Overall Statistics
# 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 collections import deque

class MomentumFactorEffectinREITs(QCAlgorithm):

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

        self.symbol = self.AddEquity('SPY', Resolution.Daily).Symbol
        # EW Trenching.
        self.holding_period = 3
        self.managed_queue = deque(maxlen = self.holding_period + 1)

        self.data = {}
        self.period = 12 * 21
        self.coarse_count = 500
        self.selection_flag = False
        self.UniverseSettings.Resolution = Resolution.Daily
        self.Schedule.On(self.DateRules.MonthEnd(self.symbol), self.TimeRules.BeforeMarketClose(self.symbol), self.Selection)
    def OnSecuritiesChanged(self, changes):
        for security in changes.AddedSecurities:
    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:

        # 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:
            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.")
            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:
        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) != 0:
            sorted_by_momentum = sorted(momentum.items(), key = lambda x: x[1], reverse = True)
            tercile = int(len(sorted_by_momentum) / 3)
            long = [x[0] for x in sorted_by_momentum[:tercile] if not self.IsInvested(x[0])]
            short = []
        self.managed_queue.append(RebalanceQueueItem(long, short))
        return long + short
    def IsInvested(self, symbol):
        return self.Securities.ContainsKey(symbol) and self.Portfolio[symbol].Invested
    def OnData(self, data):
        if not self.selection_flag:
        self.selection_flag = False

        # Trade execution
        if len(self.managed_queue) == 0: return
        # Liquidate first items if queue is full.
        if len(self.managed_queue) == self.managed_queue.maxlen:
            item_to_liquidate = self.managed_queue.popleft()
            for symbol in item_to_liquidate.long_symbols + item_to_liquidate.short_symbols:
        curr_stock_set = self.managed_queue[-1]
        if curr_stock_set.count == 0: return
        weight = 1 / self.holding_period
        # Open new trades.
        for symbol in curr_stock_set.long_symbols:
            self.SetHoldings(symbol, weight / len(curr_stock_set.long_symbols))
        for symbol in curr_stock_set.short_symbols:
            self.SetHoldings(symbol, -weight / len(curr_stock_set.short_symbols))
    def Selection(self):
        self.selection_flag = True

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