Overall Statistics Total Trades 4058 Average Win 0.38% Average Loss -0.39% Compounding Annual Return 10.615% Drawdown 63.900% Expectancy 0.302 Net Profit 904.459% Sharpe Ratio 0.445 Probabilistic Sharpe Ratio 0.046% Loss Rate 34% Win Rate 66% Profit-Loss Ratio 0.97 Alpha 0.045 Beta 0.973 Annual Standard Deviation 0.223 Annual Variance 0.05 Information Ratio 0.275 Tracking Error 0.158 Treynor Ratio 0.102 Total Fees $4683.29 Estimated Strategy Capacity$4000000.00 Lowest Capacity Asset CPT R735QTJ8XC9X
# 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)

# 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.Schedule.On(self.DateRules.MonthEnd(self.symbol), self.TimeRules.BeforeMarketClose(self.symbol), self.Selection)

def OnSecuritiesChanged(self, changes):
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:

# 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.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)

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, reverse = True)
quantile = int(len(sorted_by_momentum) / self.quantile)
long = [x 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)) 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

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):

# Performance, one month skipped.
def performance(self, values_to_skip = 0) -> float:
closes = [x for x in self.prices][values_to_skip:]
return (closes / 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"))