# 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.

import clr
clr.AddReference("System")
clr.AddReference("QuantConnect.Algorithm")
clr.AddReference("QuantConnect.Indicators")
clr.AddReference("QuantConnect.Common")

from System import *
from QuantConnect import *
from QuantConnect.Algorithm import *
from QuantConnect.Indicators import *
from QuantConnect.Data 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.SetStartDate(2019, 2, 1)    #Set Start Date
      #  self.SetEndDate(2019, 6, 1)      #Set End Date
        self.SetCash(100000)             #Set Strategy Cash
        # Find more symbols here: http://quantconnect.com/data
        self.AddEquity("AAPL")
        
        #def OnData(self, data):
       
        #  tradeBarHistory = self.data.close["AAPL", -1]
        #  stopPrice = tradeBarHistory * (.9975)
        
        # create a 15 day exponential moving average
        self.fast = self.EMA("AAPL", 15, Resolution.Minute)

        # create a 30 day exponential moving average
        self.slow = self.EMA("AAPL", 5, Resolution.Minute)

        self.previous = None


def OnData(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

        

        #stop loss calculate
        tradeBarHistory = self.data.close["AAPL", -1]
        stopPrice = tradeBarHistory * (.9975)


        # define a small tolerance on our checks to avoid bouncing
        tolerance = 0.000015

        holdings = self.Portfolio["AAPL"].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.Securities["AAPL"].Price *(1 + tolerance):
                self.Log("BUY  >> {0}".format(self.Securities["AAPL"].Price))
                self.SetHoldings("AAPL", 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.Securities["AAPL"].Price:
            self.Log("SELL >> {0}".format(self.Securities["AAPL"].Price))
            self.Liquidate("AAPL")

        self.previous = self.Time
        #    self.SetHoldings("SPY", 1)
        
       
       
        if holdings > 0 and self.Securities["AAPL"].Price < stopPrice:
            self.Log("SELL >> {0}".format(self.Securities["AAPL"].Price))
            self.Liquidate("AAPL")