US Momentum Strategy Using 12-Month Returns Excluding Most Recent Month
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Quant Buffet native backtest IDEEdit and run Quant Buffet Python for US Momentum Strategy Using 12-Month Returns Excluding Most Recent Month 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: US Momentum Strategy Using 12-Month Returns Excluding Most Recent Month
# 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
Fact, Fiction and Momentum Investing
Clifford S. Asness; Andrea Frazzini; Ronen Israel; Tobias J. Moskowitz
- Capital University
- ?AQR Capital Management, LLC
- ATAgency for Quality Assurance and Accreditation Austria
- Yale University
- National Bureau of Economic Research
- ?AQR Capital
- ?National Bureau of Economic Research (NBER)
- ?Yale University, Yale SOM
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2435323
Strategy in a nutshell
The investment scope includes stocks from the NYSE, AMEX, and NASDAQ. Momentum is determined by the returns from the past 12 months, omitting the latest month to dodge biases related to microstructure and liquidity. To leverage this "momentum," the UMD portfolio adopts a strategy where it goes long on stocks demonstrating high returns over the previous year and shorts those with low returns, aiming to capitalize on the tendency of stocks to continue moving in their recent directional trend. This approach seeks to maximize gains from stocks on an upward trajectory while minimizing exposure to those declining.
Economic rationale
Academic research robustly supports the momentum effect, largely attributed to behavioral biases such as investor herding, overreaction, underreaction, and confirmation bias. For instance, profit can result from buying stocks post-initial positive news, leveraging the market's delayed full response. Rachwalski and Wen suggest in “Momentum, Risk and Underreaction” that momentum profits arise from risks overlooked by standard models and underreaction to new risk information. Long-term momentum strategies, associated with higher risks, yield greater returns compared to short-term strategies. Additionally, momentum investing has been shown to be tax-efficient, as highlighted by Israel and Moskowitz in “How Tax Efficient are Equity Styles?”. They found that after-tax, value and momentum strategies outperform, with momentum being surprisingly tax-efficient despite its higher turnover. This efficiency comes from generating significant short-term losses and lower dividend income, allowing for substantial tax optimization without deviating from the momentum style.