bonds, commodities, equities, REITs · Global · CFDs, ETFs, funds, futures · Monthly Free

Top 3 ETF Momentum Strategy Selecting from SPY, EFA, BND, VNQ, GSG

Academic paper

Relative Strength Strategies for Investing

AuthorsMeb Faber; Cambria Investment Management

Institute

Institut Mines-Télécom Business School
Cambria Investment Management

Paper linkhttp://papers.ssrn.com/sol3/papers.cfm?abstract_id=1585517

Screenshot from the original paper

Strategy in a nutshell

In an investment strategy selecting from five ETFs—SPY, EFA, BND, VNQ, GSG—focus on identifying the three with the best 12-month performance. Allocate your investment equally among these chosen ETFs, ensuring each one has a third of the total investment. Maintain this allocation for one month. Afterward, assess the performance of all five ETFs again, selecting the top three performers for the next month. This cycle of equal weighting, holding, and monthly rebalancing adheres to a dynamic investment approach, adapting to market trends and optimizing portfolio performance.

Economic rationale

Momentum investing is recognized by scholars as a potent factor for generating returns, continuing to draw academic interest. This leads to numerous momentum-based strategies available to investors, though their future effectiveness remains a consideration. The strategy's foundation lies in rotating among asset classes with varying sensitivities to business cycles, aiming to select those with the highest return potential and lowest loss risk. Kessler and Scherer highlight in “Macro Momentum and the Economy” that rotational strategy success stems from exploiting predictable shifts in investment opportunities, offering rational payoffs to investors. Today's investors have access to a vast selection of mutual funds, ETFs, and closed-end funds, many of which offer low-cost or commission-free trading options, making this strategy increasingly accessible.

Entry & exit

Entry

On each month, rank SPY, EFA, IEF, VNQ, GSG by 126-day return and equal-weight the strongest 1 name(s).

Exit

Sell any holding that falls out of the top 1 at the next rebalance and rotate into the new leaders.

Market data required

Needs at least 126 adjusted daily closes on SPY, EFA, IEF, VNQ, GSG before the first signal is ready. ETF columns print on exchange sessions only. The lab loads Yahoo Finance via backtest.data.load_daily_prices.

Symbols

SPY, EFA, IEF, VNQ, GSG

Frequency

Monthly · Daily adjusted close; signals rebalance on the month change (last available session)

History needed

≥ 126 trading days per symbol (about 6 months), plus a few extra bars so the first signal can fire.

Fields

Adjusted close (Yahoo auto_adjust)
Session date index

Credentials

None for paper and the onsite lab
Broker or exchange API keys only for live orders

APIs & providers

Quant Buffet Data API

The lab calls load_daily_prices for the ASSETS panel of adjusted closes, then caches CSV so backtests replay the same series.

Provider: backtest.data.load_daily_prices
Auth: None for paper / sandbox backtests
Endpoints: load_daily_prices(ASSETS, start="2000-01-01")

API / docs → /en/docs/data-api

Yahoo Finance via yfinance

US equities and ETFs, adjusted OHLCV, free for research (rate limits apply).

Provider: yfinance → Yahoo Finance
Auth: None for public history
Endpoints: Ticker.history / yf.download(auto_adjust=True)

API / docs → github.com/ranaroussi/yfinance

Onsite backtest IDE

Quant Buffet native backtest IDE

Edit and run Quant Buffet Python for Top 3 ETF Momentum Strategy Selecting from SPY, EFA, BND, VNQ, GSG 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 →

IDE · 42 lines

"""
Quant Buffet interactive lab — momentum-asset-allocation-strategy-2
Allowed: backtest.data / backtest.engine / backtest.metrics, numpy, pandas.
Required: a module-level ASSETS list, and def make_on_day(prices) returning (on_day, ready)
"""
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_metrics

ASSETS = ['SPY', 'EFA', 'IEF', 'VNQ', 'GSG']
LOOKBACK = 126
TOP_N = 1
INVERT = False  # True = short-term reversal (rank ascending)

def make_on_day(prices: pd.DataFrame):
    cols = [c for c in ASSETS if c in prices.columns]
    rets = prices[cols].pct_change(LOOKBACK)
    state = {"last": None}

    def on_day(engine: PortfolioEngine, dt: pd.Timestamp) -> None:
        row = rets.loc[dt]
        if row.isna().all():
            return
        key = (dt.year, dt.month)
        if state["last"] == key:
            return
        state["last"] = key
        ranked = row.dropna().sort_values(ascending=INVERT)
        picks = list(ranked.head(TOP_N).index)
        if not INVERT:
            picks = [s for s in picks if ranked[s] > 0] or picks[:1]
        w = {} if not picks else {s: 1.0 / len(picks) for s in picks}
        engine.set_target_weights(dt, w)

    ready = rets.dropna(how="all").index.min() if rets.notna().any().any() else None
    return on_day, ready

"""
Quant Buffet interactive lab — momentum-asset-allocation-strategy-2
Allowed: backtest.data / backtest.engine / backtest.metrics, numpy, pandas.
Required: a module-level ASSETS list, and def make_on_day(prices) returning (on_day, ready)
"""
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_metrics

ASSETS = ['SPY', 'EFA', 'IEF', 'VNQ', 'GSG']
LOOKBACK = 126
TOP_N = 1
INVERT = False  # True = short-term reversal (rank ascending)

def make_on_day(prices: pd.DataFrame):
    cols = [c for c in ASSETS if c in prices.columns]
    rets = prices[cols].pct_change(LOOKBACK)
    state = {"last": None}

def on_day(engine: PortfolioEngine, dt: pd.Timestamp) -> None:
        row = rets.loc[dt]
        if row.isna().all():
            return
        key = (dt.year, dt.month)
        if state["last"] == key:
            return
        state["last"] = key
        ranked = row.dropna().sort_values(ascending=INVERT)
        picks = list(ranked.head(TOP_N).index)
        if not INVERT:
            picks = [s for s in picks if ranked[s] > 0] or picks[:1]
        w = {} if not picks else {s: 1.0 / len(picks) for s in picks}
        engine.set_target_weights(dt, w)

ready = rets.dropna(how="all").index.min() if rets.notna().any().any() else None
    return on_day, ready

Quant Buffet syntax cheat sheet (copy / insert)

Paste these fragments into the editor. The sandbox rejects QuantConnect, os, and network libraries.

Required imports

Only these libraries are allowed in the sandbox.

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_metrics

ASSETS list (whitelisted ETFs)

Module-level list. Tickers must be in the Quant Buffet whitelist.

ASSETS = ["SPY", "QQQ", "TLT", "GLD", "BIL"]

make_on_day contract

Must return (on_day, ready). on_day calls engine.set_target_weights.

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, ready

Set target weights

Weights should sum to about 1.0. Empty dict = 100% cash.

engine.set_target_weights(dt, {"SPY": 0.60, "BIL": 0.40})

Live backtest performance

CAGR

5.90%

Sharpe

0.40

Max DD

-46.38%

Vol

18.75%

Sortino

0.62

Beta

0.43

Up days

88%

Stored preview only. Run backtest to simulate every daily session from Yahoo Finance adjusted closes.

Run the backtest to load orders, then mark them on the equity chart.

Equity curve (indexed = 100)

Accent = strategy · dashed grey = buy-and-hold benchmark. Hover or tap any point for its date and values.

Drawdown

Metrics bar chart

Monthly returns

Order records

Run the backtest to load every order fill alongside 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.

Target platform

Run in: QuantConnect Cloud or LEAN CLI · QCAlgorithm with Equity securities and monthly rebalance.

Detected pattern: Momentum rotationAssets: SPY, EFA, IEF, VNQ, GSG

# Generated from Quant Buffet → QuantConnect LEAN
# Strategy: Top 3 ETF Momentum Strategy Selecting from SPY, EFA, BND, VNQ, GSG
# Detected pattern: Momentum rotation
# 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", "EFA", "IEF", "VNQ", "GSG"]
        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: Hold top 1 by 126-day return; monthly.

    def Rebalance(self):
        scores = {}
        for symbol in self.symbols:
            hist = self.History(symbol, 126 + 5, Resolution.Daily)
            if hist.empty: continue
            close = hist["close"]
            if hasattr(close, "unstack"):
                close = close.unstack(level=0).iloc[:, 0]
            if len(close) < 126 + 1: continue
            scores[symbol] = float(close.iloc[-1] / close.iloc[-126 - 1] - 1)
        ranked = sorted(scores.items(), key=lambda kv: kv[1], reverse=True)[:1]
        for symbol in self.symbols:
            self.SetHoldings(symbol, 0)
        if ranked:
            w = 1.0 / len(ranked)
            for symbol, _ in ranked:
                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.