use the universe selection feature to fetch symbols ### to be traded using the BaseData custom data system in combination with the AddUniverse{T} method. ### AddUniverse{T} requires a function that will return the symbols to be traded. ### </summary> ### <meta name="tag" content="using data" /> ### <meta name="tag" content="universes" /> ### <meta name="tag" content="custom universes" /> class DropboxUniverseSelectionAlgorithm(QCAlgorithm): def Initialize(self): self.SetStartDate(2009,7,2) self.SetEndDate(2019,7,15) self.SetCash(100000) self.SetBenchmark("SPY") self.backtestSymbolsPerDay = {} self.current_universe = [] self.UniverseSettings.Resolution = Resolution.Minute self.UniverseSettings.Leverage = 2 self.UniverseSettings.DataNormalizationMode = DataNormalizationMode.Raw self.AddUniverse("my-dropbox-universe", self.selector) def selector(self, date): # handle live mode file format if self.LiveMode: # fetch the file from dropbox str = self.Download("https://www.dropbox.com/s/9w8zdr7qs1s7tu7/lean_export.csv?dl=1") # if we have a file for today, return symbols, else leave universe unchanged self.current_universe = str.split(',') if len(str) > 0 else self.current_universe return self.current_universe # backtest - first cache the entire file if len(self.backtestSymbolsPerDay) == 0: # No need for headers for authorization with dropbox, these two lines are for example purposes byteKey = base64.b64encode("UserName:Password".encode('ASCII')) # The headers must be passed to the Download method as dictionary headers = { 'Authorization' : f'Basic ({byteKey.decode("ASCII")})' } str = self.Download("https://www.dropbox.com/s/9w8zdr7qs1s7tu7/lean_export.csv?dl=1", headers) for line in str.splitlines(): data = line.split(',') self.backtestSymbolsPerDay[data[0]] = data[1:] index = date.strftime("%Y%m%d") self.current_universe = self.backtestSymbolsPerDay.get(index, self.current_universe) return self.current_universe def OnData(self, slice): if self.changes is None: return for tradeBar in slice.Bars.Values: self.Log('{0}'.format(tradeBar.Symbol)) if self.Securities[tradeBar.Symbol].Invested == False: self.SetHoldings(tradeBar.Symbol, -2) invested = [x.Key for x in self.Portfolio if x.Value.Invested] for symbol in invested: security_holding = self.Portfolio[symbol] quantity = security_holding.Quantity #self.Log('{0}'.format(quantity)) self.MarketOnCloseOrder(symbol, -quantity) # reset changes self.changes = None def OnSecuritiesChanged(self, changes): self.changes = changes