Earnings Announcement Volume Concentration Strategy

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

class EarningsAnnouncementPremium(XXX):

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.MarketCap

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

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

self.fundamental_count:int = 3000
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)

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"))