Top 3 ETF Momentum Strategy Selecting from SPY, EFA, BND, VNQ, GSG
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Quant Buffet native backtest IDEEdit 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 →
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
Accent = strategy · dashed grey = buy-and-hold benchmark
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: 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", "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: 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.
Academic paper
Relative Strength Strategies for Investing
Meb Faber; Cambria Investment Management
- Institut Mines-Télécom Business School
- ?Cambria Investment Management
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1585517
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.