from System import *
from QuantConnect import *
from QuantConnect.Algorithm import *
from QuantConnect.Indicators import *
from QuantConnect.Data.Market import TradeBar
from datetime import datetime

class RollingWindowAlgorithm(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(2004,10,1)  #Set Start Date
        self.SetEndDate(2013,11,1)    #Set End Date
        self.SetCash(100000)           #Set Strategy Cash
        # Find more symbols here: http://quantconnect.com/data
        self.AddEquity("SPY", Resolution.Daily)

        # define our daily macd(12,26) with a 9 day signal
        self.__macd = self.MACD("SPY", 9, 26, 9, MovingAverageType.Exponential, Resolution.Daily)
        self.__previous = datetime.min
        self.PlotIndicator("MACD", True, self.__macd, self.__macd.Signal)
        self.PlotIndicator("SPY", self.__macd.Fast, self.__macd.Slow)

        # Creates a Rolling Window indicator to keep the 2 TradeBar
        self.window = RollingWindow[TradeBar](2)    # For other security types, use QuoteBar

        # Creates an indicator and adds to a rolling window when it is updated
        #self.MACD("SPY", 9, 26, 9, MovingAverageType.Exponential, Resolution.Daily).Updated += self.MacdUpdated
        self.SMA("SPY", 5).Updated += self.MacdUpdated
        self.MacdWin = RollingWindow[IndicatorDataPoint,](5)


    def MacdUpdated(self, sender, updated):
        '''Adds updated values to rolling window'''
        self.MacdWin.Add(updated)


    def OnData(self, data):
        '''OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.'''

        # Add SPY TradeBar in rollling window
        self.window.Add(data["SPY"])

        # Wait for windows to be ready.
        if not (self.window.IsReady and self.MacdWin.IsReady and 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
        
        currBar = self.window[0]                     # Current bar had index zero.
        pastBar = self.window[1]                     # Past bar has index one.
        self.Log("Price: {0} -> {1} ... {2} -> {3}".format(pastBar.Time, pastBar.Close, currBar.Time, currBar.Close))

        currMacd = self.MacdWin[0]                     # Current SMA had index zero.
        pastMacd = self.MacdWin[self.MacdWin.Count-1]   # Oldest SMA has index of window count minus 1.
        self.Log("SMA:   {0} -> {1} ... {2} -> {3}".format(pastMacd.Time, pastMacd.Value, currMacd.Time, currMacd.Value))

        if not self.Portfolio.Invested and currMacd.Value > pastMacd.Value:
            self.SetHoldings("SPY", 1.0)

        # if our macd is greater than our signal, then let's go long
        if holdings <= 0 and signalDeltaPercent > tolerance:  # 0.01%
            # longterm says buy as well
            self.SetHoldings("SPY", 1.0)

        # of our macd is less than our signal, then let's go short
        elif holdings >= 0 and signalDeltaPercent < -tolerance:
            self.Liquidate("SPY")

        self.__previous = self.Time