# https://quantpedia.com/strategies/low-volatility-factor-effect-in-stocks-long-only-version/
#
# The investment universe consists of global large-cap stocks (or US large-cap stocks). At the end of each month, the investor constructs 
# equally weighted decile portfolios by ranking the stocks on the past three-year volatility of weekly returns. The investor goes long 
# stocks in the top decile (stocks with the lowest volatility).
#
# QC implementation changes:
#   - Top quartile (stocks with the lowest volatility) is fundamental instead of decile.

#region imports
from AlgorithmImports import *
import numpy as np
from typing import List, Dict
#endregion

class LowVolatilityFactorEffectStocks(QCAlgorithm):

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

        self.symbol: Symbol = self.AddEquity('SPY', Resolution.Daily).Symbol
        
        self.period: int = 12 * 21
        
        self.fundamental_count: int = 3000
        self.quantile: int = 4
        self.leverage: int = 10
        self.data: Dict[Symbol, SymbolData] = {}
        
        self.long: List[Symbol] = []

        self.selection_flag: bool = True
        self.UniverseSettings.Resolution = Resolution.Daily
        self.Settings.MinimumOrderMarginPortfolioPercentage = 0.
        self.AddUniverse(self.FundamentalSelectionFunction)
        self.Schedule.On(self.DateRules.MonthEnd(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 daily price.
            if symbol in self.data:
                self.data[symbol].update(stock.AdjustedPrice)

        if not self.selection_flag:
            return Universe.Unchanged

        fundamental: List[Fundamental] = [
            x for x in fundamental if x.HasFundamentalData and x.Market == 'usa' and x.MarketCap != 0
        ]
        if len(fundamental) > self.fundamental_count:
            fundamental = sorted(fundamental, key = lambda x: x.MarketCap, reverse=True)[:self.fundamental_count]

        # Warmup price rolling windows.
        weekly_vol: Dict[Symbol, float] = {}

        for stock in fundamental:
            symbol: Symbol = stock.Symbol

            if symbol not in self.data:
                self.data[symbol] = SymbolData(self.period)
                history: DataFrame = self.History(symbol, self.period, Resolution.Daily)
                if history.empty:
                    self.Log(f"Not enough data for {symbol} yet.")
                    continue
                closes: pd.Series = history.loc[symbol].close
                for time, close in closes.items():
                    self.data[symbol].update(close)
            
            if self.data[symbol].is_ready():
                weekly_vol[symbol] = self.data[symbol].volatility()

        if len(weekly_vol) >= self.quantile:
            # volatility sorting
            sorted_by_vol: List[Tuple] = sorted(weekly_vol.items(), key = lambda x: x[1], reverse = True)
            quantile: int = int(len(sorted_by_vol) / self.quantile)
            self.long = [x[0] for x in sorted_by_vol[-quantile:]]
        
        return self.long
        
    def OnData(self, data: Slice) -> None:
        if not self.selection_flag:
            return
        self.selection_flag = False

        # trade execution
        invested: List[Symbol] = [x.Key for x in self.Portfolio if x.Value.Invested]
        for symbol in invested:
            if symbol not in self.long:
                self.Liquidate(symbol)

        for symbol in self.long:
            if symbol in data and data[symbol]:
                self.SetHoldings(symbol, 1. / len(self.long))

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

class SymbolData():
    def __init__(self, period: int) -> None:
        self.price: RollingWindow = RollingWindow[float](period)
    
    def update(self, value: float) -> None:
        self.price.Add(value)
    
    def is_ready(self) -> bool:
        return self.price.IsReady
        
    def volatility(self) -> float:
        closes: List[float] = [x for x in self.price]
        
        # Weekly volatility calc.
        separete_weeks: List[float] = [closes[x:x+5] for x in range(0, len(closes), 5)]
        weekly_returns: List[float] = [(x[0] - x[-1]) / x[-1] for x in separete_weeks]

        return np.std(weekly_returns)   

# 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"))