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quantconnect--lean/Algorithm.Python/DailyFxAlgorithm.py
T

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Python

# 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.Core")
AddReference("QuantConnect.Common")
from System import *
from QuantConnect import *
from QuantConnect.Data.Custom import DailyFx
from QCAlgorithm import QCAlgorithm
import numpy as np
### <summary>
### Use event/fundamental calendar information (DailyFx) to design event based forex algorithms.
### </summary>
### <meta name="tag" content="using data" />
### <meta name="tag" content="custom data" />
### <meta name="tag" content="forex" />
### <meta name="tag" content="dailyfx" />
class DailyFxAlgorithm(QCAlgorithm):
''' Add the Daily FX type to our algorithm and use its events. '''
def Initialize(self):
# Set the cash we'd like to use for our backtest
self.SetCash(100000)
# Set the start and the end date
self.SetStartDate(2016,5,26)
self.SetEndDate(2016,5,27)
self._sliceCount = 0
self._eventCount = 0
self.AddData(DailyFx, "DFX", Resolution.Second, TimeZones.Utc)
def OnData(self, data):
# Daily Fx demonstration to call on
result = data["DFX"]
self._sliceCount +=1
self.Debug("ONDATA >> {0} : {1}".format(self._sliceCount, result))