Michael Handschuh
fdc866fda0
We didn't experience the expected performance improvements. Locally under unit test there was aboout an order of magnitude throughput increase, but when run against the history benchmark, this new approach was 60% slower. We're reverting this for now to perform further analysis and better understand the performance profiling of the python history stack.
123 lines
6.1 KiB
Python
123 lines
6.1 KiB
Python
# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
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# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from System import *
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from QuantConnect import *
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from QuantConnect.Data.Consolidators import *
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from QuantConnect.Data.Market import *
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from QuantConnect.Orders import OrderStatus
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from QuantConnect.Algorithm import QCAlgorithm
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from QuantConnect.Indicators import *
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import numpy as np
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from datetime import timedelta, datetime
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### <summary>
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### Example structure for structuring an algorithm with indicator and consolidator data for many tickers.
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### </summary>
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### <meta name="tag" content="consolidating data" />
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### <meta name="tag" content="indicators" />
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### <meta name="tag" content="using data" />
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### <meta name="tag" content="strategy example" />
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class MultipleSymbolConsolidationAlgorithm(QCAlgorithm):
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# Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.
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def Initialize(self):
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# This is the period of bars we'll be creating
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BarPeriod = TimeSpan.FromMinutes(10)
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# This is the period of our sma indicators
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SimpleMovingAveragePeriod = 10
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# This is the number of consolidated bars we'll hold in symbol data for reference
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RollingWindowSize = 10
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# Holds all of our data keyed by each symbol
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self.Data = {}
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# Contains all of our equity symbols
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EquitySymbols = ["AAPL","SPY","IBM"]
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# Contains all of our forex symbols
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ForexSymbols =["EURUSD", "USDJPY", "EURGBP", "EURCHF", "USDCAD", "USDCHF", "AUDUSD","NZDUSD"]
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self.SetStartDate(2014, 12, 1)
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self.SetEndDate(2015, 2, 1)
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# initialize our equity data
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for symbol in EquitySymbols:
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equity = self.AddEquity(symbol)
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self.Data[symbol] = SymbolData(equity.Symbol, BarPeriod, RollingWindowSize)
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# initialize our forex data
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for symbol in ForexSymbols:
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forex = self.AddForex(symbol)
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self.Data[symbol] = SymbolData(forex.Symbol, BarPeriod, RollingWindowSize)
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# loop through all our symbols and request data subscriptions and initialize indicator
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for symbol, symbolData in self.Data.items():
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# define the indicator
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symbolData.SMA = SimpleMovingAverage(self.CreateIndicatorName(symbol, "SMA" + str(SimpleMovingAveragePeriod), Resolution.Minute), SimpleMovingAveragePeriod)
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# define a consolidator to consolidate data for this symbol on the requested period
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consolidator = TradeBarConsolidator(BarPeriod) if symbolData.Symbol.SecurityType == SecurityType.Equity else QuoteBarConsolidator(BarPeriod)
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# write up our consolidator to update the indicator
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consolidator.DataConsolidated += self.OnDataConsolidated
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# we need to add this consolidator so it gets auto updates
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self.SubscriptionManager.AddConsolidator(symbolData.Symbol, consolidator)
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def OnDataConsolidated(self, sender, bar):
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self.Data[bar.Symbol.Value].SMA.Update(bar.Time, bar.Close)
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self.Data[bar.Symbol.Value].Bars.Add(bar)
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# OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.
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# Argument "data": Slice object, dictionary object with your stock data
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def OnData(self,data):
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# loop through each symbol in our structure
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for symbol in self.Data.keys():
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symbolData = self.Data[symbol]
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# this check proves that this symbol was JUST updated prior to this OnData function being called
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if symbolData.IsReady() and symbolData.WasJustUpdated(self.Time):
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if not self.Portfolio[symbol].Invested:
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self.MarketOrder(symbol, 1)
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# 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).
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# Method is called 10 minutes before closing to allow user to close out position.
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def OnEndOfDay(self):
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i = 0
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for symbol in sorted(self.Data.keys()):
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symbolData = self.Data[symbol]
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# we have too many symbols to plot them all, so plot every other
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i += 1
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if symbolData.IsReady() and i%2 == 0:
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self.Plot(symbol, symbol, symbolData.SMA.Current.Value)
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class SymbolData(object):
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def __init__(self, symbol, barPeriod, windowSize):
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self.Symbol = symbol
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# The period used when population the Bars rolling window
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self.BarPeriod = barPeriod
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# A rolling window of data, data needs to be pumped into Bars by using Bars.Update( tradeBar ) and can be accessed like:
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# mySymbolData.Bars[0] - most first recent piece of data
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# mySymbolData.Bars[5] - the sixth most recent piece of data (zero based indexing)
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self.Bars = RollingWindow[IBaseDataBar](windowSize)
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# The simple moving average indicator for our symbol
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self.SMA = None
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# Returns true if all the data in this instance is ready (indicators, rolling windows, ect...)
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def IsReady(self):
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return self.Bars.IsReady and self.SMA.IsReady
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# Returns true if the most recent trade bar time matches the current time minus the bar's period, this
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# indicates that update was just called on this instance
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def WasJustUpdated(self, current):
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return self.Bars.Count > 0 and self.Bars[0].Time == current - self.BarPeriod |