# MultiIndicator v2 (Py) 

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

from System import *
from QuantConnect import *
from QuantConnect.Data import *
from QuantConnect.Algorithm import *
from QuantConnect.Indicators import *
from datetime import datetime
import decimal as d
import numpy as np


class MultiIndicator(QCAlgorithm):

    def Initialize(self):
        
        # define email address for buy/sell notifications
        # please change prior to Live deploy
        self.email_address = 'test@test.com'

        self.SetStartDate(2017,11,1)   #Set Start Date
        self.SetEndDate(2017,11,2)    #Set End Date
        self.SetCash(3000)           #Set Strategy Cash
        
        # define crypto we want to trade on
        # ETHUSD or LTCUSD or BTCUSD
        self.target_crypto = "ETHUSD"

        # Set brokerage to GDAX for cryptos
        self.SetBrokerageModel(BrokerageName.GDAX, AccountType.Cash)

        # Set crypto and time resolution
        self.AddCrypto(self.target_crypto, Resolution.Minute)
        self.SetBenchmark(self.target_crypto)
        
        # Request warmup data
        self.SetWarmup(120)
        
        # create consolidator for 1 minute
        # even though base resolution is already Minute,
        # manually consolidating at the same resolution
        # ensures pattern can be the same for larger resolution bars.
        consMinute = TradeBarConsolidator(1)
        consMinute.DataConsolidated += self.OnMinuteData
        self.SubscriptionManager.AddConsolidator(self.target_crypto, consMinute)
        
        # create consolidator for 5 minute
        cons15Minute = TradeBarConsolidator(5)
        cons15Minute.DataConsolidated += self.On15MinuteData
        self.SubscriptionManager.AddConsolidator(self.target_crypto, cons15Minute)

        # Define EMAs at minute consolidated resolution
        # Note we are at minute resolution
        self.ema_minute_f = ExponentialMovingAverage("EMA_Min_f", 2)
        self.ema_minute_m = ExponentialMovingAverage("EMA_Min_m", 15)
        self.ema_minute_s = ExponentialMovingAverage("EMA_Min_s", 120)
        
        # Define MOMs at 15 minute consolidated resolution
        # Note we are at 15 minute resoltuion
        self.mom_15minute_f = self.MOM(self.target_crypto, 3)
        
        sPlot = Chart('Strategy Equity')
        sPlot.AddSeries(Series('EMA Crossover',  SeriesType.Line, 2)) #Only for axis title override
        sPlot.AddSeries(Series('EMA 2min',  SeriesType.Line, 2))
        sPlot.AddSeries(Series('EMA 15min',  SeriesType.Line, 2))
        sPlot.AddSeries(Series('EMA 120min',  SeriesType.Line, 2))
        sPlot.AddSeries(Series('Momentum',  SeriesType.Line, 3))
        self.AddChart(sPlot)        
        
        # Initialize variables
        self.holdings = 0
        self.tolerance = .001
        self.price = self.Securities[self.target_crypto].Price
        self.price_last_buy = None
        self.price_max = None
        self.price_delta_buy = None
        self.price_delta_max = None
        self.first = True
        
    def OnMinuteData(self, sender, bar):
        self.ema_minute_f.Update(bar.EndTime, bar.Close)
        self.ema_minute_m.Update(bar.EndTime, bar.Close)
        self.ema_minute_s.Update(bar.EndTime, bar.Close)
        #self.Debug(str(self.Time) + " > New Minute Bar!")
        self.Plot('Strategy Equity', 'EMA 2min', float(str(self.ema_minute_f)))
        self.Plot('Strategy Equity', 'EMA 15min', float(str(self.ema_minute_m)))
        self.Plot('Strategy Equity', 'EMA 120min', float(str(self.ema_minute_s)))

    def On15MinuteData(self, sender, bar):
        self.mom_15minute_f.Update(bar.EndTime, bar.Close)
        #self.Debug(str(self.Time) + " > New Minute Bar!")
        self.Plot('Strategy Equity', 'Momentum', float(str(self.mom_15minute_f)))

    def OnData(self, data):
        if self.first and not self.IsWarmingUp:
            self.first = False
        pass