# https://quantpedia.com/strategies/momentum-factor-combined-with-asset-growth-effect/
#
# The investment universe consists of NYSE, AMEX and NASDAQ stocks (data for the backtest in the source paper are from Compustat). 
# Stocks with a market capitalization less than the 20th NYSE percentile (smallest stocks) are removed. The asset growth variable 
# is defined as the yearly percentage change in balance sheet total assets. Data from year t-2 to t-1 are used to calculate asset
# growth, and July is the cut-off month. Every month, stocks are then sorted into deciles based on asset growth and only stocks 
# with the highest asset growth are used. The next step is to sort stocks from the highest asset growth decile into quintiles, 
# based on their past 11-month return (with the last month’s performance skipped in the calculation). The investor then goes long
# on stocks with the strongest momentum and short on stocks with the weakest momentum. The portfolio is equally weighted and is
# rebalanced monthly. The investor holds long-short portfolios only during February-December -> January is excluded as this month
# has been repeatedly documented as a negative month for a momentum strategy (see “January Effect Filter and Momentum in Stocks”).
#
# QC implementation changes:
#   - Universe consists of 1000 largest stocks traded on NYSE, AMEX, or NASDAQ.

from AlgorithmImports import *
import numpy as np
from pandas.core.frame import DataFrame
from pandas.core.series import Series

class MomentumFactorAssetGrowthEffect(QCAlgorithm):

    def Initialize(self) -> None:
        self.SetStartDate(2000, 1, 1)
        self.SetCash(100_000)

        self.UniverseSettings.Leverage = 5
        self.UniverseSettings.Resolution = Resolution.Daily
        self.AddUniverse(self.FundamentalSelectionFunction)
        self.Settings.MinimumOrderMarginPortfolioPercentage = 0.0
        self.settings.daily_precise_end_time = False

        self.exchange_codes: list[str] = ['NYS', 'NAS', 'ASE']
        self.fundamental_count: int = 1_000
        self.fundamental_sorting_key = lambda x: x.MarketCap
        self.months_in_year: int = 12
        self.days_in_month: int = 21
        self.total_assets_history_period: int = 2
        self.decile: int = 10
        self.quintile: int = 5
        self.excluded_month: int = 1
        
        # Monthly close prices and total assets
        self.symbol_data: dict[Symbol, SymbolData] = {}
        self.long_symbols: dict[Symbol] = []
        self.short_symbols: dict[Symbol] = []
        self.selection_flag: bool = False

        market: Symbol = self.AddEquity('SPY', Resolution.Daily).Symbol        
        self.Schedule.On(self.DateRules.MonthStart(market), 
                        self.TimeRules.AfterMarketOpen(market), 
                        self.Selection)

    def FundamentalSelectionFunction(self, fundamental: List[Fundamental]) -> List[Symbol]:
        if not self.selection_flag:
            return Universe.Unchanged

        # Update the rolling window every month.
        for security in fundamental:
            if security.Symbol in self.symbol_data:
                self.symbol_data[security.Symbol].update_price(security.AdjustedPrice)

        filtered: list[Fundamental] = [f for f in fundamental if f.HasFundamentalData
                                        and f.SecurityReference.ExchangeId in self.exchange_codes
                                        and not np.isnan(f.FinancialStatements.BalanceSheet.TotalAssets.TwelveMonths)
                                        and f.FinancialStatements.BalanceSheet.TotalAssets.TwelveMonths > 0]

        sorted_filter: List[Fundamental] = sorted(filtered, 
                                                key=self.fundamental_sorting_key, 
                                                reverse=True)[:self.fundamental_count]

        # Warmup price rolling windows.
        for f in sorted_filter:
            if f.Symbol in self.symbol_data:
                continue
            
            self.symbol_data[f.Symbol] = SymbolData(f.Symbol, self.months_in_year, self.total_assets_history_period)
            history: DataFrame = self.History(f.Symbol, self.months_in_year * self.days_in_month, Resolution.Daily)
            if history.empty:
                self.Log(f"Not enough data for {f.Symbol} yet.")
                continue
            closes: Series = history.loc[f.Symbol].close
            
            # Find monthly closes.
            for index, time_close in enumerate(closes.items()):
                # index out of bounds check.
                if index + 1 < len(closes.keys()):
                    date_month = time_close[0].date().month
                    next_date_month = closes.keys()[index + 1].month
                
                    # Find last day of month.
                    if date_month != next_date_month:
                        self.symbol_data[f.Symbol].update_price(time_close[1])
            
        ready_securities: list[Fundamental] = [x for x in sorted_filter if self.symbol_data[x.Symbol].price_is_ready()]

        # Asset growth calc.
        asset_growth: dict[Symbol, float] = {}
        for security in ready_securities:
            if self.symbol_data[security.Symbol].asset_data_is_ready():
                asset_growth[security.Symbol] = self.symbol_data[security.Symbol].asset_growth()
                
            self.symbol_data[security.Symbol].update_assets(security.FinancialStatements.BalanceSheet.TotalAssets.TwelveMonths)
        
        sorted_by_growth: list[tuple[Symbol, float]] = sorted(asset_growth.items(), key=lambda x: x[1], reverse=True)
        decile: int = int(len(sorted_by_growth) / self.decile)
        top_by_growth: list[Symbol] = [x[0] for x in sorted_by_growth][:decile]
        
        performance: dict[Symbol, float] = {x: self.symbol_data[x].performance() for x in top_by_growth}
        sorted_by_performance: list[tuple[Symbol, float]] = sorted(performance.items(), key=lambda x: x[1], reverse=True)
        quintile = int(len(sorted_by_performance) / self.quintile)
        self.long_symbols = [x[0] for x in sorted_by_performance][:quintile]
        self.short_symbols = [x[0] for x in sorted_by_performance][-quintile:]
        
        return self.long_symbols + self.short_symbols
        
    def OnSecuritiesChanged(self, changes: SecurityChanges) -> None:
        for security in changes.AddedSecurities:
            security.SetFeeModel(CustomFeeModel())

    def OnData(self, slice: Slice) -> None:
        if not self.selection_flag:
            return
        self.selection_flag = False
        
        # Trade execution.
        targets: List[PortfolioTarget] = []
        for i, portfolio in enumerate([self.long_symbols, self.short_symbols]):
            for symbol in portfolio:
                if slice.ContainsKey(symbol) and slice[symbol] is not None:
                    targets.append(PortfolioTarget(symbol, ((-1) ** i) / len(portfolio)))

        self.SetHoldings(targets, True)
        self.long_symbols.clear()
        self.short_symbols.clear()

    def Selection(self) -> None:
        # Exclude January trading.
        if self.Time.month != self.excluded_month:
            self.selection_flag = True
        else:
            self.Liquidate()

class SymbolData():
    def __init__(self, symbol: Symbol, period: int, total_assets_history_period: int) -> None:
        self.Symbol: Symbol = symbol
        self.Price: RollingWindow = RollingWindow[float](period)
        self.TotalAssets: RollingWindow = RollingWindow[float](total_assets_history_period)
    
    def update_price(self, value) -> None:
        self.Price.Add(value)
    
    def update_assets(self, assets_value) -> None:
        self.TotalAssets.Add(assets_value)
    
    def asset_data_is_ready(self) -> bool:
        return self.TotalAssets.IsReady
    
    def asset_growth(self) -> float:
        asset_values: list[float] = [x for x in self.TotalAssets]
        return (asset_values[0] - asset_values[1]) / asset_values[1]
    
    def price_is_ready(self) -> bool:
        return self.Price.IsReady
        
    # Performance, one month skipped.
    def performance(self, values_to_skip: int = 1) -> float:
        closes: list[float] = [x for x in self.Price][values_to_skip:]
        return (closes[0] / closes[-1] - 1)

# Custom fee model
class CustomFeeModel(FeeModel):
    def GetOrderFee(self, parameters: OrderFeeParameters) -> OrderFee:
        fee: float = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005
        return OrderFee(CashAmount(fee, "USD"))