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.
187 lines
11 KiB
Python
187 lines
11 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 clr import AddReference
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AddReference("System")
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AddReference("QuantConnect.Algorithm")
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AddReference("QuantConnect.Common")
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AddReference("QuantConnect.Indicators")
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from System import *
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from QuantConnect import *
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from QuantConnect.Indicators import *
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from QuantConnect.Data import *
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from QuantConnect.Data.Market import *
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from QuantConnect.Data.Custom import *
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from QuantConnect.Algorithm import *
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### <summary>
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### Basic template algorithm simply initializes the date range and cash. This is a skeleton
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### framework you can use for designing an algorithm.
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### </summary>
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### <meta name="tag" content="using data" />
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### <meta name="tag" content="using quantconnect" />
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### <meta name="tag" content="trading and orders" />
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class IndicatorSuiteAlgorithm(QCAlgorithm):
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'''Demonstration algorithm of popular indicators and plotting them.'''
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def Initialize(self):
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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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self.symbol = "SPY"
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self.customSymbol = "WIKI/FB"
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self.price = None
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self.SetStartDate(2013, 1, 1) #Set Start Date
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self.SetEndDate(2014, 12, 31) #Set End Date
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self.SetCash(25000) #Set Strategy Cash
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# Find more symbols here: http://quantconnect.com/data
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self.AddEquity(self.symbol, Resolution.Daily)
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self.AddData(Quandl, self.customSymbol, Resolution.Daily)
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# Set up default Indicators, these indicators are defined on the Value property of incoming data (except ATR and AROON which use the full TradeBar object)
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self.indicators = {
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'BB' : self.BB(self.symbol, 20, 1, MovingAverageType.Simple, Resolution.Daily),
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'RSI' : self.RSI(self.symbol, 14, MovingAverageType.Simple, Resolution.Daily),
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'EMA' : self.EMA(self.symbol, 14, Resolution.Daily),
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'SMA' : self.SMA(self.symbol, 14, Resolution.Daily),
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'MACD' : self.MACD(self.symbol, 12, 26, 9, MovingAverageType.Simple, Resolution.Daily),
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'MOM' : self.MOM(self.symbol, 20, Resolution.Daily),
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'MOMP' : self.MOMP(self.symbol, 20, Resolution.Daily),
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'STD' : self.STD(self.symbol, 20, Resolution.Daily),
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# by default if the symbol is a tradebar type then it will be the min of the low property
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'MIN' : self.MIN(self.symbol, 14, Resolution.Daily),
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# by default if the symbol is a tradebar type then it will be the max of the high property
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'MAX' : self.MAX(self.symbol, 14, Resolution.Daily),
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'ATR' : self.ATR(self.symbol, 14, MovingAverageType.Simple, Resolution.Daily),
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'AROON' : self.AROON(self.symbol, 20, Resolution.Daily)
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}
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# Here we're going to define indicators using 'selector' functions. These 'selector' functions will define what data gets sent into the indicator
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# These functions have a signature like the following: decimal Selector(BaseData baseData), and can be defined like: baseData => baseData.Value
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# We'll define these 'selector' functions to select the Low value
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#
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# For more information on 'anonymous functions' see: http:#en.wikipedia.org/wiki/Anonymous_function
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# https:#msdn.microsoft.com/en-us/library/bb397687.aspx
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#
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self.selectorIndicators = {
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'BB' : self.BB(self.symbol, 20, 1, MovingAverageType.Simple, Resolution.Daily, Field.Low),
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'RSI' :self.RSI(self.symbol, 14, MovingAverageType.Simple, Resolution.Daily, Field.Low),
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'EMA' :self.EMA(self.symbol, 14, Resolution.Daily, Field.Low),
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'SMA' :self.SMA(self.symbol, 14, Resolution.Daily, Field.Low),
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'MACD' : self.MACD(self.symbol, 12, 26, 9, MovingAverageType.Simple, Resolution.Daily, Field.Low),
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'MOM' : self.MOM(self.symbol, 20, Resolution.Daily, Field.Low),
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'MOMP' : self.MOMP(self.symbol, 20, Resolution.Daily, Field.Low),
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'STD' : self.STD(self.symbol, 20, Resolution.Daily, Field.Low),
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'MIN' : self.MIN(self.symbol, 14, Resolution.Daily, Field.High),
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'MAX' : self.MAX(self.symbol, 14, Resolution.Daily, Field.Low),
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# ATR and AROON are special in that they accept a TradeBar instance instead of a decimal, we could easily project and/or transform the input TradeBar
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# before it gets sent to the ATR/AROON indicator, here we use a function that will multiply the input trade bar by a factor of two
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'ATR' : self.ATR(self.symbol, 14, MovingAverageType.Simple, Resolution.Daily, Func[IBaseData, IBaseDataBar](self.selector_double_TradeBar)),
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'AROON' : self.AROON(self.symbol, 20, Resolution.Daily, Func[IBaseData, IBaseDataBar](self.selector_double_TradeBar))
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}
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# Custom Data Indicator:
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self.rsiCustom = self.RSI(self.customSymbol, 14, MovingAverageType.Simple, Resolution.Daily)
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self.minCustom = self.MIN(self.customSymbol, 14, Resolution.Daily)
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self.maxCustom = self.MAX(self.customSymbol, 14, Resolution.Daily)
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# in addition to defining indicators on a single security, you can all define 'composite' indicators.
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# these are indicators that require multiple inputs. the most common of which is a ratio.
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# suppose we seek the ratio of BTC to SPY, we could write the following:
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spyClose = Identity(self.symbol)
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fbClose = Identity(self.customSymbol)
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# this will create a new indicator whose value is FB/SPY
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self.ratio = IndicatorExtensions.Over(fbClose, spyClose)
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# we can also easily plot our indicators each time they update using th PlotIndicator function
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self.PlotIndicator("Ratio", self.ratio)
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# The following methods will add multiple charts to the algorithm output.
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# Those chatrs names will be used later to plot different series in a particular chart.
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# For more information on Lean Charting see: https://www.quantconnect.com/docs#Charting
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Chart('BB')
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Chart('STD')
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Chart('ATR')
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Chart('AROON')
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Chart('MACD')
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Chart('Averages')
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# Here we make use of the Schelude method to update the plots once per day at market close.
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self.Schedule.On(self.DateRules.EveryDay(), self.TimeRules.BeforeMarketClose(self.symbol), self.update_plots)
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def OnData(self, data):
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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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Arguments:
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data: Slice object keyed by symbol containing the stock data
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'''
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if (#not data.Bars.ContainsKey(self.symbol) or
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not self.indicators['BB'].IsReady or
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not self.indicators['RSI'].IsReady):
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return
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self.price = data[self.symbol].Close
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if not self.Portfolio.HoldStock:
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quantity = int(self.Portfolio.Cash / self.price)
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self.Order(self.symbol, quantity)
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self.Debug('Purchased SPY on ' + self.Time.strftime('%Y-%m-%d'))
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def update_plots(self):
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if not self.indicators['BB'].IsReady or not self.indicators['STD'].IsReady:
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return
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# Plots can also be created just with this one line command.
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self.Plot('RSI', self.indicators['RSI'])
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# Custom data indicator
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self.Plot('RSI-FB', self.rsiCustom)
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# Here we make use of the chats decalred in the Initialize method, plotting multiple series
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# in each chart.
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self.Plot('STD', 'STD', self.indicators['STD'].Current.Value)
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self.Plot('BB', 'Price', self.price)
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self.Plot('BB', 'BollingerUpperBand', self.indicators['BB'].UpperBand.Current.Value)
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self.Plot('BB', 'BollingerMiddleBand', self.indicators['BB'].MiddleBand.Current.Value)
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self.Plot('BB', 'BollingerLowerBand', self.indicators['BB'].LowerBand.Current.Value)
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self.Plot('AROON', 'Aroon', self.indicators['AROON'].Current.Value)
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self.Plot('AROON', 'AroonUp', self.indicators['AROON'].AroonUp.Current.Value)
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self.Plot('AROON', 'AroonDown', self.indicators['AROON'].AroonDown.Current.Value)
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# The following Plot method calls are commented out because of the 10 series limit for backtests
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#self.Plot('ATR', 'ATR', self.indicators['ATR'].Current.Value)
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#self.Plot('ATR', 'ATRDoubleBar', self.selectorIndicators['ATR'].Current.Value)
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#self.Plot('Averages', 'SMA', self.indicators['SMA'].Current.Value)
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#self.Plot('Averages', 'EMA', self.indicators['EMA'].Current.Value)
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#self.Plot('MOM', self.indicators['MOM'].Current.Value)
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#self.Plot('MOMP', self.indicators['MOMP'].Current.Value)
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#self.Plot('MACD', 'MACD', self.indicators['MACD'].Current.Value)
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#self.Plot('MACD', 'MACDSignal', self.indicators['MACD'].Signal.Current.Value)
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def selector_double_TradeBar(self, bar):
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trade_bar = TradeBar()
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trade_bar.Close = 2 * bar.Close
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trade_bar.DataType = bar.DataType
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trade_bar.High = 2 * bar.High
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trade_bar.Low = 2 * bar.Low
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trade_bar.Open = 2 * bar.Open
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trade_bar.Symbol = bar.Symbol
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trade_bar.Time = bar.Time
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trade_bar.Value = 2 * bar.Value
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trade_bar.Period = bar.Period
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return trade_bar |