Stefano Raggi
123 lines
6.1 KiB
Python
123 lines
6.1 KiB
Python
# 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, 12, 01)
|
|
self.SetEndDate(2015, 02, 01)
|
|
|
|
# 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):
|
|
|
|
self.Data[bar.Symbol.Value].SMA.Update(bar.Time, bar.Close)
|
|
self.Data[bar.Symbol.Value].Bars.Add(bar)
|
|
|
|
# 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)
|
|
|
|
# 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
|
|
|
|
# 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 |