from Alphas.MacdAlphaModel import MacdAlphaModel
from datetime import datetime


class CrawlingFluorescentOrangeBull(QCAlgorithm):

    def Initialize(self):
        self.SetStartDate(2021, 1, 1)  # Set Start Date
        self.SetCash(10000)  # Set Strategy Cash
        self.AddAlpha(MacdAlphaModel(12, 26, 9, MovingAverageType.Simple, Resolution.Daily))
        self.SetUniverseSelection(CoarseFundamentalUniverseSelectionModel(self.CoarseSelectionFunction))
        self.averages = { }
        self.UniverseSettings.Resolution = Resolution.Daily

    def CoarseSelectionFunction(self, universe):
        selected = []
        universe = sorted(universe, key=lambda c: c.DollarVolume, reverse=True)
        universe = [c for c in universe if c.Price > 5][:100]

        for coarse in universe:  
            self.symbol = coarse.Symbol
            
            if self.symbol not in self.averages:
                # 1. Call history to get an array of 200 days of history data
                history = self.History(self.symbol, 200, Resolution.Daily)
                
                #2. Adjust SelectionData to pass in the history result
                self.averages[self.symbol] = SelectionData(history) 

            self.averages[self.symbol].update(self.Time, coarse.AdjustedPrice)
            
            if  self.averages[self.symbol].is_ready() and self.averages[self.symbol].fast > self.averages[self.symbol].slow:
                selected.append(self.symbol)
        
        return selected[:5]
        
    def MACD20(self, coarse):
        self.macd = self.MACD(self.symbol, 12, 26, 9, MovingAverageType.Exponential, Resolution.Daily)
        self.__previous = datetime.min
        self.PlotIndicator("MACD", True, self.__macd, self.__macd.Signal)
        self.PlotIndicator(self.symbol, self.__macd.Fast, self.__macd.Slow)
        
    def Data(self, data):
        if not self.macd.IsReady:
            return
        if self.previous.date() == self.Time.date():
            return
        holdings = self.Portfolio[self.symbol].Quantity
        self.__previous = self.Time
            
class Holdings():
    def buyShares(self, slice):
        if holdings == 0 and self.macd.Current.Value > self.macd.Signal.Value:
            self.Buy(self.symbol, 10)
        else:
            return
    def holdShares(self, slice):
        if holdings > 0 or self.macd.Current.Value > self.macd.Signal.Value:
            return
        if holdings < 0 or self.macd.Current.Value < self.macd.Signal.Value:
            return
        else:
            return
    def sellShares(self, slice):
        if holdings > 0 or self.macd.Current.Value < self.macd.Signal.Value:
            self.Liquidate()
        else:
            return

class SelectionData():
    #3. Update the constructor to accept a history array
    def __init__(self, history):
        self.slow = ExponentialMovingAverage(100)
        self.fast = ExponentialMovingAverage(20)
        #4. Loop over the history data and update the indicators
        for bar in history.itertuples():
            self.fast.Update(bar.Index[1], bar.close)
            self.slow.Update(bar.Index[1], bar.close)
    def is_ready(self):
        return self.slow.IsReady and self.fast.IsReady
        
    def update(self, time, price):
        self.fast.Update(time, price)
        self.slow.Update(time, price)