# 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.

### <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", Resolution.Minute)
            
            # create a 15 day exponential moving average
            self.fast = self.EMA("AAPL", 15, Resolution.Minute)
    
    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
            
            # if no data fired, return
            if not data["AAPL"]: 
                return
            
            # wait for EMA to fully initialize
            if not self.fast.IsReady:
                return
            
            # stop loss calculate
            tradeBarHistory = data["AAPL"].Open # use the open price of minute bar as benchmark to calculate stop loss
            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 current price is greater than the EMA, we'll go long
                if self.Securities["AAPL"].Price > self.fast.Current.Value * (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 current price is less than the EMA we'll liquidate our long
            if holdings > 0 and self.Securities["AAPL"].Price < self.fast.Current.Value:
                self.Log("SELL >> {0}".format(self.Securities["AAPL"].Price))
                self.Liquidate("AAPL")
           
            if holdings > 0 and self.Securities["AAPL"].Price < stopPrice:
                self.Log("SELL >> {0}".format(self.Securities["AAPL"].Price))
                self.Liquidate("AAPL")