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MACDTrendAlgorithm.py
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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.Indicators")
AddReference("QuantConnect.Common")
from System import *
from QuantConnect import *
from QuantConnect.Algorithm import *
from QuantConnect.Indicators import *
from datetime import datetime
### <summary>
### Simple indicator demonstration algorithm of MACD
### </summary>
### <meta name="tag" content="indicators" />
### <meta name="tag" content="indicator classes" />
### <meta name="tag" content="plotting indicators" />
class MACDTrendAlgorithm(QCAlgorithm):
def Initialize(self):
'''Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.'''
self.SetStartDate(2004, 1, 1) #Set Start Date
self.SetEndDate(2015, 1, 1) #Set End Date
self.SetCash(100000) #Set Strategy Cash
# Find more symbols here: http://quantconnect.com/data
self.AddEquity("SPY", Resolution.Daily)
# define our daily macd(12,26) with a 9 day signal
self.__macd = self.MACD("SPY", 12, 26, 9, MovingAverageType.Exponential, Resolution.Daily)
self.__previous = datetime.min
self.PlotIndicator("MACD", True, self.__macd, self.__macd.Signal)
self.PlotIndicator("SPY", self.__macd.Fast, self.__macd.Slow)
def OnData(self, data):
'''OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.'''
# wait for our macd to fully initialize
if not self.__macd.IsReady: return
# only once per day
if self.__previous.date() == self.Time.date(): return
# define a small tolerance on our checks to avoid bouncing
tolerance = 0.0025
holdings = self.Portfolio["SPY"].Quantity
signalDeltaPercent = (self.__macd.Current.Value - self.__macd.Signal.Current.Value)/self.__macd.Fast.Current.Value
# if our macd is greater than our signal, then let's go long
if holdings <= 0 and signalDeltaPercent > tolerance: # 0.01%
# longterm says buy as well
self.SetHoldings("SPY", 1.0)
# of our macd is less than our signal, then let's go short
elif holdings >= 0 and signalDeltaPercent < -tolerance:
self.Liquidate("SPY")
self.__previous = self.Time