from AlgorithmImports import *
### <summary>### Simple indicator demonstration algorithm of MACD### </summary>### <meta name="tag" content="indicators" />### <meta name="tag" content="indicator classes" />### <meta name="tag" content="plotting indicators" />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(2010, 1, 1)    #Set Start Date        self.SetEndDate(2021, 11, 27)    #Set End Date        self.SetCash(100000)             #Set Strategy Cash
        # add SPY to our portfolio        self.AddEquity("SPY")        # create a 50 day exponential moving average        self.fast = self.EMA("SPY", 50, Resolution.Daily)        # create a 200 day exponential moving average        self.slow = self.EMA("SPY", 200, Resolution.Daily)        # warm up the inidicator (i.e. give it previous data)        self.SetWarmUp(200)        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                # Docs: https://www.quantconnect.com/docs/algorithm-reference/indicators                # wait for our indicators to fully initialize        if not self.slow.IsReady and not self.IsWarmingUp:            return                # only once per day        if self.previous is not None and self.previous.date() == self.Time.date():            return                # plot our indicators        self.Plot("EMA", self.slow, self.fast)                # define a small tolerance on our checks to avoid bouncing          # we only want to go long if we're currently short or flat
        # we only want to liquidate if we're currently long        # if the fast is less than the slow we'll liquidate our long     
        self.previous = self.Time


class MACDTrendAlgorithm(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(2004, 1, 1)    #Set Start Date        self.SetEndDate(2023, 10, 20)      #Set End Date        self.SetCash(100000)             #Set Strategy Cash
    def OnData(self, data):        '''OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.'''        # wait for our macd to fully initialize        if not self.__macd.IsReady: return
        # only once per day        if self.__previous.date() == self.Time.date(): return
        # define a small tolerance on our checks to avoid bouncing        tolerance = 0.0025
        holdings = self.Portfolio["SPY"].Quantity
        signalDeltaPercent = (self.__macd.Current.Value - self.__macd.Signal.Current.Value)/self.__macd.Fast.Current.Value



        self.__previous = self.Time