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.
176 lines
6.7 KiB
Python
176 lines
6.7 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.Indicators")
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AddReference("QuantConnect.Common")
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from System import *
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from QuantConnect import *
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from QuantConnect.Algorithm import *
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from QuantConnect.Data import *
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from QuantConnect.Indicators import *
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from QuantConnect.Orders import *
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from QuantConnect.Securities import *
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import decimal as d
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### <summary>
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### Regression test for history and warm up using the data available in open source.
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### </summary>
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### <meta name="tag" content="history and warm up" />
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### <meta name="tag" content="history" />
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### <meta name="tag" content="regression test" />
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### <meta name="tag" content="warm up" />
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class IndicatorWarmupAlgorithm(QCAlgorithm):
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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.SetStartDate(2013, 10, 8) #Set Start Date
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self.SetEndDate(2013, 10, 11) #Set End Date
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self.SetCash(1000000) #Set Strategy Cash
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# Find more symbols here: http://quantconnect.com/data
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self.AddEquity("SPY")
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self.AddEquity("IBM")
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self.AddEquity("BAC")
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self.AddEquity("GOOG", Resolution.Daily)
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self.AddEquity("GOOGL", Resolution.Daily)
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self.__sd = { }
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for security in self.Securities:
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self.__sd[security.Key] = self.SymbolData(security.Key, self)
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# we want to warm up our algorithm
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self.SetWarmup(self.SymbolData.RequiredBarsWarmup)
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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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# we are only using warmup for indicator spooling, so wait for us to be warm then continue
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if self.IsWarmingUp: return
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for sd in self.__sd.values():
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lastPriceTime = sd.Close.Current.Time
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if self.RoundDown(lastPriceTime, sd.Security.SubscriptionDataConfig.Increment):
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sd.Update()
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def OnOrderEvent(self, fill):
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sd = self.__sd.get(fill.Symbol, None)
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if sd is not None:
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sd.OnOrderEvent(fill)
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def RoundDown(self, time, increment):
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if increment.days != 0:
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return time.hour == 0 and time.minute == 0 and time.second == 0
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else:
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return time.second == 0
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class SymbolData:
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RequiredBarsWarmup = 40
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PercentTolerance = 0.001
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PercentGlobalStopLoss = 0.01
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LotSize = 10
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def __init__(self, symbol, algorithm):
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self.Symbol = symbol
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self.__algorithm = algorithm # if we're receiving daily
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self.__currentStopLoss = None
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self.Security = algorithm.Securities[symbol]
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self.Close = algorithm.Identity(symbol)
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self.ADX = algorithm.ADX(symbol, 14)
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self.EMA = algorithm.EMA(symbol, 14)
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self.MACD = algorithm.MACD(symbol, 12, 26, 9)
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self.IsReady = self.Close.IsReady and self.ADX.IsReady and self.EMA.IsReady and self.MACD.IsReady
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self.IsUptrend = False
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self.IsDowntrend = False
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def Update(self):
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self.IsReady = self.Close.IsReady and self.ADX.IsReady and self.EMA.IsReady and self.MACD.IsReady
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tolerance = d.Decimal(1 - self.PercentTolerance)
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self.IsUptrend = self.MACD.Signal.Current.Value > self.MACD.Current.Value * tolerance and\
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self.EMA.Current.Value > self.Close.Current.Value * tolerance
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self.IsDowntrend = self.MACD.Signal.Current.Value < self.MACD.Current.Value * tolerance and\
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self.EMA.Current.Value < self.Close.Current.Value * tolerance
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self.TryEnter()
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self.TryExit()
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def TryEnter(self):
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# can't enter if we're already in
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if self.Security.Invested: return False
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qty = 0
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limit = 0.0
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if self.IsUptrend:
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# 100 order lots
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qty = self.LotSize
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limit = self.Security.Low
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elif self.IsDowntrend:
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qty = -self.LotSize
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limit = self.Security.High
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if qty != 0:
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ticket = self.__algorithm.LimitOrder(self.Symbol, qty, limit, "TryEnter at: {0}".format(limit))
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def TryExit(self):
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# can't exit if we haven't entered
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if not self.Security.Invested: return
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limit = 0
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qty = self.Security.Holdings.Quantity
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exitTolerance = d.Decimal(1 + 2 * self.PercentTolerance)
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if self.Security.Holdings.IsLong and self.Close.Current.Value * exitTolerance < self.EMA.Current.Value:
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limit = self.Security.High
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elif self.Security.Holdings.IsShort and self.Close.Current.Value > self.EMA.Current.Value * exitTolerance:
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limit = self.Security.Low
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if limit != 0:
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ticket = self.__algorithm.LimitOrder(self.Symbol, -qty, limit, "TryExit at: {0}".format(limit))
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def OnOrderEvent(self, fill):
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if fill.Status != OrderStatus.Filled: return
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qty = self.Security.Holdings.Quantity
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# if we just finished entering, place a stop loss as well
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if self.Security.Invested:
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stop = fill.FillPrice*d.Decimal(1 - self.PercentGlobalStopLoss) if self.Security.Holdings.IsLong \
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else fill.FillPrice*d.Decimal(1 + self.PercentGlobalStopLoss)
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self.__currentStopLoss = self.__algorithm.StopMarketOrder(self.Symbol, -qty, stop, "StopLoss at: {0}".format(stop))
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# check for an exit, cancel the stop loss
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elif (self.__currentStopLoss is not None and self.__currentStopLoss.Status is not OrderStatus.Filled):
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# cancel our current stop loss
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self.__currentStopLoss.Cancel("Exited position")
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self.__currentStopLoss = None |