# https://quantpedia.com/strategies/momentum-factor-effect-in-reits/
#
# The investment universe consists of all US REITs listed on markets. Every month, the investor ranks all available REITs 
# by their past 11-month return one-month lagged and groups them into equally weighted tercile portfolios. He/she then goes
# long on the best performing tercile for three months. One-third of the portfolio is rebalanced this way monthly, and REITs
# are equally weighted. This is not the only way to capture the momentum factor in REITs as a consequential portfolio could be
# formed as a long/short or from quartiles/quintiles/deciles instead of terciles or based on different formation and holding 
# periods (additional types of this strategy are stated in the “Other papers” section).
#
# QC implementation changes:
#   - Instead of all listed stock, we select 500 most liquid stocks from QC filtered stock universe (~8000 stocks) due to time complexity issues tied to whole universe filtering.
    
from AlgorithmImports import *

class MomentumFactorEffectinREITs(QCAlgorithm):

    def Initialize(self):
        self.SetStartDate(2000, 1, 1)
        self.SetCash(100000) 

        self.symbol = self.AddEquity('SPY', Resolution.Daily).Symbol
        
        # EW Trenching.
        self.holding_period = 3
        self.managed_queue = []

        self.data = {}
        self.period = 12 * 21
        self.quantile = 3
        self.leverage = 5
        
        self.coarse_count = 500
        self.selection_flag = False
        self.UniverseSettings.Resolution = Resolution.Daily
        self.AddUniverse(self.CoarseSelectionFunction,self.FineSelectionFunction)
        self.Schedule.On(self.DateRules.MonthEnd(self.symbol), self.TimeRules.BeforeMarketClose(self.symbol), self.Selection)
        
    def OnSecuritiesChanged(self, changes):
        for security in changes.AddedSecurities:
            security.SetFeeModel(CustomFeeModel())
            security.SetLeverage(self.leverage)
            
    def CoarseSelectionFunction(self, coarse):
        if not self.selection_flag:
            return Universe.Unchanged

        # Update the rolling window every month.
        for stock in coarse:
            symbol = stock.Symbol

            # Store monthly price.
            if symbol in self.data:
                self.data[symbol].update(stock.AdjustedPrice)

        # selected = [x.Symbol for x in coarse if x.HasFundamentalData and x.Market == 'usa']
        selected = [x.Symbol
            for x in sorted([x for x in coarse if x.HasFundamentalData and x.Market == 'usa'],
                key = lambda x: x.DollarVolume, reverse = True)[:self.coarse_count]]
        
        # Warmup price rolling windows.
        for symbol in selected:
            if symbol in self.data:
                continue
            
            self.data[symbol] = SymbolData(symbol, 13)
            history = self.History(symbol, self.period * 30, Resolution.Daily)
            if history.empty:
                self.Log(f"Not enough data for {symbol} yet.")
                continue
            closes = history.loc[symbol].close
            
            closes_len = len(closes.keys())
            # Find monthly closes.
            for index, time_close in enumerate(closes.iteritems()):
                # index out of bounds check.
                if index + 1 < closes_len:
                    date_month = time_close[0].date().month
                    next_date_month = closes.keys()[index + 1].month
                
                    # Found last day of month.
                    if date_month != next_date_month:
                        self.data[symbol].update(time_close[1])
        
        selected = [x for x in selected if self.data[x].is_ready()]
        return selected

    def FineSelectionFunction(self, fine):
        fine = [x.Symbol for x in fine if (x.CompanyReference.IsREIT == 1)] 
        
        momentum = {x : self.data[x].performance(1) for x in fine}
        
        long = []
        short = []
        
        if len(momentum) >= self.quantile:
            sorted_by_momentum = sorted(momentum.items(), key = lambda x: x[1], reverse = True)
            quantile = int(len(sorted_by_momentum) / self.quantile)
            long = [x[0] for x in sorted_by_momentum[:quantile]]
            
            weight = self.Portfolio.TotalPortfolioValue / self.holding_period / len(long)
            long_symbol_q = [(symbol, np.floor(weight / self.data[symbol].prices[0])) for symbol in long]
            
            self.managed_queue.append(RebalanceQueueItem(long_symbol_q))

        return long
        
    def OnData(self, data):
        if not self.selection_flag:
            return
        self.selection_flag = False

        # rebalance portfolio
        remove_item = None
        
        for item in self.managed_queue:
            if item.holding_period == self.holding_period: # all portfolio parts are held for n months
                for symbol, quantity in item.opened_symbol_q:
                    self.MarketOrder(symbol, -quantity)
                            
                remove_item = item
            
            # trade execution    
            if item.holding_period == 0: # all portfolio parts are held for n months
                opened_symbol_q = []
                
                for symbol, quantity in item.opened_symbol_q:
                    if symbol in data and data[symbol]:
                        self.MarketOrder(symbol, quantity)
                        opened_symbol_q.append((symbol, quantity))
                            
                # only opened orders will be closed        
                item.opened_symbol_q = opened_symbol_q
                
            item.holding_period += 1
            
        # need to remove closed part of portfolio after loop. Otherwise it will miss one item in self.managed_queue
        if remove_item:
            self.managed_queue.remove(remove_item)
    
    def Selection(self):
        self.selection_flag = True

class SymbolData():
    def __init__(self, symbol, period):
        self.symbol = symbol
        self.prices = RollingWindow[float](period)
    
    def update(self, value):
        self.prices.Add(value)
    
    def is_ready(self) -> bool:
        return self.prices.IsReady
        
    # Performance, one month skipped.
    def performance(self, values_to_skip = 0) -> float:
        closes = [x for x in self.prices][values_to_skip:]
        return (closes[0] / closes[-1] - 1)
        
class RebalanceQueueItem():
    def __init__(self, symbol_q):
        # symbol/quantity collections
        self.opened_symbol_q = symbol_q  
        self.holding_period = 0
        
# Custom fee model
class CustomFeeModel(FeeModel):
    def GetOrderFee(self, parameters):
        fee = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005
        return OrderFee(CashAmount(fee, "USD"))