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

from System import *
from QuantConnect import *
from QuantConnect.Algorithm import *
from QuantConnect.Indicators import *
from datetime import datetime
from Alphas.MacdAlphaModel import MacdAlphaModel
from Execution.ImmediateExecutionModel import ImmediateExecutionModel
from Portfolio.EqualWeightingPortfolioConstructionModel import EqualWeightingPortfolioConstructionModel

class SymbolData:
    def __init__(self, symbol, algorithm):
        self.Symbol = symbol
        self.buypoint, self.sellpoint= None, None
        self.longLiqPoint, self.shortLiqPoint, self.yesterdayclose= None, None, None
        self.numdays = 30
        self.Bolband = algorithm.BB(symbol, self.numdays, 2, MovingAverageType.Simple, Resolution.Daily)
        self.__previous = datetime.min
        
class OptimizedDynamicRegulators(QCAlgorithm):

    def Initialize(self):
        self.SetStartDate(2018, 10, 25)  # Set Start Date
        self.SetCash(100000)  # Set Strategy Cash
        # self.AddEquity("SPY", Resolution.Minute)
        self.AddAlpha(MacdAlphaModel(12, 26, 9, MovingAverageType.Exponential, Resolution.Daily))
        self.numdays = 30
        self.ceiling,self.floor = 60,20
        self.SetBenchmark("SPY")

        self.SetExecution(ImmediateExecutionModel())

        self.SetPortfolioConstruction(EqualWeightingPortfolioConstructionModel())

        self.SetRiskManagement(MaximumSectorExposureRiskManagementModel())

        self.__numberOfSymbols = 100
        self.__numberOfSymbolsFine = 5
        self.SetUniverseSelection(FineFundamentalUniverseSelectionModel(self.CoarseSelectionFunction, self.FineSelectionFunction, None, None))

    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[symbol].Quantity

        signalDeltaPercent = (self.MACD.Current.Value - self.MACD.Signal.Current.Value)/self.MACD.Fast.Current.Value

        # 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(symbol, 1.0)

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


        self.__previous = self.Time

    def SetSignal(self):
        close = self.History(symbol, 31, Resolution.Daily)['close']
        todayvol = np.std(close[1:31])
        yesterdayvol = np.std(close[0:30])
        deltavol = (todayvol - yesterdayvol) / todayvol
        self.numdays = int(round(self.numdays * (1 + deltavol)))

        if self.numdays > self.ceiling:
           self.numdays = self.ceiling
        elif self.numdays < self.floor:
            self.numdays = self.floor
        
        self.high = self.History(symbol, self.numdays, Resolution.Daily)['high']
        self.low = self.History(symbol, self.numdays, Resolution.Daily)['low']      

        self.buypoint = max(self.high)
        self.sellpoint = min(self.low)
        historyclose = self.History(symbol, self.numdays, Resolution.Daily)['close'] 
        self.longLiqPoint = np.mean(historyclose)
        self.shortLiqPoint = np.mean(historyclose)
        self.yesterdayclose = historyclose.iloc[-1]
        
        # wait for our BollingerBand to fully initialize
        if not self.Bolband.IsReady: return

        holdings = self.Portfolio[symbol].Quantity
    
        if self.yesterdayclose > self.Bolband.UpperBand.Current.Value and self.Portfolio[symbol].Price >= self.buypoint:
            self.SetHoldings(symbol, 1)
        elif self.yesterdayclose < self.Bolband.LowerBand.Current.Value and self.Portfolio[symbol].Price <= self.sellpoint:
            self.SetHoldings(symbol, -1)

        if holdings > 0 and self.Portfolio[symbol].Price <= self.shortLiqPoint:
            self.Liquidate(symbol)
        elif holdings < 0 and self.Portfolio[symbol].Price >= self.shortLiqPoint:
            self.Liquidate(symbol)
      
        self.Log(str(self.yesterdayclose)+(" # of days ")+(str(self.numdays)))
        
        
    # sort the data by daily dollar volume and take the top 'NumberOfSymbols'
    def CoarseSelectionFunction(self, coarse):
        # sort descending by daily dollar volume
        sortedByDollarVolume = sorted(coarse, key=lambda x: x.DollarVolume, reverse=True)
    
        # return the symbol objects of the top entries from our sorted collection
        return [ x.Symbol for x in sortedByDollarVolume[:self.__numberOfSymbols] ]
    
    # sort the data by P/E ratio and take the top 'NumberOfSymbolsFine'
    def FineSelectionFunction(self, fine):
        # sort descending by P/E ratio
        sortedByPeRatio = sorted(fine, key=lambda x: x.ValuationRatios.PERatio, reverse=True)
    
        # take the top entries from our sorted collection
        return [ x.Symbol for x in sortedByPeRatio[:self.__numberOfSymbolsFine] ]