Jared Broad
74 lines
3.1 KiB
Python
74 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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datapoint = IndicatorDataPoint(index, row["close"])
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self.fast.Update(datapoint)
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self.slow.Update(datapoint)
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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) |