Overall Statistics Total Trades11Average Win0%Average Loss0%Compounding Annual Return-2.435%Drawdown2.900%Expectancy0Net Profit-0.821%Sharpe Ratio-0.463Probabilistic Sharpe Ratio17.259%Loss Rate0%Win Rate0%Profit-Loss Ratio0Alpha-0.045Beta0.275Annual Standard Deviation0.042Annual Variance0.002Information Ratio-1.25Tracking Error0.089Treynor Ratio-0.07Total Fees\$3.00
### 180129 MultipleSymbolConsolidationAlgorithm.pyFinalFromGitHub
### For Testing
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, 09, 01)
self.SetStartDate(2014, 9, 1)
self.SetEndDate(2015, 1, 1)

# initialize our equity data
for symbol in EquitySymbols:
self.Data[symbol] = SymbolData(equity.Symbol, BarPeriod, RollingWindowSize)

# initialize our forex data
for symbol in ForexSymbols:
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.items():
# 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

def OnDataConsolidated(self, sender, bar):

self.Data[bar.Symbol.Value].SMA.Update(bar.Time, bar.Close)
sma = self.Data[bar.Symbol.Value].SMA.Current.Value

# 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.SetHoldings(symbol, 0.1)
#self.MarketOrder(symbol, 1)
self.Debug("Data: {0} -> {1}".format(self.Data[symbol].Bars[1],self.Data[symbol].SMA))

# 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)
# The simple moving average indicator for our symbol
self.SMA = None

sma_lookback = 5
self.HistoricalSMA = RollingWindow[float](sma_lookback)

# Returns true if all the data in this instance is ready (indicators, rolling windows, ect...)
return self.Bars.Count > 0 and self.Bars[0].Time == current - self.BarPeriod