#https://quantpedia.com/strategies/net-current-asset-value-effect/
from QuantConnect.Data.UniverseSelection import *
import math
import numpy as np
import pandas as pd
import scipy as sp

class NetCurrentAssetValueEffect(QCAlgorithm):

    def Initialize(self):

        self.SetStartDate(2010, 1, 1)   
        self.SetEndDate(datetime.now())          
        self.SetCash(1000000)            
        self.UniverseSettings.Resolution = Resolution.Daily
        self.sorted_by_ncav = None
        self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction)
        self.AddEquity("SPY", Resolution.Daily)
        self.Schedule.On(self.DateRules.MonthStart("SPY"), self.TimeRules.AfterMarketOpen("SPY"), self.rebalance)
        # Count the number of months that have passed since the algorithm starts
        self.months = -1
        self.yearly_rebalance = True
    def CoarseSelectionFunction(self, coarse):
        if self.yearly_rebalance:
            # drop stocks which have no fundamental data or have low price
            self.filtered_coarse = [x.Symbol for x in coarse if (x.HasFundamentalData)]
            return self.filtered_coarse
        else: 
            return []       

    def FineSelectionFunction(self, fine):
        if self.yearly_rebalance:
            # Filter stocks with nonzero Total Assets
            fine = [x for x in fine if (x.FinancialStatements.BalanceSheet.TotalAssets.TwelveMonths != 0)] 
            for i in fine:
                i.MarketCap = float(i.EarningReports.BasicAverageShares.ThreeMonths * (i.EarningReports.BasicEPS.TwelveMonths*i.ValuationRatios.PERatio))
            fine = [x for x in fine if (x.MarketCap != 0)] 
            
            fine = [x for x in fine if ((x.ValuationRatios.WorkingCapitalPerShare*x.EarningReports.BasicAverageShares.Value)/x.MarketCap > 1.5)] 
            self.sorted_by_ncav = [i.Symbol for i in fine] 
            self.Debug(str(len(fine)))
            for i in fine:
                self.Debug(str(i.ValuationRatios.WorkingCapitalPerShare*i.EarningReports.BasicAverageShares.Value/i.MarketCap))

            return self.sorted_by_ncav
        else:
            return []

    def rebalance(self):
        # yearly rebalance
        self.months += 1
        if self.months%12 == 0:
            self.yearly_rebalance = True


    def OnData(self, data):
         if not self.yearly_rebalance: return 
         if self.sorted_by_ncav:
            portfolio_size = int(len(self.sorted_by_ncav))
            stocks_invested = [x.Key for x in self.Portfolio]
            for i in stocks_invested:
                #liquidate the stocks not in the filtered 
                if i not in self.sorted_by_ncav:
                    self.Liquidate(i) 
                #long the stocks in the list
                elif i in self.sorted_by_ncav:
                    self.SetHoldings(i, 1/(portfolio_size))
            self.yearly_rebalance = False