from clr import AddReference
AddReference("System")
AddReference("QuantConnect.Algorithm")
AddReference("QuantConnect.Common")

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

### <summary>
### Using rolling windows for efficient storage of historical data; which automatically clears after a period of time.
### </summary>
### <meta name="tag" content="using data" />
### <meta name="tag" content="history and warm up" />
### <meta name="tag" content="history" />
### <meta name="tag" content="warm up" />
### <meta name="tag" content="indicators" />
### <meta name="tag" content="rolling windows" />
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(2018,1,1)  #Set Start Date
        self.SetEndDate(2018,1,20)    #Set End Date
        self.SetCash(100000)           #Set Strategy Cash
        # Find more symbols here: http://quantconnect.com/data
        self.AddEquity("SVXY", Resolution.Minute)

        # 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.SMA("SVXY", 225).Updated += self.SmaUpdated
        self.smaWin = RollingWindow[IndicatorDataPoint](225)


    def SmaUpdated(self, sender, updated):
        '''Adds updated values to rolling window'''
        self.smaWin.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 SVXY TradeBar in rollling window
        self.window.Add(data["SVXY"])

        # Wait for windows to be ready.
        if not (self.window.IsReady and self.smaWin.IsReady): return

        
        currSma = self.smaWin[0]                     # Current SMA had index zero.
        pastSma = self.smaWin[1] 
        slope = currSma.Value - pastSma.Value
        self.Log("slope: " + str(slope))
        #self.Log("pSMA:{0} -> {1} ... cSMA {2}".format(pastSma.Time, pastSma.Value, currSma.Value))
        
        

        if not self.Portfolio.Invested and currSma.Value > pastSma.Value:
            self.SetHoldings("SVXY", 1)