# https://quantpedia.com/strategies/earnings-announcement-premium/
#
# The investment universe consists of all stocks from the CRSP database. At the beginning of every calendar month, stocks are ranked in ascending 
# order on the basis of the volume concentration ratio, which is defined as the volume of the previous 16 announcement months divided by the total
# volume in the previous 48 months. The ranked stocks are assigned to one of 5 quintile portfolios. Within each quintile, stocks are assigned to
# one of two portfolios (expected announcers and expected non-announcers) using the predicted announcement based on the previous year. All stocks
# are value-weighted within a given portfolio, and portfolios are rebalanced every calendar month to maintain value weights. The investor invests
# in a long-short portfolio, which is a zero-cost portfolio that holds the portfolio of high volume expected announcers and sells short the 
# portfolio of high volume expected non-announcers.
#
# QC implementation changes:
#   - The investment universe consists of 1000 most liquid stocks traded on NYSE, AMEX, or NASDAQ.

from collections import deque
from AlgorithmImports import *
from typing import List, Dict, Tuple

class EarningsAnnouncementPremium(QCAlgorithm):

    def Initialize(self):
        self.SetStartDate(2000, 1, 1)
        self.SetCash(100000) 

        self.symbol:Symbol = self.AddEquity('SPY', Resolution.Daily).Symbol
        
        self.period:int = 21
        self.month_period:int = 48
        self.leverage:int = 10
        self.quantile:int = 5
        self.selection_sorting_key = lambda x: x.dollar_volume

        # Volume daily data.
        self.data:Dict[Symbol, RollingWindow[float]] = {}

        # Volume monthly data.
        self.monthly_volume:Dict[Symbol, float] = {}

        self.fundamental_count:int = 1000
        self.weight:Dict[Symbol, float] = {}
        
        self.selection_flag:bool = True
        self.Settings.MinimumOrderMarginPortfolioPercentage = 0.
        self.UniverseSettings.Resolution = Resolution.Daily
        self.AddUniverse(self.FundamentalSelectionFunction)
        self.Schedule.On(self.DateRules.MonthStart(self.symbol), self.TimeRules.AfterMarketOpen(self.symbol), self.Selection)

        self.settings.daily_precise_end_time = False

    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]:
        # Update the rolling window every day.
        for stock in fundamental:
            symbol:Symbol = stock.Symbol

            # Store monthly price.
            if symbol in self.data:
                self.data[symbol].Add(stock.Volume)
            
        if not self.selection_flag:
            return Universe.Unchanged
        
        selected:List[Fundamental] = [x for x in fundamental if x.HasFundamentalData and x.Market == 'usa' and x.MarketCap != 0 and \
                    ((x.SecurityReference.ExchangeId == "NYS") or (x.SecurityReference.ExchangeId == "NAS") or (x.SecurityReference.ExchangeId == "ASE"))]
        
        if len(selected) > self.fundamental_count:
            selected = [x
                for x in sorted([x for x in selected], key = self.selection_sorting_key, reverse = True)[:self.fundamental_count]]
                
        fine_symbols:List[Symbol] = [x.Symbol for x in selected]

        volume_concentration_ratio:Dict[Fundamental, float] = {}

        # Warmup volume rolling windows.
        for stock in selected:
            symbol:Symbol = stock.Symbol
            # Warmup data.
            if symbol not in self.data:
                self.data[symbol] = RollingWindow[float](self.period)
                history:DataFrame = self.History(symbol, self.period, Resolution.Daily)
                if history.empty:
                    self.Debug(f"No history for {symbol} yet")
                    continue
                if 'volume' not in history.columns:
                    continue
                volumes:Series = history.loc[symbol].volume
                for _, volume in volumes.items():
                    self.data[symbol].Add(volume)
            
            # Ratio/market cap pair.
            if not self.data[symbol].IsReady:
                continue

            if symbol not in self.monthly_volume:
                self.monthly_volume[symbol] = deque(maxlen = self.month_period)

            monthly_vol:float = sum([x for x in self.data[symbol]])
            last_month_date:datetime = self.Time - timedelta(days = self.Time.day)
            last_file_date:datetime = stock.EarningReports.FileDate.Value # stock annoucement day
            was_announcement_month:Tuple[int] = (last_file_date.year == last_month_date.year and last_file_date.month == last_month_date.month)    # Last month was announcement date.
            self.monthly_volume[symbol].append(VolumeData(last_month_date, monthly_vol, was_announcement_month))
            
            # 48 months of volume data is ready.
            if len(self.monthly_volume[symbol]) == self.monthly_volume[symbol].maxlen:
                # Volume concentration ratio calc.
                announcement_count:int = 12
                announcement_volumes:List[float] = [x.Volume for x in self.monthly_volume[symbol] if x.WasAnnouncementMonth][-announcement_count:]

                if len(announcement_volumes) == announcement_count:
                    announcement_months_volume:float = sum(announcement_volumes)
                    total_volume:float = sum([x.Volume for x in self.monthly_volume[symbol]])
                    
                    if announcement_months_volume != 0 and total_volume != 0:
                        # Store ratio, market cap pair.
                        volume_concentration_ratio[stock] = announcement_months_volume / total_volume
        
        # Volume sorting.
        if len(volume_concentration_ratio) > self.quantile:
            sorted_by_volume:List[Tuple[Fundamental, float]] = sorted(volume_concentration_ratio.items(), key = lambda x: x[1], reverse=True)
            quintile:int = int(len(sorted_by_volume) / self.quantile)
            high_volume:List[Fundamental] = [x[0] for x in sorted_by_volume[:quintile]]
                
            # Filering announcers and non-announcers.
            month_to_lookup:int = self.Time.month
            year_to_lookup:int = self.Time.year - 1
                
            long:List[Fundamental] = []
            short:List[Fundamental] = []
            for stock in high_volume:
                symbol:Symbol = stock.Symbol
                
                announcement_dates:List[List[int]] = [[x.Date.year, x.Date.month] for x in self.monthly_volume[symbol] if x.WasAnnouncementMonth]
                if [year_to_lookup, month_to_lookup] in announcement_dates:
                    long.append(stock)
                else:
                    short.append(stock)

            # Delete not updated symbols.
            symbols_to_remove:List[Symbol] = []
            for symbol in self.monthly_volume:
                if symbol not in fine_symbols:
                    symbols_to_remove.append(symbol)
            for symbol in symbols_to_remove:
                del self.monthly_volume[symbol]
                
            # Market cap weighting.
            for i, portfolio in enumerate([long, short]):
                mc_sum:float = sum(list(map(lambda stock: stock.MarketCap , portfolio)))
                for stock in portfolio:
                    self.weight[stock.Symbol] = (((-1)**i) * stock.MarketCap / mc_sum)

        return list(self.weight.keys())
    
    def OnData(self, data: Slice) -> None:
        if not self.selection_flag:
            return
        self.selection_flag = False
        
        # Trade execution.
        portfolio:List[PortfolioTarget] = [PortfolioTarget(symbol, w) for symbol, w in self.weight.items() if symbol in data and data[symbol]]
        self.SetHoldings(portfolio, True)

        self.weight.clear()
        
    def Selection(self) -> None:
        self.selection_flag = True

# Monthly volume data.
class VolumeData():
    def __init__(self, date, monthly_volume, was_announcement_month):
        self.Date = date
        self.Volume = monthly_volume
        self.WasAnnouncementMonth = was_announcement_month
    
# Custom fee model
class CustomFeeModel(FeeModel):
    def GetOrderFee(self, parameters):
        fee = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005
        return OrderFee(CashAmount(fee, "USD"))