# https://quantpedia.com/strategies/rd-expenditures-and-stock-returns/
#
# The investment universe consists of stocks that are listed on NYSE NASDAQ or AMEX. At the end of April, for each stock in the universe, calculate a measure of total R&D expenditures in the past 5 years scaled by the firm’s Market cap (defined on page 7, eq. 1).
# Go long (short) on the quintile of firms with the highest (lowest) R&D expenditures relative to their Market Cap. Weight the portfolio equally and rebalance next year. The backtested performance of the paper is substituted by our more recent backtest in Quantconnect.
#
# QC implementation changes:
#   - The investment universe consists of 500 most liquid stocks that are listed on NYSE NASDAQ or AMEX.

#region imports
from AlgorithmImports import *
from numpy import log, average
from scipy import stats
import numpy as np
#endregion

class RDExpendituresandStockReturns(QCAlgorithm):

    def Initialize(self) -> None:
        self.SetStartDate(1998, 1, 1)
        self.SetCash(100000)

        self.fundamental_count:int = 500
        self.fundamental_sorting_key = lambda x: x.DollarVolume
        self.rebalance_month:int = 4
        self.quantile:int = 5
        self.leverage:int = 5
        self.min_share_price:float = 5.
        self.exchange_codes:List[str] = ['NYS', 'NAS', 'ASE']
        
        # R&D history.
        self.RD:Dict[Symbol, float] = {}
        self.rd_period:int = 5
        
        self.long:List[Symbol] = []
        self.short:List[Symbol] = []
        
        data:Equity = self.AddEquity('XLK', Resolution.Daily)
        data.SetLeverage(self.leverage)
        self.technology_sector:Symbol = data.Symbol
      
        market:Symbol = self.AddEquity('SPY', Resolution.Daily).Symbol
        
        self.selection_flag:bool = False
        self.UniverseSettings.Resolution = Resolution.Daily
        self.AddUniverse(self.FundamentalSelectionFunction)
        self.Settings.MinimumOrderMarginPortfolioPercentage = 0.
        self.Schedule.On(self.DateRules.MonthEnd(market), self.TimeRules.AfterMarketOpen(market), self.Selection)
   
    def OnSecuritiesChanged(self, changes: SecurityChanges) -> None:
        for security in changes.AddedSecurities:
            security.SetFeeModel(CustomFeeModel())
            security.SetLeverage(self.leverage)
        
    def FundamentalSelectionFunction(self, fundamental: List[Fundamental]) -> List[Symbol]:
        if not self.selection_flag:
            return Universe.Unchanged
        
        selected:List[Fundamental] = [
            x for x in fundamental if x.HasFundamentalData and x.Price > self.min_share_price and x.SecurityReference.ExchangeId in self.exchange_codes and x.MarketCap != 0 and \
            not np.isnan(x.FinancialStatements.IncomeStatement.ResearchAndDevelopment.TwelveMonths) and x.FinancialStatements.IncomeStatement.ResearchAndDevelopment.TwelveMonths != 0
        ]

        if len(selected) > self.fundamental_count:
            selected = [x for x in sorted(selected, key=self.fundamental_sorting_key, reverse=True)[:self.fundamental_count]]

        selected_symbols:List[Symbol] = list(map(lambda x: x.Symbol, selected))
        
        ability:Dict[Fundamental, float] = {}
        updated_flag:List[Symbol] = []  # updated this year already
        
        for stock in selected:
            symbol:Symbol = stock.Symbol
            
            # prevent storing duplicated value for the same stock in one year
            if symbol not in updated_flag:
                # Update RD.
                if symbol not in self.RD:
                    self.RD[symbol] = RollingWindow[float](self.rd_period)
                
                if self.RD[symbol].IsReady:
                    coefs:np.ndarray = np.array([1, 0.8, 0.6, 0.4, 0.2])
                    rds:np.ndarray = np.array([x for x in self.RD[symbol]])
                    
                    rdc:float = sum(coefs * rds)
                    ability[stock] = rdc / stock.MarketCap
                
                rd:float = stock.FinancialStatements.IncomeStatement.ResearchAndDevelopment.TwelveMonths
                self.RD[symbol].Add(rd)
            
            # prevent storing duplicated value for the same stock in one year
            if selected_symbols.count(symbol) > 1:
                updated_flag.append(symbol)
        
        # Remove not updated symbols
        symbols_to_delete:List[Symbol] = []
        for symbol in self.RD.keys():
            if symbol not in selected_symbols:
                symbols_to_delete.append(symbol)    
        for symbol in symbols_to_delete:
            if symbol in self.RD:
                del self.RD[symbol]
        
        # starts trading after data storing period
        if len(ability) >= self.quantile:
            # Ability sorting.
            sorted_by_ability:List = sorted(ability.items(), key = lambda x: x[1], reverse = True)
            quantile:int = int(len(sorted_by_ability) / self.quantile)
            high_by_ability:List[Symbol] = [x[0].Symbol for x in sorted_by_ability[:quantile]]
            low_by_ability:List[Symbol] = [x[0].Symbol for x in sorted_by_ability[-quantile:]]
            
            self.long = high_by_ability
            self.short = low_by_ability
        
        return self.long + self.short
    
    def Selection(self) -> None:
        if self.Time.month == self.rebalance_month:
            self.selection_flag = True
            
    def OnData(self, data: Slice) -> None:
        if not self.selection_flag:
            return
        self.selection_flag = False
        
        # order execution
        targets:List[PortfolioTarget] = []
        for i, portfolio in enumerate([self.long, self.short]):
            for symbol in portfolio:
                if symbol in data and data[symbol]:
                    targets.append(PortfolioTarget(symbol, ((-1) ** i) / len(portfolio)))
        
        self.SetHoldings(targets, True)

        self.long.clear()
        self.short.clear()
        
class CustomFeeModel(FeeModel):
    def GetOrderFee(self, parameters):
        fee = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005
        return OrderFee(CashAmount(fee, "USD"))