58f0caf647
Some python algorithms suffered corrections to run under the new python framework (pythonnet). Others were deleted because some features will be supported in futures implementations. Adds a method in AlgorithmPythonUtil to transform C# DateTime into Python datetime
72 lines
3.0 KiB
Python
72 lines
3.0 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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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.Data.Market import *
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from QuantConnect.Orders import *
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class DelistingEventsAlgorithm(QCAlgorithm):
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'''Showcases the delisting event of QCAlgorithm
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The data for this algorithm isn't in the github repo, so this will need to be run on the QC site'''
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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(2007, 05, 16) #Set Start Date
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self.SetEndDate(2007, 05, 25) #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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aaa = self.AddEquity("AAA", Resolution.Daily)
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spy = self.AddEquity("SPY", Resolution.Daily)
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self.aaa = aaa.Symbol
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self.spy = spy.Symbol
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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 self.Transactions.OrdersCount == 0:
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self.SetHoldings(self.aaa, 1)
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self.Debug("Purchased stock")
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for kvp in data.Bars:
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symbol = kvp.Key
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value = kvp.Value
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self.Log("OnData(Slice): {0}: {1}: {2}".format(self.Time, symbol, value.Close))
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# the slice can also contain delisting data: data.Delistings in a dictionary string->Delisting
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for kvp in data.Delistings:
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symbol = kvp.Key
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value = kvp.Value
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if value.Type == DelistingType.Warning:
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self.Log("OnData(Delistings): {0}: {1} will be delisted at end of day today.".format(self.Time, symbol))
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if value.Type == DelistingType.Delisted:
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self.Log("OnData(Delistings): {0}: {1} has been delisted.".format(self.Time, symbol))
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def OnOrderEvent(self, orderEvent):
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self.Log("OnOrderEvent(OrderEvent): {0}: {1}".format(self.Time, orderEvent)) |