#region imports
from AlgorithmImports import *
#endregion


# Your New Python File
#you have access to fundamental data in alphamodel

#quareterly check added in both files due to separation of concerns methodology
class MyAlphaModel(AlphaModel):
    def __init__(self):
        self.averages = { }
        #reshuffle quarterly
        self.rebalanceTime = datetime.min
        
    #every time there is new info for alpha model which creates insights which it passes to portfolio contruction model
    #you have access to fundamental data in alphamodel
    
    def Update(self,algorithm,slice):
        '''Updates this alpha model with the latest data from the algorithm.
        This is called each time the algorithm receives data for subscribed securities
        Args:
            algorithm: The algorithm instance
            data: The new data available
        Returns:
            New insights'''
        #Expiry.EndOfQuarter(algorithm.Time)
        #self.Debug(str(self.Time)+' On data called')
        #here by Bars, u get only when there is price data for security, 
        # cause OnData is called for dividend etc. types things also
        insights = []
        keys = slice.Bars.Keys
        for key in keys:
            if key in self.averages:
                stock_price = slice[key].Price
                volume=slice[key].Volume
                time=slice[key].EndTime
                asset = self.averages[key]
                # warm up stock's indicators if not ready
                if not asset.areIndsReady():
                    history = algorithm.History(asset.symbol, 253)
                    for index, row in history.loc[asset.symbol].iterrows():
                        asset.updateInds(index, row["close"])
                        tradeBar = TradeBar(index, asset.symbol, row.open, row.high, row.low, row.close, row.volume, timedelta(1))
                        asset.updateBarInds(tradeBar)
                        asset.updateVolInds(index, row.volume)
                        asset.todayVolume=row.volume
                    # now register indicators for automatic daily updates
                    asset.registerIndsForUpdates(algorithm)
                    vcpHistory = algorithm.History(asset.symbol, 50)
                    for index, row in vcpHistory.loc[asset.symbol].iterrows():
                        asset.checkVCP(row["close"],index)

                asset.updateForSelection(time, slice[key],algorithm)
                # self.Plot("Debug",'P1', asset.pivot1)
                # self.Plot("Debug",'Price', stock_price)
                # self.Plot("Debug",'P2', asset.pivot2)
                # self.Plot("Debug",'ema200', asset.slow.Current.Value)

        # check if portfolio is full, otherwise invest 
        if algorithm.Time < self.rebalanceTime:
            # if not invested, then only try to invest, otherwise let portfolio run till monnth change
            # for x in algorithm.Portfolio:
            #     algorithm.Debug(x)
            # capacity=10-len([x.Key for x in algorithm.Portfolio if x.Value.Invested])
            # if capacity>0:
            # Filter the values of the dict: we only want up-trending securities from ones which has indicator values
            values = list(filter(lambda x: x.is_uptrend and x.vcpFormed and x.todayVolume>x.avgVolume.Current.Value*3, self.averages.values()))
            # Sorts the values of the dict: we want those with greater difference between the moving averages
            candidates=sorted(values,key=lambda x: x.scale, reverse=True)[:10]
            for candidate in candidates:
                #if candidate not in porfolio already, BUT SHUD THIS BE IN PORTFOLIO CONSTRUCTION?
                if not algorithm.Portfolio[candidate.symbol].Invested:
                    #algorithm.SetHoldings(candidate.symbol, 0.1)
                    #Insight.Price(candidate.symbol, self.rebalanceTime, InsightDirection.Up, None, None, None, 0.1)
                    insights.append(Insight.Price(candidate.symbol, self.rebalanceTime, InsightDirection.Up, None, None, None, 0.1)) 
        #month changed, liquidate everything and reenter in top 10
        else:
            self.rebalanceTime = Expiry.EndOfQuarter(algorithm.Time)
            # month changed, liquidate portfolio
            #algorithm.Liquidate() #dint use it as alpha Insight has expiry added
        #you can create sort of weighted score of securities by index of it in sorted eps.revenue,margin growth arrays
        #like 4+2+1=7
        return insights

    def OnSecuritiesChanged(self, algorithm, changes):
        algorithm.Debug('On security changed called')
        # liquidate removed securities
        for security in changes.RemovedSecurities:
            if security.Symbol in self.averages:
                self.averages.pop(security.Symbol)

        # we want 20% allocation in each security in our universe
        for security in changes.AddedSecurities:
            self.averages[security.Symbol] = SymbolData(security.Symbol)
            #self.SetHoldings(security.Symbol, 0.1)

class SymbolData(object):
    def __init__(self, symbol):
        self.symbol = symbol
        self.todayVolume = 0.1
        self.tolerance = 1.01
        self.fast = ExponentialMovingAverage(50)
        self.avgVolume = SimpleMovingAverage(20)
        self.slow = ExponentialMovingAverage(200)
        self.fiftyTwoHigh = Maximum(253)
        self.fiftyTwoLow = Minimum(253)
        self.downFromHigh=1
        self.upFromLow=0
        self.is_uptrend = False
        self.scale = 0
        self.secondaryRally = 0.0
        self.naturalRally = 0.0
        self.pivot1=0.0 #high prie pivot
        self.pivot2=0.0 #low price pivot
        self.upTrend = 0.0
        self.naturalReaction = 0.0
        self.secondaryReaction = 0.0
        self.trend="up"
        self.colState="up"
        self.downTrend=0.0
        self.length = 14
        self.molt = 1
        self.slow = ExponentialMovingAverage(200)
        self.adrPerc = NormalizedAverageTrueRange(self.length)
        self.vcpFormed=False
            
    def areIndsReady(self):
        indsReady=True
        indsReady=self.fiftyTwoHigh.IsReady and indsReady
        indsReady=self.fiftyTwoLow.IsReady and indsReady 
        indsReady=self.fast.IsReady and indsReady 
        indsReady=self.slow.IsReady and indsReady
        indsReady=self.avgVolume.IsReady and indsReady
        return indsReady

    def updateForSelection(self, time, currentSlice,context):
        stockPrice=currentSlice.Price
        #update volume
        self.todayVolume=currentSlice.Volume
        if self.areIndsReady():
            fast = self.fast.Current.Value
            slow = self.slow.Current.Value
            self.downFromHigh=1-(stockPrice/self.fiftyTwoHigh.Current.Value)
            self.upFromLow=(stockPrice/self.fiftyTwoLow.Current.Value)-1
            if(self.symbol.Value == 'DDD'):
                context.Debug('here')
            #keep updating pivots daily as even when down 200ma, u need the pivots later man
            self.checkVCP(stockPrice,time)
            if stockPrice>slow:
                self.is_uptrend = stockPrice>slow and stockPrice>fast and fast > slow * self.tolerance and self.upFromLow>=0.8 and self.downFromHigh<=0.2

        if  self.is_uptrend:
            #self.checkVCP(stockPrice) # if in uptrend and vcp then only enter
            self.scale = (fast - slow) / ((fast + slow) / 2.0)
    
    def updateInds(self, time, value):
        self.fiftyTwoHigh.Update(time, value)
        self.fiftyTwoLow.Update(time, value)
        self.fast.Update(time, value)
        self.slow.Update(time, value)
    
    def updateBarInds(self,barData):
        self.adrPerc.Update(barData)

    def updateVolInds(self,time,volume):
        self.avgVolume.Update(time,volume)

    def registerIndsForUpdates(self,context):
        context.RegisterIndicator(self.symbol,self.fiftyTwoHigh,Resolution.Daily)
        context.RegisterIndicator(self.symbol,self.fiftyTwoLow,Resolution.Daily)
        context.RegisterIndicator(self.symbol,self.fast,Resolution.Daily)
        context.RegisterIndicator(self.symbol,self.slow,Resolution.Daily)
        context.RegisterIndicator(self.symbol,self.adrPerc,Resolution.Daily)
        context.RegisterIndicator(self.symbol,self.avgVolume,Resolution.Daily,Field.Volume)

    def checkVCP(self,close,time):
        # self.vcpFormed=True
        # return True
        # make default as false
        self.vcpFormed=False
        if(self.trend=="down"): # add your logic here for downtrend noting
            if(self.pivot1==0.0): #no upward pivot set yet
                self.upTrend= close+close*0.05 #stoploss/up trend marker
                self.pivot1=self.upTrend
                self.vcpFormed=False #in pivot setup make vcp false
            if (close>self.upTrend): #rose above downtrend so downtrend over, mark upTrend instead of natural reaction
                self.upTrend=close
                self.trend="up"
                self.colState="up"
                #strategy.entry("startBuy",strategy.long) //*** entered long here in scrip
                self.vcpFormed=True #as we want to go long on stock which broke above downtrend on rising 200
            else:
                if(close>=(self.pivot1+self.pivot1*0.02)): #broke above natural rally pivot, mark uptrend, enter again
                    self.trend="up" # change trend
                    self.colState="up" #change note down column
                    self.upTrend=close #note down price as upTrend
                    #strategy.entry("startBuy",strategy.long, when=strategy.position_size <= 0) //start buying *** entered long here in scrip
                    self.vcpFormed=True #as we want to go long on stock which broke above pivot 1. this u can remove later if u want to enter on 3-C types formation
            if(close<self.downTrend): #price less than downTrend IS ALWAYS DOWNTREND
                if(self.trend=="down"): #more downtrend
                    self.downTrend=close
                    self.vcpFormed=False #downtrend continues
                if(self.colState=="naturalRally"): #direct downtrend from natural rally instead of natural reaction
                    self.downTrend=close
                    self.pivot1=self.naturalRally
                    self.vcpFormed=False #direct downtrend from natural rally, 
                    #so vol expanded and dont enter
                self.pivot2=self.downTrend
                self.colState="down"
            else:
                if(self.colState=="down"): # if noting in downtrend col, price is more than downtrend, check for SIGNIFICANT rally
                    if(((close-self.downTrend)/self.downTrend)>=(self.adrPerc.Current.Value/100)): # first significant rally from downtrend, mark downtrend as lower pivot(pivot2), note colState as naturalRally
                        self.pivot2=self.downTrend
                        self.naturalRally=close
                        self.colState="naturalRally"
                        self.vcpFormed=False #stock going through natural price cycle still
            if(self.colState=="naturalRally"):
                if(close>self.naturalRally):
                    self.naturalRally=close #keep updating natural rally with higher values
                    self.vcpFormed=False #stock going through natural price cycle still
                else:  # close less than natural rally
                    if(((self.naturalRally-close)/self.naturalRally)>=(self.adrPerc.Current.Value/100)): # first significant reaction from natural rally, mark natural rally as higher pivot(pivot1), note colState as naturalReaction
                        self.colState="naturalReaction"
                        self.pivot1=self.naturalRally
                        self.vcpFormed=False #stock going through natural price cycle still
        #self.vcpFormed=True
        if(self.trend=="up"):
            if(self.pivot2 == 0.0): #no downward pivot set yet
                self.downTrend= self.slow.Current.Value-self.slow.Current.Value*0.05 #stoploss
                self.pivot2=self.downTrend
                self.vcpFormed=False #in pivot setup make vcp false
            if(close>self.slow.Current.Value and self.upTrend == 0): #if uptrend is not initialized first time 
                self.trend="up"
                self.upTrend=close
                self.pivot1=self.upTrend
                self.colState="up"
                self.vcpFormed=False #in pivot setup make vcp false
            if (close<self.downTrend): #uptrend over, exit strategy, mark downTrend instead of natural reaction
                self.downTrend=close
                self.trend="down"
                self.colState="down"
                self.vcpFormed=False #broke pivot so vcp false #*****in script closed strategy here****
            if(close<=(self.pivot2-self.pivot2*0.02)): # broke natural reaction, mark downtrend
                self.trend="down"
                self.downTrend=close
                self.colState="down"
                self.vcpFormed=False #broke pivot so vcp false #*****in script closed strategy here****
            if(close>self.upTrend):
                if(self.trend=="up" and self.upTrend != 0):#uptrend is set and prices rising
                    self.upTrend=close
                    self.vcpFormed=False #in constant uptrending and no vcp formed yet
                if(self.colState=="naturalReaction"): #direct uptrend from natural reaction instead of natural rally
                    self.upTrend=close
                    self.pivot2=self.naturalReaction
                    self.vcpFormed=True #direct uptrend from natural reaction, 
                    #so broke pivot and eligible for entry

                self.pivot1=self.upTrend #???????READ THIS????? here u assigning pivot when prices rising, shudnt u be assigning it when first reaction from uptrend?
                if(self.colState=="naturalRally"): #you added it later, broke above natural rally, means from reaction came to natural rally and now uptrend
                    self.vcpFormed=True
                self.colState="up"
            if(close<self.upTrend and self.trend=="up"): # only when in uptrend
                if(self.colState == "up" and ((self.upTrend-close)/(close))>=(self.adrPerc.Current.Value/100)): #first reaction from uptrend 
                    self.naturalReaction=close
                    self.colState="naturalReaction" #enter into natural reaction colState
                    self.vcpFormed=False #in normal price behavior, wait for vcp now 
                if(close<self.naturalReaction and self.colState=="naturalReaction"):
                    self.naturalReaction=close #keep updating natural reaction with lower values
                    self.vcpFormed=False #in normal price behavior, wait for vcp now
                if(self.colState=="naturalReaction" and ((close-self.naturalReaction)/(self.naturalReaction))>=(self.adrPerc.Current.Value/100)): #natural rally happened, mark natural reaction as pivot 2
                    self.colState="naturalRally"
                    self.naturalRally=close
                    self.pivot2=self.naturalReaction
                    self.vcpFormed=False #natural rally is normal price behavior, wait for vcp now

                # if(self.colState=="naturalRally" and false): #if were noting in natural rally,
                #     if(((abs(upTrend-high))/(upTrend))<=0.01) // stock high was just short of uptrend,
                #         if(((high-close)/(close))>=((adrPerc-1)/2))//but stock reacts heavily from uptrend point, danger signal, sell
                #             strategy.close("startBuy",when=time_cond) //exit here    

#region imports
from AlgorithmImports import *
#endregion
# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from datetime import datetime
from System.Collections.Generic import List
#from AlphaModel import *
from MyAlpha import *
### <summary>
### In this algorithm we demonstrate how to perform some technical analysis as
### part of your coarse fundamental universe selection
### </summary>
### <meta name="tag" content="using data" />
### <meta name="tag" content="indicators" />
### <meta name="tag" content="universes" />
### <meta name="tag" content="coarse universes" />
class EmaCrossUniverseSelectionAlgorithm(QCAlgorithm):

    def Initialize(self):
        '''Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.'''

        self.SetStartDate(2017,1,1)  #Set Start Date
        self.SetEndDate(2022,7,4)    #Set End Date
        self.SetCash(100000)           #Set Strategy Cash
        self.UniverseSettings.Resolution = Resolution.Daily
        self.UniverseSettings.Leverage = 2
        self.month = 0
        self.coarse_count = 1
        #self.universeSelected=False
        
        #reshuffle quarterly
        self.rebalanceTime = self.Time
        
        # this add universe method accepts two parameters:
        # - coarse selection function: accepts an IEnumerable<CoarseFundamental> and returns an IEnumerable<Symbol>
        self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction)
        self.AddAlpha(MyAlphaModel())
        #use below if you dont want portfolio construction to rebalance portfolio when universe is changed and security is removed
        self.Settings.RebalancePortfolioOnSecurityChanges = False
        self.Settings.RebalancePortfolioOnInsightChanges = False
        self.SetPortfolioConstruction(MyCustomWeightingPortfolioConstructionModel(self.shallRebalance))
        #self.SetPortfolioConstruction(NullPortfolioConstructionModel()) 
        #performing middle
        #self.SetRiskManagement(NullRiskManagementModel())
        #performing best
        #self.AddRiskManagement(MaximumDrawdownPercentPerSecurity(0.08))
        #performing worst
        #self.AddRiskManagement(TrailingStopRiskManagementModel(0.15))
        self.SetExecution(ImmediateExecutionModel())

        #liquidate portfolio on end date
        self.Schedule.On(self.DateRules.On(self.EndDate.year, self.EndDate.month, self.EndDate.day),  
                         self.TimeRules.At(0, 0),  
                         self.LiquidateAll)
    def LiquidateAll(self):
        self.Liquidate()
        self.Debug("liquidated on the last day")
    def OnData(self,slice):
        #if end date tomorrow, liquidate everything
        # if(self.Time.date()==((self.EndDate - timedelta(days = 1)).date())):
        #     self.Liquidate()

        if self.Time < self.rebalanceTime:
            return
        else:
            #till found better place, liquidated from here
            self.Liquidate()
            self.rebalanceTime = Expiry.EndOfQuarter(self.Time)

    # pass to fine selection function for market cap
    def CoarseSelectionFunction(self, coarse):
        #self.Debug('coarse universe selection called')
        #return portfolio unchanged if not rebalanceTime
        if self.Time < self.rebalanceTime:
            return Universe.Unchanged    

        filteredByVolume = list(filter(lambda x: x.Volume >= 500000 and x.HasFundamentalData, coarse)) #and x.Symbol.Value=='WNC'
        return [ x.Symbol for x in filteredByVolume ]
        # liquidate and rerank everything to see 
        # if each month actual orders are being placed
        # self.Liquidate()
        # Filter the values of the dict: we only want up-trending securities from ones which has indicator values
        
    # Filter for market cap less than large caps
    def FineSelectionFunction(self, fine):
        self.Debug('fine universe selection called: ')
        #return portfolio unchanged if not rebalanceTime 
        if self.Time < self.rebalanceTime:
            return Universe.Unchanged

        filteredByMarketCap = list(filter(lambda x: 300000000 < x.MarketCap < 2000000000 , fine))
        
        #take top n stop for analysis
        sortedByMarketCap = sorted(filteredByMarketCap, key=lambda x: x.MarketCap, reverse=True)[:150]
       
        # will keep the indicators for all mid caps
        self.Debug(str(self.Time)+' Market cap length'+ str(len(sortedByMarketCap)))  
        
        #self.universeSelected = True
        return [ x.Symbol for x in sortedByMarketCap ]

        # Share the same rebalance function for Universe and PCM for clarity
    
    def shallRebalance(self, time):
        self.Debug(str(time)+'*********Rebalncing called*********')
        if time.month == self.month or time.month not in [1, 4, 7, 10]:
            return None
            
        self.month = time.month
        return time

class MyCustomWeightingPortfolioConstructionModel(PortfolioConstructionModel):
    def __init__(self,rebalance=None):
        #super().__init__(rebalance) #this function is for rebalancing
        #tried calling but not workinh
        # if rebalance is not None:
        #     self.SetRebalancingFunc(rebalance)
        self.month=0

    def CreateTargets(self, algorithm, insights):
        self.percent=0.1
        targets=[]    
        capacity=10-len([x.Key for x in algorithm.Portfolio if x.Value.Invested])
        #added check here to prevent unnecessary loops
        if capacity>0:
            for insight in insights:
                #added check here to prevent more addition when current target exhaust capacity
                if capacity>0:
                    #****IMP this does not work with normal cash/leverage=1 account. only works in margin account. dont know why!***
                    targets.append(PortfolioTarget.Percent(algorithm,insight.Symbol, 0.1))
                    capacity=capacity-1
        return targets

    # Determines if the portfolio should be rebalanced base on the provided rebalancing func
    # def IsRebalanceDue(self, insights, algorithmUtc):
    #     if self.rebalance is not None:
    #         return self.rebalance(algorithmUtc)
    #     return True
    
    def OnSecuritiesChanged(self, algorithm, changes):
        pass

# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from AlgorithmImports import *
from System.Collections.Generic import List

### <summary>
### In this algorithm we demonstrate how to perform some technical analysis as
### part of your coarse fundamental universe selection
### </summary>
### <meta name="tag" content="using data" />
### <meta name="tag" content="indicators" />
### <meta name="tag" content="universes" />
### <meta name="tag" content="coarse universes" />
class EmaCrossUniverseSelectionAlgorithm(QCAlgorithm):

    def Initialize(self):
        '''Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.'''

        self.SetStartDate(2015,1,1)  #Set Start Date
        self.SetEndDate(2019,1,1)    #Set End Date
        self.SetCash(100000)           #Set Strategy Cash

        self.UniverseSettings.Resolution = Resolution.Daily
        self.UniverseSettings.Leverage = 2

        self.coarse_count = 10
        self.averages = { }

        # this add universe method accepts two parameters:
        # - coarse selection function: accepts an IEnumerable<CoarseFundamental> and returns an IEnumerable<Symbol>
        self.AddUniverse(self.CoarseSelectionFunction)


    # sort the data by daily dollar volume and take the top 'NumberOfSymbols'
    def CoarseSelectionFunction(self, coarse):

        # We are going to use a dictionary to refer the object that will keep the moving averages
        for cf in coarse:
            if cf.Symbol not in self.averages:
                self.averages[cf.Symbol] = SymbolData(cf.Symbol)

            # Updates the SymbolData object with current EOD price
            avg = self.averages[cf.Symbol]
            avg.update(cf.EndTime, cf.AdjustedPrice)

        # Filter the values of the dict: we only want up-trending securities
        values = list(filter(lambda x: x.is_uptrend, self.averages.values()))

        # Sorts the values of the dict: we want those with greater difference between the moving averages
        values.sort(key=lambda x: x.scale, reverse=True)

        for x in values[:self.coarse_count]:
            self.Log('symbol: ' + str(x.symbol.Value) + '  scale: ' + str(x.scale))

        # we need to return only the symbol objects
        return [ x.symbol for x in values[:self.coarse_count] ]

    # this event fires whenever we have changes to our universe
    def OnSecuritiesChanged(self, changes):
        # liquidate removed securities
        for security in changes.RemovedSecurities:
            if security.Invested:
                self.Liquidate(security.Symbol)

        # we want 20% allocation in each security in our universe
        for security in changes.AddedSecurities:
            self.SetHoldings(security.Symbol, 0.1)


class SymbolData(object):
    def __init__(self, symbol):
        self.symbol = symbol
        self.tolerance = 1.01
        self.fast = ExponentialMovingAverage(50)
        self.slow = ExponentialMovingAverage(200)
        self.is_uptrend = False
        self.scale = 0

    def update(self, time, value):
        if self.fast.Update(time, value) and self.slow.Update(time, value):
            fast = self.fast.Current.Value
            slow = self.slow.Current.Value
            self.is_uptrend = fast > slow * self.tolerance

        if self.is_uptrend:
            self.scale = (fast - slow) / ((fast + slow) / 2.0)