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(2021,1,1)    #Set Start Date
        self.SetEndDate(2021,5,15)      #Set End Date
        self.SetCash(1000000)             #Set Strategy Cash
        # Find more symbols here: http://quantconnect.com/data
        self.AddEquity("QQQ")
        option = self.AddOption("QQQ", Resolution.Minute) # Add the option corresponding to underlying stock
    
        self.symbol = option.Symbol
        
        option.SetFilter(-2, +2, timedelta(6), timedelta(13))
        
        # create a 15 day exponential moving average
        self.fast = self.EMA("QQQ", 50, Resolution.Minute)

        # create a 30 day exponential moving average
        self.slow = self.EMA("QQQ", 200, Resolution.Minute)
        
        self.MarketTicket = None
        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 ema to fully initialize
        if not self.slow.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
        
        QQQprice = self.Securities["QQQ"].Price  # Gives close price of QQQ
        QQQopen = self.Securities["QQQ"].Open # Gives last open price of QQQ
        
        holdings = self.Portfolio["QQQ"].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):
                
                for symbol, chain in data.OptionChains.items():
                    contracts = [c for c in chain if c.Right == OptionRight.Call and c.UnderlyingLastPrice > c.Strike]
                    if len(contracts) == 0:
                        continue
                    sorted_contracts = sorted(contracts, key=lambda x: x.Strike, reverse=True)
                    contract = sorted_contracts[0].Symbol
                    self.MarketTicket = self.MarketOrder(contract, 1)

        # we only want to liquidate if we're currently long
        # if the fast is less than the slow we'll liquidate our long
        if self.Portfolio.Invested and self.fast.Current.Value < self.slow.Current.Value:
            #self.Log("SELL >> {0}".format(self.Securities["QQQ"].Price))
            self.Liquidate()

        self.previous = self.Time