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# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from clr import AddReference
AddReference("System")
AddReference("QuantConnect.Algorithm")
AddReference("QuantConnect.Common")
from System import *
from QuantConnect import *
from QuantConnect.Algorithm import *
from time import sleep
### <summary>
### Example algorithm showing how to use QCAlgorithm.Train method
### </summary>
### <meta name="tag" content="using quantconnect" />
### <meta name="tag" content="training" />
class TrainingExampleAlgorithm(QCAlgorithm):
'''Example algorithm showing how to use QCAlgorithm.Train method'''
def Initialize(self):
self.SetStartDate(2013, 10, 7)
self.SetEndDate(2013, 10, 14)
self.AddEquity("SPY", Resolution.Daily)
# Set TrainingMethod to be executed immediately
self.Train(self.TrainingMethod)
# Set TrainingMethod to be executed at 8:00 am every Sunday
self.Train(self.DateRules.Every(DayOfWeek.Sunday), self.TimeRules.At(8 , 0), self.TrainingMethod)
def TrainingMethod(self):
self.Log(f'Start training at {self.Time}')
# Use the historical data to train the machine learning model
history = self.History(["SPY"], 200, Resolution.Daily)
# ML code:
pass
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