# https://seekingalpha.com/article/4299701-leveraged-etfs-for-long-term-investing
# Momentum addaped verision of:
# https://www.quantconnect.com/forum/discussion/7708/using-levered-etfs-in-ira-10-years-24-cagr-1-56-sharpe/p1
# with some extra symbols.

import pandas as pd
import numpy as np

class MultidimensionalTransdimensionalPrism(QCAlgorithm):

    def Initialize(self):
        self.SetStartDate(2020, 7, 7)  # Set Start Date
        self.SetCash(10000)  # Set Strategy Cash
        self.SetEndDate(2020, 7, 16)
        
        # create a dictionary to store momentum indicators for all symbols 
        self.data = {}
        self.rsi = {}
        self.momp = {}
        
        #MOM period
        period = 21*4
        #rsi period
        period2 = 10
        #MOMP period
        period3 = 44
        
        
        self.symbols = ["TQQQ", "UBT", "TECL","UGLD","TYD"]
        self.SetRiskManagement(TrailingStopRiskManagementModel(0.25))
        
        # warm up the MOM indicator
        self.SetWarmUp(period)
        for symbol in self.symbols:
            self.AddEquity(symbol, Resolution.Hour)
            self.data[symbol] = self.MOM(symbol, period, Resolution.Hour)
            
        for symbol in self.symbols:
            self.rsi[symbol] = self.RSI(symbol, period2, Resolution.Daily)
            
        for symbol in self.symbols:
            self.momp[symbol] = self.MOMP(symbol, period3, Resolution.Daily)

        self.Schedule.On(self.DateRules.Every(DayOfWeek.Wednesday), \
                 self.TimeRules.At(15, 30), \
                 self.Rebalance)
                
    def OnData(self, data):
        # check if this algorithm is still warming up
        pass

    def Rebalance(self):
        if self.IsWarmingUp: return
        
        #pd = panda data series for mom (not used)
        #top3 = pd.Series(self.data).sort_values(ascending = False)[:3]
        
        #self.Log(str(top3.index))
        #self.Log(str(pd.Series(self.data)))
        #self.Log(str(self.data))
        alldata = pd.DataFrame({'momp':pd.Series(self.momp),'rsivalue':pd.Series(self.rsi)})
        RSIlessthan80 = alldata.drop(alldata[alldata['rsivalue']<=80].index)
        top3 = RSIlessthan80.sort_values(by='momp', ascending = False)[:3]
        #self.Log(str(alldata))
        
        # is RSI less than 80?
        # RSIlessthan80 is a boolean variable with True or False in it
        # filter rows for year 2002 using  the boolean expression
        # RSIlessthan80 = alldata[alldata.rsi.lt(80)]
        # self.Log(str(RSIlessthan80))
        
        #top3 = pd.Series(self.momp).sort_values(ascending = False)[:3]
        #momptop3 = pd.Series(self.momp).sort_values(ascending = False)[:3]
        
        #self.Log(str(momptop3.index))
        #self.Log(str(pd.Series(self.momp)))
        #self.Log(str(self.momp))
        
        #self.Plot("Stock", "TQQQ", self.Securities["TQQQ"].Price)
        #self.Plot("Stock", "UBT", self.Securities["UBT"].Price)
        #self.Plot("Stock", "TECL", self.Securities["TECL"].Price)
        #self.Plot("Stock", "UGLD", self.Securities["UGLD"].Price)
        #self.Plot("Stock", "TYD", self.Securities["TYD"].Price)
        
        #self.Plot("Momentum", "TQQQ", self.data["TQQQ"].Current.Value)
        #self.Plot("Momentum", "UBT", self.data["UBT"].Current.Value)
        #self.Plot("Momentum", "TECL", self.data["TECL"].Current.Value)
        #self.Plot("Momentum", "UGLD", self.data["UGLD"].Current.Value)
        #self.Plot("Momentum", "TYD", self.data["TYD"].Current.Value)
        
        #self.Plot("RSI", "TQQQ", self.rsi["TQQQ"].Current.Value)
        #self.Plot("RSI", "UBT", self.rsi["UBT"].Current.Value)
        #self.Plot("RSI", "TECL", self.rsi["TECL"].Current.Value)
        #self.Plot("RSI", "UGLD", self.rsi["UGLD"].Current.Value)
        #self.Plot("RSI", "TYD", self.rsi["TYD"].Current.Value)
        
        #self.Log(str(self.rsi))
        
        for kvp in self.Portfolio:
            #self.Log(f"\nkvp: {type(kvp)}\nkvp.Key: {type(kvp.Key)}\nkvp.Value: {type(kvp.Value)}")
            security_hold = kvp.Value
            #self.Log(str(self.rsi.get(security_hold.Symbol.Value) ))
            #self.Log(str(kvp.Value))# Returns QuantConnect.Securities.Equity.EquityHolding
            #self.Log(str(security_hold.Symbol.Value)) #shows all the securities in the symbols libary
            #self.Log(str(security_hold.Invested))   #shows all the securities in the libary 
                                                    #invested or not invested as True or False
            # liquidate the security which is no longer in the top3 momentum list
            
            if security_hold.Invested and (security_hold.Symbol.Value not in top3.index):
                self.Liquidate(security_hold.Symbol)
        
        added_symbols = []        
        for symbol in top3.index:
            if not self.Portfolio[symbol].Invested:
                added_symbols.append(symbol)
        for added in added_symbols:
            self.SetHoldings(added, 1/len(added_symbols))