Overall Statistics |

Total Trades 0 Average Win 0% Average Loss 0% Compounding Annual Return 0% Drawdown 0% Expectancy 0 Net Profit 0% Sharpe Ratio 0 Probabilistic Sharpe Ratio 0% Loss Rate 0% Win Rate 0% Profit-Loss Ratio 0 Alpha 0 Beta 0 Annual Standard Deviation 0 Annual Variance 0 Information Ratio -0.162 Tracking Error 0.14 Treynor Ratio 0 Total Fees $0.00 Estimated Strategy Capacity $0 |

# 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. 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 number of consolidated bars we'll hold in symbol data for reference RollingWindowSize = 50 # Holds all of our data keyed by each symbol self.Data = {} # Contains all of our equity symbols EquitySymbols = ["AAPL","SPY","IBM"] # Date bookends self.SetStartDate(2014, 12, 1) self.SetEndDate(2016, 1, 1) # initialize our equity data for symbol in EquitySymbols: equity = self.AddEquity(symbol, Resolution.Daily) self.Data[symbol] = SymbolData(equity.Symbol, RollingWindowSize) # loop through all our symbols and request data subscriptions and initialize indicator for symbol, symbolData in self.Data.items(): # define the indicator symbolData.MACD = MovingAverageConvergenceDivergence("MACD", 12, 26, 9, Resolution.Daily) # define a consolidator to consolidate data for this symbol on the requested period consolidator = TradeBarConsolidator(1) # 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): self.Data[bar.Symbol.Value].MACD.Update(bar.Time, bar.Close) self.Data[bar.Symbol.Value].Bars.Add(bar) def OnData(self,data): # loop through each symbol in our structure for symbol in self.Data.keys(): symbolData = self.Data[symbol] if symbolData.IsReady() : ## This works and shows all data points are accessible self.Debug(str(symbol) + str(symbolData.MACD.Slow.Current.Value)) self.Debug(str(symbol) + str(symbolData.MACD.Fast.Current.Value)) ## This does not work.. what is the correct way to access the indexed MACD values? #self.Debug(str(symbol) + str(symbolData.Bars[0].MACD.Fast)) class SymbolData(object): def __init__(self, symbol, windowSize): self.Symbol = symbol self.Bars = RollingWindow[IBaseDataBar](windowSize) # The MACD indicator for our symbol self.MACD = 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.MACD.IsReady