# 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>
### In this example we look at the canonical 15/30 day moving average cross. This algorithm
### will go long when the 15 crosses above the 30 and will liquidate when the 15 crosses
### back below the 30.
### </summary>
### <meta name="tag" content="indicators" />
### <meta name="tag" content="indicator classes" />
### <meta name="tag" content="moving average cross" />
### <meta name="tag" content="strategy example" />
class MovingAverageCrossAlgorithm(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(2009, 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")

 # create a 15 day exponential moving average
 self.fast = self.ema("SPY", 15, Resolution.DAILY)

 # create a 30 day exponential moving average
 self.slow = self.ema("SPY", 30, Resolution.DAILY)

 self.previous = None


 def on_data(self, data):
 '''OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.'''
 # a couple things to notice in this method:
 # 1. We never need to 'update' our indicators with the data, the engine takes care of this for us
 # 2. We can use indicators directly in math expressions
 # 3. We can easily plot many indicators at the same time

 # wait for our slow ema to fully initialize
 if not self.slow.is_ready:
 return

 # only once per day
 if self.previous is not None and self.previous.date() == self.time.date():
 return

 # define a small tolerance on our checks to avoid bouncing
 tolerance = 0.00015

 holdings = self.portfolio["SPY"].quantity

 # we only want to go long if we're currently short or flat
 if holdings <= 0:
 # if the fast is greater than the slow, we'll go long
 if self.fast.current.value > self.slow.current.value *(1 + tolerance):
 self.log("BUY >> {0}".format(self.securities["SPY"].price))
 self.set_holdings("SPY", 1.0)

 # we only want to liquidate if we're currently long
 # if the fast is less than the slow we'll liquidate our long
 if holdings > 0 and self.fast.current.value < self.slow.current.value:
 self.log("SELL >> {0}".format(self.securities["SPY"].price))
 self.liquidate("SPY")

 self.previous = self.time