Lagged Momentum Rotation Strategy in US REITs
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Quant Buffet native backtest IDEEdit and run Quant Buffet Python for Lagged Momentum Rotation Strategy in US REITs in the browser. Results update live with equity, drawdown, and metrics charts. Allowed: backtest.data, backtest.engine, backtest.metrics, numpy, pandas. Define ASSETS and make_on_day(prices). Shortcut: Ctrl+Enter. API docs →
Quant Buffet syntax cheat sheet (copy / insert)
Paste these fragments into the editor. The sandbox rejects QuantConnect, os, and network libraries.
from __future__ import annotations
import numpy as np
import pandas as pd
from backtest.data import load_daily_prices
from backtest.engine import EngineConfig, PortfolioEngine
from backtest.metrics import compute_metricsASSETS = ["SPY", "QQQ", "TLT", "GLD", "BIL"]def make_on_day(prices: pd.DataFrame):
cols = [c for c in ASSETS if c in prices.columns]
sma = prices[cols].rolling(200, min_periods=200).mean()
state = {"last": None}
def on_day(engine: PortfolioEngine, dt: pd.Timestamp) -> None:
if sma.loc[dt].isna().all():
return
key = (dt.year, dt.month)
if state["last"] == key:
return
state["last"] = key
long = [
s for s in cols
if pd.notna(prices.at[dt, s]) and pd.notna(sma.at[dt, s])
and prices.at[dt, s] > sma.at[dt, s]
]
weights = {} if not long else {s: 1.0 / len(long) for s in long}
engine.set_target_weights(dt, weights)
ready = sma.dropna(how="all").index.min() if sma.notna().any().any() else None
return on_day, readyengine.set_target_weights(dt, {"SPY": 0.60, "BIL": 0.40})Live backtest performance
Export to your platform
Transform Quant Buffet lab code (ASSETS + make_on_day / PortfolioEngine) into native classes for a third-party IDE — then copy and paste.
# Generated from Quant Buffet → QuantConnect LEAN
# Strategy: Lagged Momentum Rotation Strategy in US REITs
# Detected pattern: Absolute momentum
# Source uses Quant Buffet lab APIs (ASSETS + make_on_day / PortfolioEngine).
# Review fees, data, and risk before live trading — educational export only.
from AlgorithmImports import *
class QuantBuffetExport(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2010, 1, 1)
self.SetCash(100000)
tickers = ["SPY", "TLT", "GLD", "BIL"]
self.symbols = []
for t in tickers:
if "-" in t: # crypto proxy e.g. BTC-USD
self.symbols.append(self.AddCrypto(t.replace("-USD", ""), Resolution.Daily).Symbol)
else:
self.symbols.append(self.AddEquity(t, Resolution.Daily).Symbol)
self.Schedule.On(
self.DateRules.MonthStart(self.symbols[0]),
self.TimeRules.AfterMarketOpen(self.symbols[0], 30),
self.Rebalance,
)
# Logic: Long assets with positive 252-day return; equal-weight; monthly.
def Rebalance(self):
# Pattern: abs_momentum — Long assets with positive 252-day return; equal-weight; monthly.
# Default: equal-weight. Port your make_on_day weights here via SetHoldings.
w = 1.0 / len(self.symbols) if self.symbols else 0.0
for symbol in self.symbols:
self.SetHoldings(symbol, w)
Exported code uses the platform’s native classes and libraries. Install dependencies in your third-party IDE, then run. Validate before live trading.
Academic paper
REIT Momentum and the Performance of Real Estate Mutual Funds
Jeroen Derwall; Joop Huij; Dirk Brounen; Wessel Marquering
- NLMaastricht University
- NLUtrecht University
- ?Maastricht University - Department of Finance
- ?Maastricht University - European Centre for Corporate Engagement
- ?Utrecht University - School of Economics
- NLErasmus University Rotterdam
- ?Erasmus University - Rotterdam School of Management
- ?Erasmus University Rotterdam (EUR) - Erasmus Research Institute of Management (ERIM)
- ?Robeco
- NLTinbergen Institute
- ?Erasmus Research Institute of Management (ERIM)
- ?Erasmus University Rotterdam (EUR) - Department of Financial Management
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1161160
Strategy in a nutshell
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).
Economic rationale
Momentum persistence is usually explained by behavioral biases like investor herding, investor over and underreaction, and confirmation bias. For example, if a firm/trust releases good news and the stock price only reacts partially to the good news (under-reaction bias), then buying the stock/trust after the initial release of the news will generate profits.