Overall Statistics |
Total Trades 3 Average Win 0% Average Loss 0% Compounding Annual Return -1.831% Drawdown 2.500% Expectancy 0 Net Profit -0.765% Sharpe Ratio -0.367 Loss Rate 0% Win Rate 0% Profit-Loss Ratio 0 Alpha 0.188 Beta -10.525 Annual Standard Deviation 0.046 Annual Variance 0.002 Information Ratio -0.785 Tracking Error 0.046 Treynor Ratio 0.002 Total Fees $3.00 |
### 180203 0304pm MultiSymbol03 ### 180203 MultipleSymbolConsolidationAlgorithm.pyFinalFromGitHub ### For Testing ### Attempting to add a RollingWindow for the SMA indicator, and then add the RollingWindow data ### to the self.Data dictionary. To allow access of previous period SMA data. ### Added lines ### 90 self.Data[bar.Symbol.Value].smaWin.Add(IndicatorDataPoint) ### 132 self.smaWin = RollingWindow[IndicatorDataPoint](windowSize) ### Received ### Runtime Error: Python.Runtime.PythonException: ArgumentException : Object of type 'System.RuntimeType' cannot be converted to type 'QuantConnect.Indicators.IndicatorDataPoint'. ### # 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 System import * from QuantConnect import * from QuantConnect.Data.Consolidators import * from QuantConnect.Data.Market import * from QuantConnect.Orders import OrderStatus from QuantConnect.Algorithm import QCAlgorithm from QuantConnect.Indicators import * import numpy as np from datetime import timedelta, datetime ### <summary> ### Example structure for structuring an algorithm with indicator and consolidator data for many tickers. ### </summary> ### <meta name="tag" content="consolidating data" /> ### <meta name="tag" content="indicators" /> ### <meta name="tag" content="using data" /> ### <meta name="tag" content="strategy example" /> class MultipleSymbolConsolidationAlgorithm(QCAlgorithm): # Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized. def Initialize(self): # This is the period of bars we'll be creating BarPeriod = TimeSpan.FromMinutes(10) # This is the period of our sma indicators SimpleMovingAveragePeriod = 10 # This is the number of consolidated bars we'll hold in symbol data for reference RollingWindowSize = 10 # Holds all of our data keyed by each symbol self.Data = {} # Contains all of our equity symbols EquitySymbols = ["AAPL","SPY","IBM"] # # Contains all of our forex symbols # ForexSymbols =["EURUSD", "USDJPY", "EURGBP", "EURCHF", "USDCAD", "USDCHF", "AUDUSD","NZDUSD"] self.SetStartDate(2014, 9, 1) self.SetEndDate(2015, 2, 1) # initialize our equity data for symbol in EquitySymbols: equity = self.AddEquity(symbol) self.Data[symbol] = SymbolData(equity.Symbol, BarPeriod, RollingWindowSize) # # initialize our forex data # for symbol in ForexSymbols: # forex = self.AddForex(symbol) # self.Data[symbol] = SymbolData(forex.Symbol, BarPeriod, RollingWindowSize) # loop through all our symbols and request data subscriptions and initialize indicator for symbol, symbolData in self.Data.iteritems(): # define the indicator symbolData.SMA = SimpleMovingAverage(self.CreateIndicatorName(symbol, "SMA" + str(SimpleMovingAveragePeriod), Resolution.Minute), SimpleMovingAveragePeriod) # define a consolidator to consolidate data for this symbol on the requested period consolidator = TradeBarConsolidator(BarPeriod) if symbolData.Symbol.SecurityType == SecurityType.Equity else QuoteBarConsolidator(BarPeriod) # write up our consolidator to update the indicator consolidator.DataConsolidated += self.OnDataConsolidated # we need to add this consolidator so it gets auto updates self.SubscriptionManager.AddConsolidator(symbolData.Symbol, consolidator) def OnDataConsolidated(self, sender, bar): # update the indicator self.Data[bar.Symbol.Value].SMA.Update(bar.Time, bar.Close) # we're also going to add this bar to our rolling window so we have access to it later self.Data[bar.Symbol.Value].Bars.Add(bar) # Add the indicator data point to the rolling window ### self.Data[bar.Symbol.Value].smaWin.Add(IndicatorDataPoint) #self.Debug("IndicatorDataPoint "+str(smaWin.Add(IndicatorDataPoint))) # OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here. # Argument "data": Slice object, dictionary object with your stock data def OnData(self,data): # loop through each symbol in our structure for symbol in self.Data.keys(): symbolData = self.Data[symbol] # this check proves that this symbol was JUST updated prior to this OnData function being called if symbolData.IsReady() and symbolData.WasJustUpdated(self.Time): if not self.Portfolio[symbol].Invested: #self.MarketOrder(symbol, 1) self.SetHoldings(symbol, 0.1) for z in range(0,8): # Loop through 8 points of data self.Debug("Data {0} SMA {1} ".format(self.Data[symbol].Bars[z], self.Data[symbol].Bars[z])) # End of a trading day event handler. This method is called at the end of the algorithm day (or multiple times if trading multiple assets). # Method is called 10 minutes before closing to allow user to close out position. def OnEndOfDay(self): i = 0 for symbol in sorted(self.Data.keys()): symbolData = self.Data[symbol] # we have too many symbols to plot them all, so plot every other i += 1 if symbolData.IsReady() and i%2 == 0: self.Plot(symbol, symbol, symbolData.SMA.Current.Value) class SymbolData(object): def __init__(self, symbol, barPeriod, windowSize): self.Symbol = symbol # The period used when population the Bars rolling window self.BarPeriod = barPeriod # A rolling window of data, data needs to be pumped into Bars by using Bars.Update( tradeBar ) and can be accessed like: # mySymbolData.Bars[0] - most first recent piece of data # mySymbolData.Bars[5] - the sixth most recent piece of data (zero based indexing) self.Bars = RollingWindow[IBaseDataBar](windowSize) # Rolling window for SMA data points ### self.smaWin = RollingWindow[IndicatorDataPoint](windowSize) # The simple moving average indicator for our symbol self.SMA = None # Returns true if all the data in this instance is ready (indicators, rolling windows, ect...) def IsReady(self): return self.Bars.IsReady and self.SMA.IsReady # Returns true if the most recent trade bar time matches the current time minus the bar's period, this # indicates that update was just called on this instance def WasJustUpdated(self, current): return self.Bars.Count > 0 and self.Bars[0].Time == current - self.BarPeriod