# 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 AlgorithmImports import *

### <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.set_start_date(2004, 1, 1) #Set Start Date
 self.set_end_date(2015, 1, 1) #Set End Date
 self.set_cash(100000) #Set Strategy Cash
 # Find more symbols here: http://quantconnect.com/data
 self.add_equity("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.plot_indicator("MACD", True, self.__macd, self.__macd.signal)
 self.plot_indicator("SPY", self.__macd.fast, self.__macd.slow)


 def on_data(self, data):
 '''on_data 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.is_ready: 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

 signal_delta_percent = (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 signal_delta_percent > tolerance: # 0.01%
 # longterm says buy as well
 self.set_holdings("SPY", 1.0)

 # of our macd is less than our signal, then let's go short
 elif holdings >= 0 and signal_delta_percent < -tolerance:
 self.liquidate("SPY")


 self.__previous = self.time