from datetime import timedelta
import numpy as np
from scipy import stats
from collections import deque

class ModulatedMultidimensionalReplicator(QCAlgorithm):

    def Initialize(self):
        self.SetStartDate(2019, 6, 1)     
        #self.SetEndDate(2020, 1, 1)        
        self.SetCash(50000)

        tickers = ["QQQ","IYC","IYK","IGV","SMH","GLD","TLH","TLT"]
        self.sym=[]
        for x in tickers:
            self.sym.append(Symbol.Create(x, SecurityType.Equity, Market.USA))
        self.SetUniverseSelection( ManualUniverseSelectionModel(self.sym) )
        self.UniverseSettings.Resolution = Resolution.Daily
        
        self.AddAlpha(MOMAlphaModel())
        
        self.Settings.RebalancePortfolioOnInsightChanges = False          
        self.Settings.RebalancePortfolioOnSecurityChanges = False
        
        self.SetPortfolioConstruction(EqualWeightingPortfolioConstructionModel(self.DateRules.Every(DayOfWeek.Monday)))
        self.SetExecution(ImmediateExecutionModel())    
        
    def OnData(self, data):
        '''OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.
            Arguments:
                data: Slice object keyed by symbol containing the stock data
        '''
        # if not self.Portfolio.Invested:
        #    self.SetHoldings("SPY", 1)
        pass
        

class MOMAlphaModel(AlphaModel): 
    def __init__(self):
        
        self.indi = {} 
        self.securities = [] 
        
    def OnSecuritiesChanged(self, algorithm, changes):
        
        '''
        Description:
            Event fired each time the we add/remove securities from the data feed
        Args:
            algorithm: The algorithm instance that experienced the change in securities
            changes: The security additions and removals from the algorithm
        '''
        
        # add new securities
        for security in changes.AddedSecurities:
            self.securities.append(security)
            
            symbol = security.Symbol
            
            # Call history to get an array of 200 days of history data
            history = algorithm.History(symbol, 100, Resolution.Daily)
            # Adjust SelectionData to pass in the history result
            self.indi[symbol] = SelectionData(history, 100)
            #  RegisterIndicator  
            #algorithm.RegisterIndicator(symbol, self.indi, Resolution.Daily)

        # remove securities
        for security in changes.RemovedSecurities:
            if security in self.securities:
                self.securities.remove(security)
                
    def Update(self, algorithm, data):
        insights = []
        
        # momentum 
        ordered = sorted(self.indi.items(), key=lambda x: x[1].Value, reverse=True)[3]  
        
        for x in ordered:
            symbol = x[0]
            insights.append( Insight.Price(symbol, timedelta(1), InsightDirection.Up) ) 
        
        return insights
        
        
class SelectionData():
    # Update the constructor to accept a history array
    def __init__(self,history, period):
        self.IsReady = False
        self.Value = 0
        self.ex_reg = CustomSlope('CSlope', period)

        #4. Loop over the history data and update the indicators
        for bar in history.itertuples():
            self.ex_reg.Update(bar.Index[1], bar.close)
    
    def is_ready(self):
        return self.ex_reg.IsReady
        
    def Update(self, time, price):
        self.ex_reg.Update(time, price)
        

class CustomSlope:
    def __init__(self, name, period):
        self.Name = name
        self.Time = datetime.min
        self.IsReady = False
        self.Value = 0
        self.Slope = 0
        self.Corr = 0
        self.queue = deque(maxlen=period)
        
    def __repr__(self):
        return "{0} -> IsReady: {1}. Time: {2}. Value: {3}".format(self.Name, self.IsReady, self.Time, self.Value)
        
    def Update(self, time, value):
        self.queue.appendleft(value)
        count = len(self.queue)
        self.Time = time
        
        self.IsReady = count == self.queue.maxlen
        
        #### start here the indicator calulation
        if self.IsReady:    
            y = np.log(self.queue)
            x = [range(len(y))]
            slope, corr = stats.linregress(x, y)[0], stats.linregress(x, y)[2]
            self.Slope = slope 
            self.Corr = corr  
            self.Value = slope * corr 
        #### finish the custom indicator
        
        return self.IsReady