# https://quantpedia.com/strategies/enhanced-betting-against-beta-strategy-in-equities/
#
# The investment universe consists of high market capitalization CRSP stocks listed primarily in NYSE and NASDAQ – stocks which have 
# been among the top thousand market capitalization stocks in the previous year. Further, leave out stocks where market betas are 
# estimated to be above two or below 0,3.
# Firstly, divide the stocks into quintiles based on the past 48 months value-weighted market returns. Then use only the quintile 
# with the highest returns and apply the Betting against beta strategy (https://quantpedia.com/Screener/Details/77 in our database 
# – The beta for each stock is calculated with respect to the benchmark using a 1-year rolling window. Stocks are then ranked in
# ascending order on the basis of their estimated beta. The ranked stocks are assigned to one of two portfolios: low beta and high
# beta. Securities are weighted by the ranked betas and portfolios are rebalanced every calendar month. Both portfolios are rescaled
# to have a beta of one at portfolio formation. The “Betting-Against-Beta” is the zero-cost zero-beta portfolio that is long on the 
# low-beta portfolio and short on the high-beta portfolio.).
# The strategy is rebalanced monthly as the reason for high transaction costs and frequent turnovers connected with the daily strategy.
#
# QC implementation changes:
#   - Universe consists of 1000 most liquid stocks traded on NYSE or NASDAQ.    

import numpy as np
from AlgorithmImports import *
import pandas as pd
from typing import List, Dict

class EnhancedBettingAgainstBetaStrategyEquities(QCAlgorithm):
    
    def Initialize(self):
        # self.SetStartDate(2000, 1, 1)
        self.SetStartDate(2017, 1, 1)
        self.SetCash(100000)

        self.SetSecurityInitializer(lambda x: x.SetMarketPrice(self.GetLastKnownPrice(x)))
        
        # Daily price data.
        self.data:Dict[Symbol, SymbolData] = {}
        
        self.period:int = 4*12*21
        self.beta_period:int = 12*21
        self.leverage:int = 10
        self.quantile:int = 5
        self.beta_thresholds:List[float] = [0.3, 2.]
        self.exchange_codes:List[str] = ['NYS', 'NAS']	

        self.market:Symbol = self.AddEquity('SPY', Resolution.Daily).Symbol

        # Warmup market data.
        self.data[self.market] = SymbolData(self.period)
        history:DataFrame = self.History(self.market, self.period, Resolution.Daily)
        if not history.empty:
            closes:Series = history.loc[self.market].close
            for time, close in closes.items():
                self.data[self.market].update(close)        
            
        self.weight:Dict[Symbol, float] = {}
        
        self.fundamental_count:int = 1000
        self.fundamental_sorting_key = lambda x: x.DollarVolume

        self.selection_flag:bool = True
        self.Settings.MinimumOrderMarginPortfolioPercentage = 0.
        self.UniverseSettings.Resolution = Resolution.Daily
        self.AddUniverse(self.FundamentalSelectionFunction)
        self.Schedule.On(self.DateRules.MonthEnd(self.market), self.TimeRules.AfterMarketOpen(self.market), 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].update(stock.AdjustedPrice)

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

        # Warmup price rolling windows.
        for stock in selected:
            symbol:Symbol = stock.Symbol
            if symbol in self.data:
                continue
            
            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:Series = history.loc[symbol].close
            for time, close in closes.items():
                self.data[symbol].update(close)
            
        stock_data:Dict[Symbol, StockData] = {}
        
        market_closes:np.ndarray = np.array([x for x in self.data[self.market]._price][:self.beta_period])
        market_returns:np.ndarray = (market_closes[1:] - market_closes[:-1]) / market_closes[:-1]
        
        if len(market_returns) != 0:
            for stock in selected:
                symbol:Symbol = stock.Symbol
                
                if not self.data[symbol].is_ready():
                    continue

                # Data is ready.
                stock_closes:np.ndarray = np.array([x for x in self.data[symbol]._price][:self.beta_period])
                stock_returns:np.ndarray = (stock_closes[1:] - stock_closes[:-1]) / stock_closes[:-1]
                    
                # Manual beta calc.
                cov:np.ndarray = np.cov(market_returns, stock_returns)[0][1]
                market_variance:float = np.std(market_returns) ** 2
                beta:float = cov / market_variance            
                    
                if beta >= self.beta_thresholds[0] and beta <= self.beta_thresholds[1]:
                    # Return calc.
                    ret = self.data[symbol].performance()
                    stock_data[symbol] = StockData(beta, ret, stock.MarketCap)
    
        if len(stock_data) >= self.quantile: 
            # Value weighted return sorting.
            total_market_cap:float = sum([x[1]._market_cap for x in stock_data.items()])
            sorted_by_return:[List[Tuple[Symbol, StockData]]] = sorted(stock_data.items(), key = lambda x: x[1]._performance * (x[1]._market_cap / total_market_cap), reverse = True)
            quintile:int = int(len(sorted_by_return) / self.quantile)
            top_by_ret:List[StockData] = [x for x in sorted_by_return[:quintile]]
            
            sorted_by_beta:[List[Tuple[Symbol, StockData]]] = sorted(top_by_ret, key = lambda x: x[1]._beta, reverse = True)
            
            beta_median:float = np.median([x[1]._beta for x in sorted_by_beta])
            
            low_beta_stocks:List[Tuple[StockData, float]] = [(x, abs(beta_median - x[1]._beta)) for x in sorted_by_beta if x[1]._beta < beta_median]
            high_beta_stocks:List[Tuple[StockData, float]] = [(x, abs(beta_median - x[1]._beta)) for x in sorted_by_beta if x[1]._beta > beta_median]
            
            # Beta diff weighting.
            for i, portfolio in enumerate([low_beta_stocks, high_beta_stocks]):
                total_diff:float = sum(list(map(lambda x: x[1], portfolio)))
                for symbol_data, diff in portfolio:
                    self.weight[symbol_data[0]] = ((-1)**i) * (diff / total_diff)
        
        return [x[0] for x in self.weight.items()]
        
    def OnData(self, data: Slice) -> None:
        if not self.selection_flag:
            return
        self.selection_flag = False
        
        # portfolio:List[PortfolioTarget] = [PortfolioTarget(symbol, w) for symbol, w in self.weight.items() if symbol in data and data[symbol]]
        portfolio:List[PortfolioTarget] = [PortfolioTarget(symbol, w) for symbol, w in self.weight.items() if w < 0 and symbol in data and data[symbol]]
        self.SetHoldings(portfolio, True)
        
        self.weight.clear()
        
    def Selection(self) -> None:
        self.selection_flag = True

class StockData():
    def __init__(self, beta:float, performance:float, market_cap:float):
        self._beta:float = beta
        self._performance:float = performance
        self._market_cap:float = market_cap

class SymbolData():
    def __init__(self, period:int):
        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 performance(self) -> float:
        return (self._price[0] / self._price[self._price.Count - 1] - 1)

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