# https://quantpedia.com/strategies/asset-growth-effect/
#
# The investment universe consists of all non-financial U.S. stocks listed on NYSE, AMEX, and NASDAQ. Stocks are then sorted each year at the end 
# of June into ten equal groups based on the percentage change in total assets for the previous year. The investor goes long decile with low asset
# growth firms and short decile with high asset growth firms. The portfolio is weighted equally and rebalanced every year.
#
# QC implementation changes:
#   - The investment universe consists of 3000 largest US stocks listed on NYSE, AMEX, and NASDAQ.

#region imports
from AlgorithmImports import *
import numpy as np
#endregion

class AssetGrowthEffect(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.FundamentalFunction)
        self.Settings.MinimumOrderMarginPortfolioPercentage = 0.0

        # Latest assets data.
        self.total_assets: dict[Symbol, float] = {}
        self.long_symbols: list[Symbol] = []
        self.short_symbols: list[Symbol] = []
        self.selection_flag: bool = False
        
        # Filter Parameters
        self.exchange_codes: List[str] = ['NYS', 'NAS', 'ASE']
        self.quantile: int = 10
        self.rebalancing_month: int = 6
        # self.fin_sector_code: int = 103
        
        self.fundamental_count:int = 3000
        self.fundamental_sorting_key = lambda x: x.MarketCap
        
        self.exchange: Symbol = self.AddEquity("SPY", Resolution.Daily).Symbol
        self.Schedule.On(self.DateRules.MonthEnd(self.exchange), 
                        self.TimeRules.AfterMarketOpen(self.exchange), 
                        self.Selection)

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

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

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

        assets_growth: dict[Symbol, float] = {}
        for security in filtered:
            symbol: Symbol = security.Symbol
            
            if symbol not in self.total_assets:
                self.total_assets[symbol] = None
                
            current_assets: float = security.FinancialStatements.BalanceSheet.TotalAssets.TwelveMonths
            
            # There is not previous assets data.
            if not self.total_assets[symbol]:
                self.total_assets[symbol] = current_assets
                continue
            
            # Assets growth calc.
            assets_growth[symbol] = (current_assets - self.total_assets[symbol]) / self.total_assets[symbol]
            
            # Update data.
            self.total_assets[symbol] = current_assets
        
        # Asset growth sorting.
        if len(assets_growth) >= self.quantile:
            sorted_by_assets_growth: dict[Symbol, float] = sorted(assets_growth.items(), 
                                                                key = lambda x: x[1], 
                                                                reverse = True)
            quantile: int = int(len(sorted_by_assets_growth) / self.quantile)
            self.long_symbols = [x[0] for x in sorted_by_assets_growth[-quantile:]]
            self.short_symbols = [x[0] for x in sorted_by_assets_growth[:quantile]]
        
        return self.long_symbols + self.short_symbols
        
    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:
        if self.Time.month == self.rebalancing_month:
            self.selection_flag = True
            
    def OnSecuritiesChanged(self, changes: SecurityChanges) -> None:
        for security in changes.AddedSecurities:
            security.SetFeeModel(CustomFeeModel())

# 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"))