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 *

### <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(2008, 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")
        self.AddEquity("GOOGL")
        self.AddEquity("AMZN")
        self.AddEquity("MSFT")
        self.AddEquity("AAPL")

        # create a 15 day exponential moving average
        self.fast = self.EMA("SPY", 15, Resolution.Daily)
        self.fasta = self.EMA("GOOGL", 15, Resolution.Daily)
        self.fastb = self.EMA("AMZN", 15, Resolution.Daily)
        self.fastc = self.EMA("MSFT", 15, Resolution.Daily)
        self.fastd = self.EMA("AAPL", 15, Resolution.Daily)

        # create a 30 day exponential moving average
        self.slow = self.EMA("SPY", 30, Resolution.Daily)
        self.slowa = self.EMA("GOOGL", 30, Resolution.Daily)
        self.slowb = self.EMA("AMZN", 30, Resolution.Daily)
        self.slowc = self.EMA("MSFT", 30, Resolution.Daily)
        self.slowd = self.EMA("AAPL", 30, Resolution.Daily)

        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

        # wait for our slow and fast ema to fully initialize
        if not self.slow.IsReady:
            return
        if not self.fast.IsReady:
            return
        if not self.slowa.IsReady:
            return
        if not self.fasta.IsReady:
            return
        if not self.slowb.IsReady:
            return
        if not self.fastb.IsReady:
            return
        if not self.slowc.IsReady:
            return
        if not self.fastc.IsReady:
            return
        if not self.slowd.IsReady:
            return
        if not self.fastd.IsReady:
            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
        holdingsa = self.Portfolio["GOOGL"].Quantity
        holdingsb = self.Portfolio["AMZN"].Quantity
        holdingsc = self.Portfolio["MSFT"].Quantity
        holdingsd = 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.slow.Current.Value *(1 + tolerance):
                self.Log("BUY SPY >> {0}".format(self.Securities["SPY"].Price))
                self.SetHoldings("SPY", .2)
        if holdingsa <= 0:
            if self.fasta.Current.Value > self.slowa.Current.Value *(1 + tolerance):
                self.Log("BUY GOOGL >> {0}".format(self.Securities["GOOGL"].Price))
                self.SetHoldings("GOOGL", .2)
        if holdingsb <= 0:
            if self.fastb.Current.Value > self.slowb.Current.Value *(1 + tolerance):
                self.Log("BUY AMZN >> {0}".format(self.Securities["AMZN"].Price))
                self.SetHoldings("AMZN", .2)
        if holdingsc <= 0:
            if self.fastc.Current.Value > self.slowc.Current.Value *(1 + tolerance):
                self.Log("BUY MSFT >> {0}".format(self.Securities["MSFT"].Price))
                self.SetHoldings("MSFT", .2)
        if holdingsd <= 0:
            if self.fastd.Current.Value > self.slowd.Current.Value *(1 + tolerance):
                self.Log("BUY AAPL >> {0}".format(self.Securities["AAPL"].Price))
                self.SetHoldings("AAPL", .2)

        # 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 SPY >> {0}".format(self.Securities["SPY"].Price))
            self.Liquidate("SPY")
        if holdingsa > 0 and self.fasta.Current.Value < self.slowa.Current.Value:
            self.Log("SELL GOOGL >> {0}".format(self.Securities["GOOGL"].Price))
            self.Liquidate("GOOGL")
        if holdingsb > 0 and self.fastb.Current.Value < self.slowb.Current.Value:
            self.Log("SELL AMZN >> {0}".format(self.Securities["AMZN"].Price))
            self.Liquidate("AMZN")
        if holdingsc > 0 and self.fastc.Current.Value < self.slowc.Current.Value:
            self.Log("SELL MSFT >> {0}".format(self.Securities["MSFT"].Price))
            self.Liquidate("MSFT")
        if holdingsd > 0 and self.fastd.Current.Value < self.slowd.Current.Value:
            self.Log("SELL AAPL >> {0}".format(self.Securities["AAPL"].Price))
            self.Liquidate("AAPL")

        self.previous = self.Time