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.
73 lines
3.1 KiB
Python
73 lines
3.1 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 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.Data import *
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from QuantConnect.Algorithm import *
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from QuantConnect.Indicators import *
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### <summary>
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### This algorithm demonstrates using the history provider to retrieve data
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### to warm up indicators before data is received.
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### </summary>
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### <meta name="tag" content="indicators" />
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### <meta name="tag" content="history" />
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### <meta name="tag" content="history and warm up" />
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### <meta name="tag" content="using data" />
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class WarmupHistoryAlgorithm(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(2014,5,2) #Set Start Date
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self.SetEndDate(2014,5,2) #Set End Date
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self.SetCash(100000) #Set Strategy Cash
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# Find more symbols here: http://quantconnect.com/data
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forex = self.AddForex("EURUSD", Resolution.Second)
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forex = self.AddForex("NZDUSD", Resolution.Second)
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fast_period = 60
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slow_period = 3600
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self.fast = self.EMA("EURUSD", fast_period)
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self.slow = self.EMA("EURUSD", slow_period)
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# "slow_period + 1" because rolling window waits for one to fall off the back to be considered ready
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# History method returns a dict with a pandas.DataFrame
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history = self.History(["EURUSD", "NZDUSD"], slow_period + 1)
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# prints out the tail of the dataframe
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self.Log(str(history.loc["EURUSD"].tail()))
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self.Log(str(history.loc["NZDUSD"].tail()))
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for index, row in history.loc["EURUSD"].iterrows():
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self.fast.Update(index, row["close"])
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self.slow.Update(index, row["close"])
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self.Log("FAST {0} READY. Samples: {1}".format("IS" if self.fast.IsReady else "IS NOT", self.fast.Samples))
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self.Log("SLOW {0} READY. Samples: {1}".format("IS" if self.slow.IsReady else "IS NOT", self.slow.Samples))
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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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if self.fast.Current.Value > self.slow.Current.Value:
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self.SetHoldings("EURUSD", 1)
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else:
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self.SetHoldings("EURUSD", -1) |