ETF swing trading strategies are rules for entering and exiting an exchange-traded fund over a few days to a few weeks. The seven below cover the main ways traders time short-term ETF moves, each with an entry trigger, a stop, and the market condition where it works best.
1. Moving-average pullback
In an uptrend, price pulls back toward a rising moving average and then resumes. The entry is a bounce off the 20- or 50-day average with a confirming candle; the stop is a close below the average. It works best in steady trends and fails in sharp reversals. See which moving average to use.
2. Breakout
Price clears a prior high or resistance level on rising volume, and the trader enters on the break. The stop sits below the breakout level. Breakouts pay best when volatility expands and whipsaw the most in quiet ranges.
3. Trend following
The classic: hold while price is above the 200-day moving average, move to cash when it is below. It is slower than the others and gives back part of every move, but it removes discretion. Our 200-day rule example documents the full rules.
4. Mean reversion
When an ETF stretches far from its average on a momentum reading such as RSI, a trader bets it snaps back. Entries come when RSI is oversold (or overbought for shorts); the stop is a move past the extreme. This works in ranges and fails when a trend keeps extending.
5. Relative strength rotation
Hold the strongest ETFs and drop the weakest. You rank a watchlist by recent performance and rotate on a schedule. It is the same idea behind our relative strength guide and the momentum ranker.
6. Moving-average crossover
A shorter average crossing a longer one signals a trend change. The 50-day crossing the 200-day is the common version. Crossovers are always late, so they whipsaw in choppy markets but keep you on the right side of big trends.
7. Range trading
When an ETF is stuck between support and resistance, buy near support and sell near resistance. The stop is a close beyond the range. This needs a clearly defined range and breaks down the moment the range does.
Strategies compared
Strategy
Entry
Stop
Best market
MA pullback
Bounce off rising MA
Close below MA
Steady uptrend
Breakout
Break above resistance
Below breakout level
Expanding volatility
Trend following
Price above 200-day
Close below 200-day
Any, long horizon
Mean reversion
RSI oversold
Past the extreme
Range
RS rotation
Top of ranking
Drops in ranking
Dispersion
MA crossover
50 above 200
50 below 200
Strong trends
Range trading
Near support
Close beyond range
Clear range
A worked example, with no invented numbers
Take any ETF in an uptrend. Write down its 50-day moving average, then decide your rules in advance: you will buy only on a close that bounces back above that average after touching it, and you will sell on a close below it. Now replay the last six months of the chart and count how many times your rule would have fired, and what each trade would have done after spreads and fees. That count, on your own data, tells you more than any example I could print here. Use the backtest calculator to run it properly.
What to watch for
Short-term ETF trading carries real costs. The SEC notes that ETF shares can trade at a premium or discount to their net asset value, and that spreads widen when liquidity is thin. Frequent trading turns those small costs into a large drag, which is why the break-even calculator exists. Before you trade, read how to choose ETFs for swing trading.
FAQ
What is the best ETF swing trading strategy?
There is no single best strategy. Trend following and moving-average pullbacks suit trending markets, while mean reversion suits ranges. The best setup is the one you can apply with consistent rules in the market condition you actually face.
Is swing trading ETFs profitable?
It can be, but it is not guaranteed. FINRA notes that market timing involves actively trading on short-term price moves and carries higher transaction costs and risk. Results depend heavily on spreads, fees, and discipline.
How long do you hold an ETF for swing trading?
Swing trades typically last from a few days to a few weeks, longer than a day trade and shorter than a long-term investment. The holding period is set by your exit rule, not a calendar.
Disclaimer: Educational content only, not financial advice. Short-term trading carries high risk and costs. Full disclaimer and affiliate disclosure.
ETF technical analysis is reading a fund’s price chart and indicators to spot trends and time entries and exits, the same way you would analyze a single stock. It works on ETFs because they trade on an exchange throughout the day, producing the same price and volume data as any other security.
Can you do technical analysis on ETFs?
Yes, and the tools are identical. A moving average, a momentum indicator, and a volume bar all work on an ETF chart exactly as they do on a stock chart. The one real difference is breadth: a broad index ETF holds hundreds of names, so it is more diversified than a single stock. That means signals driven by one company’s news are less relevant, and some short-term chart patterns are weaker on a diversified fund than on an individual name. Long-term trend signals, by contrast, tend to be cleaner on a broad ETF because the noise of a single stock is averaged out.
Momentum indicators. RSI and MACD show whether a move is strengthening or fading.
Relative strength. Comparing an ETF against its benchmark or peers shows leadership. See relative strength ETFs.
Volume. Rising volume on a breakout confirms it; falling volume on a rally is a warning sign.
Chart patterns and the honest caveat
Support, resistance, and breakouts appear on ETF charts just as they do on stocks. The caveat is that a broad ETF is a portfolio, not a company, so patterns that depend on a single business’s story carry less weight. In practice, most ETF technical analysis leans on trend and momentum rather than micro patterns, because a diversified fund trends more reliably than it carves precise formations.
Analyze any ETF in three steps
Here is a repeatable method you can run on any free chart in a few minutes. First, pull up the ETF and add a 200-day simple moving average: is price above or below it? Above means the long-term trend is up. Second, add an RSI or MACD to check whether the move is strong or stretched. Third, compare the ETF’s recent return against the broader market to read its relative strength. Those three checks, trend, momentum, and relative strength, are the whole game, and they are the same logic our backtest calculator replays on your own price data.
The best tools to analyze ETFs
You need two things: a charting tool for indicators and a screener for filtering. Free options like TradingView or your broker’s chart cover the indicators, and ETF screeners let you sort by performance, fees, and flows. On top of that, our free tools handle the parts screeners do not: testing a trend rule and ranking your watchlist by momentum.
FAQ
Can you do technical analysis on ETFs?
Yes. ETFs trade on an exchange the same way stocks do, so the same tools work: moving averages, momentum indicators, volume, and relative strength. The one difference is that a broad index ETF is more diversified than a single stock, so signals tied to one company’s news are less relevant, and some short-term chart patterns can be weaker.
What is the best tool to analyze ETFs?
For most investors, a free charting tool (TradingView or your broker’s built-in charts) plus an ETF screener covers it. You need charts for indicators and a screener for filtering by performance, fees, and flows. Our free backtest calculator and momentum ranker handle the trend and ranking side.
What is the 3:5:10 rule for ETFs?
The 3:5:10 rule is a Securities and Exchange Commission limit on funds investing in other funds, from Section 12(d)(1) of the Investment Company Act of 1940. A fund may not buy more than 3% of another fund’s shares, invest more than 5% of its own assets in a single fund, or more than 10% of its assets in funds overall. It is an anti-pyramiding rule for fund-of-funds arrangements, not a personal allocation rule for individual investors.
What did Warren Buffett say about ETFs?
Buffett has repeatedly recommended a low-cost S&P 500 index fund for most investors, and his estate instructions direct 90% of the money for his wife into an S&P 500 index fund. That is a buy-and-hold index recommendation, not a technical analysis strategy.
Disclaimer: Educational content only, not financial advice. Technical analysis describes what has happened, not what will happen next. Full disclaimer and affiliate disclosure.
Short answer: DBMF (iMGP DBi Managed Futures Strategy ETF) has delivered higher risk-adjusted returns, a lower expense ratio (0.85% vs 0.90%), and smaller drawdowns than KMLM since both launched. KMLM (KraneShares Mount Lucas Managed Futures Index Strategy ETF) offers a purely rules-based passive approach to trend following. The right choice depends on whether you prefer an active hybrid strategy (DBMF) or a transparent rules-based index (KMLM).
The short version
DBMF (0.85% expense ratio, ~$4.0B AUM, launched May 2019) uses a proprietary “Dynamic Beta Engine” to replicate the performance of the top CTA hedge funds at a lower cost. It has delivered a 5-year annualized return of ~9.3% and a 1-year return of ~28.5%.
KMLM (0.90% expense ratio, ~$386M AUM, launched Dec 2020) tracks the KFA MLM Index — a rules-based trend-following system across 18+ futures markets. It has delivered a 5-year annualized return of ~6.3% and a 1-year return of ~15.2%.
Both provide low correlation to stocks and bonds — a key reason retail traders use them to diversify portfolios.
DBMF has a max drawdown of -17.2% vs KMLM’s -27.5%, and lower volatility (2.9% vs 4.8% monthly).
Neither ETF is right or wrong — they take different approaches to the same problem: trend-following exposure in an ETF wrapper.
What are managed futures ETFs, and why do retail traders use them?
Managed futures ETFs are public funds that implement trend-following strategies across global futures markets — equities, bonds, currencies, and commodities. They go long in rising markets and short in falling ones, aiming to profit from persistent price trends regardless of direction.
For retail ETF traders, their main appeal is portfolio diversification. Managed futures have historically shown low correlation to both stocks and bonds. In 2022, when the S&P 500 fell ~19% and the Bloomberg Aggregate Bond Index fell ~13%, both DBMF (+21.6%) and KMLM (+30.6%) posted strong positive returns.
How does DBMF work?
DBMF is an actively managed fund. Its sub-advisor, Dynamic Beta Investments (DBi), uses a proprietary quantitative model called the “Dynamic Beta Engine.” This model analyzes the trailing 60-day performance of the largest CTA (commodity trading advisor) hedge funds and constructs a portfolio of liquid futures contracts that aims to replicate their aggregate performance — not their positions, but their results.
This is a meaningful distinction. DBMF does not try to predict which direction a market will move. Instead, it treats the collective wisdom of the CTA industry as the “alpha source” and uses a small number of liquid futures to track that aggregate performance at a fraction of hedge fund fees.
Key facts about DBMF (source: iM Global Partner, August 2026):
Metric
DBMF
Expense ratio
0.85%
AUM
~$4.0 billion
Inception
May 7, 2019
Management
Active (Dynamic Beta Engine)
Max drawdown (since inception)
-17.2%
5Y annualized return
~9.3%
1Y return
~28.5%
YTD 2026
~12.8%
Dividend yield (TTM)
~5.0%
Correlation to KMLM
0.50–0.53
How does KMLM work?
KMLM is a passive, rules-based fund. It tracks the KFA MLM Index, which is a systematic trend-following strategy developed by Mount Lucas Management, a firm with a decades-long track record in managed futures. The index applies a simple trend signal across a diversified set of futures markets: if the current price is above its moving average, go long; if below, go short.
KMLM’s approach is fully transparent and rules-based — you can see exactly how the index is calculated. The fund equal-weights positions across sectors, including equity indices, fixed income, currencies, and commodities.
Key facts about KMLM (source: KraneShares, August 2026):
Metric
KMLM
Expense ratio
0.90%
AUM
~$386 million
Inception
December 1, 2020
Management
Passive (tracks KFA MLM Index)
Max drawdown (since inception)
-27.5%
5Y annualized return
~6.3%
1Y return
~15.2%
YTD 2026
~13.1%
Dividend yield (TTM)
~4.4%
Index weight scheme
Equal-weight across sectors
DBMF vs KMLM: head-to-head comparison
Which has better returns?
Over the period both have been available (since December 2020), DBMF has outperformed on most return metrics. Over 5 years, DBMF’s annualized return of ~9.3% compares to ~6.3% for KMLM (source: PortfoliosLab, data as of August 22, 2026). On a 1-year basis, DBMF’s ~28.5% return nearly doubles KMLM’s ~15.2%.
YTD 2026, however, the two are roughly neck-and-neck: KMLM leads by ~0.3 percentage points (13.1% vs 12.8%).
Which has lower fees?
DBMF is cheaper at 0.85% vs KMLM’s 0.90%. The difference of 0.05 percentage points is modest — on a $10,000 investment, it’s about $5 per year. For most retail traders, this difference alone should not drive the decision.
Which has lower risk?
DBMF has been the lower-risk option across multiple measures. Its max drawdown of -17.2% is significantly less than KMLM’s -27.5%. Its monthly volatility (2.9%) is also lower than KMLM’s (4.8%). On a risk-adjusted basis, DBMF’s Sharpe ratio of 2.23 (trailing 12 months) exceeds KMLM’s 1.27, meaning it delivered more return per unit of risk (source: PortfoliosLab).
Which provides better diversification?
Both provide strong diversification to stocks and bonds — that is the core reason to own any managed futures ETF. Their correlation to each other is only 0.50–0.53, meaning they behave differently even within the same asset class. KMLM’s higher volatility and larger drawdowns give it a more aggressive profile, while DBMF’s smoother ride may be easier to hold during market stress.
When would you choose DBMF over KMLM?
You want an active approach that adapts. DBMF’s Dynamic Beta Engine adjusts its positioning based on what the top CTA funds are doing. This is a “manager of managers” approach in an ETF wrapper.
Lower drawdowns matter to you. DBMF’s -17.2% max drawdown is over 10 percentage points better than KMLM’s. For traders who want to set and forget a managed futures allocation, this matters.
You prefer a larger, more liquid fund. DBMF has ~$4B in AUM and averages ~1.86M shares traded daily — roughly 4x KMLM’s daily volume. Tighter bid-ask spreads and lower tracking error are natural consequences of larger AUM in managed futures.
You want to track the aggregate CTA industry. DBMF is designed to deliver the average performance of professional CTA hedge funds, making it a “CTA index in an ETF.”
When would you choose KMLM over DBMF?
You prefer a fully transparent, rules-based strategy. KMLM tracks a published index with a known methodology (the KFA MLM Index). You can see exactly what it trades and how positions are sized.
You want equal-weight exposure across sectors. KMLM weights its positions equally across equity, fixed income, currency, and commodity futures. This prevents any single sector from dominating the portfolio.
You believe simple trend-following works over the long term. KMLM’s moving-average crossover approach is the classic trend-following strategy that has been documented in academic literature for decades. There is no “black box” — the logic is straightforward.
You want to avoid active management risk. KMLM has no manager discretion. The model either gives a long signal or a short signal — there is no human judgment layer.
What do the numbers say about DBMF vs KMLM?
Metric
DBMF
KMLM
Edge
Expense ratio
0.85%
0.90%
DBMF
AUM
~$4.0B
~$386M
DBMF
5Y annualized return
~9.3%
~6.3%
DBMF
YTD 2026 return
~12.8%
~13.1%
KMLM (slight)
1Y return
~28.5%
~15.2%
DBMF
Max drawdown
-17.2%
-27.5%
DBMF
Volatility (1M)
2.9%
4.8%
DBMF
Sharpe ratio (1Y)
2.23
1.27
DBMF
Dividend yield (TTM)
~5.0%
~4.4%
DBMF
Management style
Active
Passive
Depends on preference
Inception
May 2019
Dec 2020
DBMF (longer track record)
Can you hold both DBMF and KMLM in the same portfolio?
Yes. Their correlation of 0.50–0.53 means they share roughly half of their price movements. This is moderate enough that holding both provides additional diversification — they tend to zig and zag at different times, even within the same trend-following category. Some traders split their managed futures allocation between the two, using DBMF for the active CTA replication and KMLM for the pure rules-based trend component.
Quick self-check
Test your understanding with these three questions. Click each to reveal the answer.
Question 1: Which ETF has a longer track record — DBMF or KMLM?
DBMF launched in May 2019, while KMLM launched in December 2020. DBMF also has a live track record going back ~10 years through its predecessor SMA structure — roughly 4 years of history before converting to an ETF.
Question 2: What is the core difference in how DBMF and KMLM generate their signals?
DBMF uses a proprietary “Dynamic Beta Engine” that analyzes the 60-day performance of top CTA hedge funds and replicates their aggregate returns. KMLM tracks a rules-based index that applies moving-average trend signals to 18+ futures markets. DBMF is active and tries to replicate the “average CTA”; KMLM is passive and follows a published trend-following methodology.
Question 3: Did both ETFs provide positive returns in 2022 when stocks and bonds both fell sharply?
Yes. In 2022, DBMF returned +21.6% and KMLM returned +30.6%, while the S&P 500 fell ~19% and the Bloomberg Aggregate Bond Index fell ~13%. This is the classic “crisis alpha” that makes managed futures a valuable portfolio diversifier.
Frequently asked questions
Which managed futures ETF is better for a beginner — DBMF or KMLM?
For beginners, DBMF is often the more straightforward choice. It has a longer track record, lower drawdowns, and you are buying a single fund that handles the complexity of managed futures exposure. KMLM is also suitable, but its higher volatility and larger drawdowns may test a new investor’s conviction during flat or down periods.
Are DBMF and KMLM correlated to the stock market?
Both have historically shown near-zero or slightly negative correlation to the S&P 500. DBMF has a beta of approximately -0.2 to the S&P 500, and KMLM has a beta of approximately -0.36. This low correlation is the primary reason traders add them to a diversified portfolio — they tend to do well when stocks struggle.
Which ETF has a higher dividend yield?
DBMF has a higher trailing 12-month dividend yield of ~5.0% compared to KMLM’s ~4.4%. However, distributions from managed futures ETFs can vary significantly from year to year depending on realized gains and interest earned on the collateral portfolio (mostly T-bills).
What is the minimum investment for DBMF or KMLM?
Both are ETFs traded on NYSE Arca, so you can buy a single share. At current prices (August 2026), one share of DBMF costs approximately $31 and one share of KMLM costs approximately $29. There is no minimum beyond the price of one share plus any broker commission.
Can I use DBMF or KMLM in a tax-advantaged account like an IRA?
Yes, both ETFs are eligible for all standard account types including IRAs, 401(k) rollovers, and taxable brokerage accounts. Neither distributes a K-1 tax form — they issue standard 1099s, making them suitable for retirement accounts where K-1 income can be administratively problematic.
Educational content only — not financial advice. Past performance does not guarantee future results. Trading involves risk of loss. Read our disclaimer and affiliate disclosure.
The best moving average for ETF trading depends on your holding period, but the 200-day simple moving average is the standard for long-term trend following. Shorter averages suit faster traders, and there is no single number that beats all others.
SMA versus EMA
A simple moving average (SMA) is the plain average of the last N closing prices. An exponential moving average (EMA) weights recent prices more heavily, so it turns sooner. For ETF trend following, the 200-day SMA is the benchmark most rules are built on, because it is slow enough to ignore noise. Traders who want earlier entries and exits often swap in an EMA or a shorter lookback, accepting more whipsaw trades in exchange.
Common settings and what they are for
50-day. Short-term trend; used for entries, exits, and as the fast line in a crossover.
100-day. Intermediate trend; a middle ground between noise and lag.
A crossover strategy holds the ETF while a short average is above a long average and moves to cash when it flips. The 50-day crossing the 200-day is the most common version. The trade-off is built in: crossovers are always late, so they give back part of every move at the top and bottom, and they whipsaw in choppy markets. The point is discipline, not prediction.
How to pick the right one
Match the lookback to how long you plan to hold. A day or swing trader might use a 20-day average; a long-term investor is better served by the 200-day. The only way to know whether a setting actually helped a given ETF is to test it. Our free backtest calculator does exactly that: paste the ETF’s price history, set the lookback, and see whether the rule beat buy-and-hold.
Test before you trust
Do not take any moving average on faith. Replay it on your own data first, and watch how it behaves in both trending and range-bound periods. The backtest calculator will show you the win, the whipsaws, and the drawdowns, so the choice is made on evidence rather than habit. For the broader picture, see our signal comparison guide.
FAQ
EMA or SMA for ETFs?
Both work. The simple moving average (SMA) weights every day equally and is the classic choice for the 200-day trend filter. The exponential moving average (EMA) reacts faster to recent prices. For long-term trend following on ETFs, the 200-day SMA is the standard; for shorter, faster signals, traders often prefer an EMA.
What moving average settings are best for ETFs?
Common settings are the 50-day for short-term trend, the 100-day for intermediate, and the 200-day for long-term trend. There is no magic number; longer lookbacks give fewer, slower signals with fewer whipsaws, and shorter lookbacks react faster but trade more.
What is a moving average crossover strategy?
A crossover strategy buys when a shorter moving average crosses above a longer one (a golden cross) and sells or moves to cash when it crosses below (a death cross). A common ETF version is the 50-day crossing the 200-day.
How do I choose a moving average for ETF trading?
Match the lookback to your holding period. If you trade weekly, a 20- or 50-day average may fit. If you invest for years, the 200-day is the classic trend filter. Test any choice on your own ETF before trusting it.
Disclaimer: Educational content only, not financial advice. Fund fees and details change; verify them on the issuer’s site before investing. Full disclaimer and affiliate disclosure.
An ETF momentum strategy ranks exchange-traded funds by recent performance and holds the strongest, rebalancing on a set schedule. This page is the step-by-step how-to; for the funds themselves, see our momentum ETF list.
Why momentum when picking ETFs?
Momentum rests on a simple, well-documented idea: assets that have outperformed recently tend to keep outperforming for a while, and assets that have lagged tend to keep lagging. Applying it to ETFs lets you stay with the trends that are working and step away from the ones that are not, without predicting the market.
How to choose ETFs using momentum
The standard recipe has three steps. First, define your universe, such as a list of broad sector or asset-class ETFs you are willing to hold. Second, rank them by total return over a lookback, most often 12 months minus the most recent month (the 12-1 rule) to avoid short-term reversals. Third, hold the top few and drop the rest. Run the ranking on a fixed schedule, usually monthly or quarterly, so the decision stays mechanical.
How much of your portfolio should you allocate?
There is no universal answer, but the honest starting point is smaller than most people expect. Because momentum can lag the market for long stretches, many investors cap a momentum sleeve at 10% to 20% of the portfolio and keep the rest in a broad index or trend-following core. Treat momentum as a satellite that you can afford to see underperform for years at a time, not as the foundation.
Is momentum investing risky?
Yes. Momentum is prone to sharp reversals: when leadership turns over, momentum positions fall fast and together. It also trades more than a buy-and-hold fund, so costs and taxes compound. And it can underperform for years during regime changes. The risks are real and should be sized for, not ignored.
Build your own momentum screen
You can replicate the core idea with a spreadsheet. List your ETFs, pull each one’s return over the last 12 months, and sort descending. Hold the top three to five. You can run this ranking instantly with our free ETF momentum ranker. Do the same exercise next month and trade only when the ranking changes. That is the whole strategy in a repeatable form, and it is the same logic our free backtest calculator replays with your own data.
FAQ
Which momentum ETF is the best to invest in?
The largest and most liquid are MTUM and SPMO, with VFMO as a low-cost option. The best fit depends on your universe (US large cap, broad market, or international) and cost tolerance.
Are momentum ETFs a good investment?
Momentum has been one of the most persistent factors in markets, but it is not a guarantee. Momentum ETFs can lag for years, turn over often, and fall sharply in fast reversals, so most investors treat them as a slice rather than a core holding.
What is a momentum ETF?
A momentum ETF is a fund that holds assets that have risen the most over a recent window, usually 6 to 12 months, on the theory that winners keep winning.
What are momentum factor ETFs?
Momentum factor ETFs are a subset that use a factor-based, rules-driven momentum screen rather than an active manager. MTUM, SPMO, VFMO and QMOM are examples. See the momentum ETF list for details.
Disclaimer: Educational content only, not financial advice. Fund fees and details change; verify them on the issuer’s site before investing. Full disclaimer and affiliate disclosure.
Relative strength ETFs are exchange-traded funds that rank holdings by how strongly they are outperforming their peers or a benchmark, and hold the leaders. They package relative strength analysis into a fund so you do not have to run the ranking yourself.
What is relative strength?
Relative strength measures how one asset is performing compared with something else, usually over 3 to 12 months. If an ETF is up 4% while the broader market is up 2%, it has positive relative strength. The key point is that it is a comparison, not an absolute number: an ETF can fall and still have strong relative strength if everything else fell more.
In practice, analysts rank a universe of ETFs or stocks by their relative strength and buy the strongest, on the theory that leadership persists for a while before it reverses.
What relative strength is not
Not absolute momentum. Momentum is often just an asset’s own return. Relative strength is that return measured against a benchmark or peers.
Not fundamental strength. A company can have strong earnings and weak relative strength, or vice versa. Relative strength is purely price-based.
How relative strength ETFs work
Most relative strength ETFs use a ranking model built by Dorsey Wright, a Nasdaq company that has specialized in relative strength since the 1980s. The model scores each eligible stock on its relative strength, and the fund holds the top slice. Holdings are rebalanced periodically (PDP rebalances quarterly) so the portfolio keeps rotating into new leaders as old ones fade.
The main relative strength ETFs
Fund
Ticker
Expense ratio
Approach
Relative Strength Managed Volatility Strategy
RSMV
0.95%
Relative strength plus a volatility overlay
Invesco Dorsey Wright Momentum
PDP
0.62%
Ranks roughly 100 US stocks by relative strength
Expense ratios as of August 2026. Verify current fees before investing. Dorsey Wright also runs DWAS (small cap) and QQQA (Nasdaq-100) on the same relative strength approach.
Relative strength versus momentum
The line is blurry, and many funds mix the two. A momentum ETF typically buys assets that have risen the most. A relative strength ETF buys assets that are beating their benchmark. In a rising market the two produce similar lists; in a falling market relative strength can point to assets that are merely falling less. For the step-by-step process of applying either, see our ETF momentum strategy guide.
Risks to relative strength
Reversals. Leadership can turn over suddenly, and a relative strength fund will be caught holding yesterday’s leaders.
Concentration. Ranking models tilt toward a few hot sectors, so the fund can be far less diversified than the index.
Turnover and cost. Quarterly rebalancing trades more than an index fund, adding costs and tax drag on top of the expense ratio.
Chopping. In trendless, range-bound markets the strategy whipsaws and can lag a simple index fund.
Rank your own watchlist in five minutes
You do not need a fund to see relative strength in action. Take the ETFs on your watchlist and write down each one’s return over the last 6 and 12 months, then the same for the S&P 500. Rank them by how far above or below the index they sit. The ones above the index have positive relative strength; the ones below have negative. Watch how that ranking shifts month to month. That is the entire concept, and it is why the funds are mechanical rather than clever.
To test whether a trend or momentum rule actually helped historically, run the numbers through our free backtest calculator.
FAQ
What is relative strength?
Relative strength measures how an asset is performing compared with a benchmark or its peers, usually over 3 to 12 months. A high relative strength reading means the asset is outperforming, not that it has risen in absolute terms.
What is a relative strength ETF?
A relative strength ETF is a fund that ranks holdings by relative strength and holds the leaders, rebalancing on a schedule. RSMV and the Dorsey Wright funds (PDP, DWAS, QQQA) are examples.
Is relative strength the same as momentum?
They overlap but are not the same. Momentum usually means an asset’s own recent return. Relative strength compares that return against a benchmark or peers, so an asset can have positive momentum but weak relative strength if its peers rose more.
What is the best relative strength ETF?
There is no single best. RSMV combines relative strength with a volatility overlay, while PDP ranks roughly 100 US stocks by relative strength. The right choice depends on whether you want the volatility management or a pure ranking.
Disclaimer: Educational content only, not financial advice. Fund fees and details change; verify them on the issuer’s site before investing. Full disclaimer and affiliate disclosure.
MTUM (iShares MSCI USA Momentum Factor ETF) and SPMO (Invesco S&P 500 Momentum ETF) are the two largest momentum ETFs available to US retail traders. Both buy stocks with strong recent price gains, but they differ in their index methodology, rebalancing frequency, cost, and historical returns. SPMO has outperformed over the past decade with a lower expense ratio, while MTUM offers broader diversification including mid-cap stocks.
The short version:
SPMO tracks the S&P 500 Momentum Index — 100 large-cap stocks, rebalanced semi-annually. Expense ratio: 0.13%. 10-year annualized return: ~20.3%.
MTUM tracks the MSCI USA Momentum SR Variant Index — ~126 large- and mid-cap stocks, rebalanced quarterly. Expense ratio: 0.15%. 10-year annualized return: ~16.5%.
SPMO won on performance in 5 of the last 6 calendar years (2021–2026) and carries a lower fee.
MTUM wins on diversification with more holdings across a broader market-cap range and quarterly rebalancing that adapts faster to market shifts.
Both funds share ~74% overlap in holdings — they’re more similar than different.
What Are MTUM and SPMO?
MTUM and SPMO are both momentum factor ETFs — funds that select stocks based on recent price performance rather than market cap weighting. They are rules-based, passive funds that track a momentum index, not active strategies.
How Do MTUM and SPMO Differ in Methodology?
What index does MTUM track?
MTUM tracks the MSCI USA Momentum SR Variant Index. The “SR Variant” means rebalancing changes are spread over three days around the effective date to reduce trading costs. The index selects stocks from the MSCI USA Index (large- and mid-cap US stocks) with the highest momentum scores based on 6-month and 12-month price returns (excluding the most recent month). It holds approximately 126 stocks and rebalances quarterly.
What index does SPMO track?
SPMO tracks the S&P 500 Momentum Index. It selects the 100 stocks with the highest momentum scores from the S&P 500 (large-cap only). Momentum is calculated using 12-month price returns, excluding the most recent month. It rebalances semi-annually in March and September.
MTUM vs SPMO: Side-by-Side Comparison
Feature
MTUM
SPMO
Issuer
BlackRock (iShares)
Invesco
Inception
April 16, 2013
October 9, 2015
Expense Ratio
0.15%
0.13%
AUM
~$25.3 billion
~$22.2 billion
Underlying Index
MSCI USA Momentum SR Variant
S&P 500 Momentum
Number of Holdings
~126
100
Market Cap Universe
Large + Mid Cap
Large Cap (S&P 500)
Rebalancing
Quarterly
Semi-annual (Mar/Sep)
Portfolio Turnover
~116%
~44%
Dividend Yield (TTM)
~0.54%
~0.69%
Beta (3-5Y)
~1.22
~1.06
Sources: iShares.com, Schwab ETF Research, Invesco, as of August 2026.
Which Has Better Historical Performance?
SPMO has significantly outperformed MTUM over the past decade. According to PortfoliosLab, SPMO delivered a 10-year annualized return of ~20.3% compared to MTUM’s ~16.5% — a meaningful gap driven by stronger performance in recent years.
Year
MTUM Return
SPMO Return
S&P 500
2021
+13.5%
+23%
+27%
2022
-18.2%
-10%
-19%
2023
+9.1%
+18%
+24%
2024
+32.9%
+46%
+23%
2025
+22.1%
+27%
+16%
2026 YTD (Jul)
~+20.0%
~+27-29%
~+13%
Sources: iShares.com (MTUM), Trefis/PortfoliosLab (SPMO), as of August 2026. YTD figures are approximate NAV total returns.
SPMO outperformed MTUM in 5 out of the 6 periods shown. The gap was widest in 2024 (SPMO +46% vs MTUM +33%) and 2021 (+23% vs +13.5%). Both funds meaningfully beat the S&P 500 in 2024, 2025, and 2026 YTD.
Why Has SPMO Outperformed MTUM?
Three factors explain most of the gap:
More concentrated momentum signal. SPMO holds only the top 100 momentum stocks from the S&P 500. MTUM casts a wider net (126 stocks including mid-caps), which dilutes the momentum factor. The SPMO portfolio has a higher “effective holdings ratio” (0.28 vs MTUM’s lower concentration), meaning it places bigger bets on the highest-momentum names.
Lower turnover. SPMO’s semi-annual rebalancing (44% turnover) means less trading drag. MTUM’s quarterly rebalancing (116% turnover) incurs more transaction costs and can trade in and out of positions more frequently.
Lower fee. SPMO charges 0.13% vs MTUM’s 0.15%. The 2-basis-point difference is small but compounds over time.
What Are the Holdings and Sector Differences?
Both funds are heavily weighted toward technology — the sector that has driven most US market momentum in recent years. As of mid-2026, MTUM allocated ~47.6% to tech, while SPMO allocated ~56.8%. Both are significantly overweight tech compared to the broader S&P 500.
Key sector differences:
Energy: MTUM holds ~11.8% vs SPMO’s lower allocation — MTUM’s mid-cap inclusion picks up more energy names.
Industrials: MTUM ~14.8% vs SPMO ~12.8%.
Consumer Staples: SPMO holds ~4% vs MTUM’s lower allocation, providing a touch more defensive positioning.
Communication Services: SPMO ~9% vs MTUM’s lower weight.
Despite these differences, the two funds share ~74% overlap in holdings (per ETF Trends analysis). The largest common holdings include Nvidia, Apple, Meta, and other mega-cap tech names that have persistently exhibited strong momentum.
Which Is Better for a Trend Trading System?
The answer depends on your system’s design:
If your system prioritizes…
Choose
Why
Maximum momentum exposure
SPMO
Higher concentration in top momentum names, stronger historical returns
Lower fees
SPMO
0.13% vs 0.15% — small but meaningful at scale
Broader diversification
MTUM
126 holdings including mid-caps, quarterly rebalancing
Faster adaptation to market regime changes
MTUM
Quarterly rebalancing can rotate in/out of sectors faster
Lower portfolio turnover / tax efficiency
SPMO
44% turnover vs 116% — less trading and fewer taxable events
Mid-cap exposure
MTUM
MSCI USA includes mid-caps; S&P 500 is large-cap only
For a simple trend-following system that buys momentum and holds, SPMO has the stronger track record. For a tactical system that rebalances frequently or pairs momentum with other factors, MTUM’s quarterly resets and broader universe may complement your strategy better.
Quick Self-Check
Test your understanding of the MTUM vs SPMO comparison. Click each question to reveal the answer.
Which fund has a lower expense ratio?
SPMO charges 0.13%, while MTUM charges 0.15%. SPMO is cheaper by 2 basis points.
How often does each fund rebalance?
MTUM rebalances quarterly (4 times per year). SPMO rebalances semi-annually (twice per year, in March and September).
Which fund includes mid-cap stocks?
MTUM includes mid-cap stocks because it tracks the MSCI USA Index (large- + mid-cap). SPMO is limited to the S&P 500, which is large-cap only.
Frequently Asked Questions
Can I use both MTUM and SPMO in the same portfolio?
Yes, but there is significant overlap (about 74% of holdings are shared). Combining both adds little diversification benefit. You are better off picking one and allocating the rest of your portfolio to a different factor or asset class.
Which fund is better for long-term holding?
SPMO has a stronger long-term track record (10-year annualized ~20.3% vs MTUM’s ~16.5%) and a lower expense ratio. However, momentum as a factor can experience long periods of underperformance — neither fund should be your only holding.
Do MTUM and SPMO pay dividends?
Yes, both pay dividends. MTUM pays quarterly dividends with a trailing 12-month yield of approximately 0.54%. SPMO also pays quarterly with a yield of approximately 0.69%. Both are relatively low-yield due to their growth-oriented holdings.
Which momentum ETF is larger by assets?
MTUM is the larger fund with approximately $25.3 billion in AUM (as of August 2026), compared to SPMO’s $22.2 billion. MTUM has been on the market since 2013, two years longer than SPMO (2015).
What happens to these funds during a bear market?
Momentum funds tend to fall harder in bear markets because they are heavily invested in the stocks that rose the most — which also tend to fall the fastest. In 2022, MTUM fell -18.2% and SPMO fell -10%, compared to the S&P 500’s -19%. SPMO’s semi-annual rebalancing helped it hold up better by not trading in and out of positions during the downturn.
Educational content only — not financial advice. Past performance does not guarantee future results. Trading involves risk of loss. View our Disclaimer and Affiliate Disclosure.
This free calculator estimates whether a short-term ETF trade covers its own costs and beats the alternative of simply holding cash. Enter your trade, its costs, and a cash comparison, and it shows your net result and the break-even price. It uses only the numbers you type in.
Step 1 of 3
Trade setup
The result uses your expected exit price. It is an estimate, not a prediction.
Trading and ETF costs
Total spread impact for entering and exiting.
Prorated for your holding period.
Compare with cash
What you could earn holding cash instead.
Applied only to a positive taxable result.
Tax treatment varies by jurisdiction and account type. This is a simplified estimate.
Results Estimate only
Cost breakdown
How the calculation works
Short-term trades can be affected heavily by spreads and transaction fees. ETF expense ratios accrue even over a brief holding period. The break-even price is the exit price that covers the entered pre-tax costs. The cash comparison is an estimated alternative return, not a guarantee. Tax calculations are simplified and may not match your jurisdiction or account type.
How to use it
Enter your starting amount, entry and expected exit price, and holding dates. Then add your trading costs and fund expense ratio, and finally the cash return you could earn by doing nothing. The calculator shows your net profit or loss, the break-even exit price, and whether the trade beats cash. For the strategy behind the trade, see our ETF swing trading strategies guide.
FAQ
What does the break-even price tell me?
The break-even exit price is the price you need to reach so that your proceeds cover your entry cost plus the buy fee, sell fee, spread, and the prorated fund expense. It is a pre-tax number, so taxes are handled separately in the comparison.
What is the cash alternative?
The cash alternative is the estimate of what the same money would grow to if held in cash or a cash-based ETF at the annual return you enter, over the same holding period.
Is the result a prediction?
No. It is an estimate built entirely from the prices and assumptions you enter. Actual execution prices, spreads, fees, and tax treatment can all differ.
Disclaimer: This calculator provides an estimate based on the assumptions entered. Actual execution prices, spreads, fees, distributions, market movements, and tax treatment may differ. It is not personal financial or tax advice. Full disclaimer and affiliate disclosure.
This free tool ranks your ETFs by momentum, so you can see which ones have been strongest over a window you choose. Enter two prices per ETF (the price some months ago and the price now), and it sorts them from strongest to weakest. It uses only the numbers you type in.
Step 1 of 3
What momentum window do you want to measure?
How many ETFs do you want to rank?
Enter each ETF’s price
Ranking by momentum
How to use it
Pick a lookback window (3, 6, or 12 months), choose how many ETFs to compare, then enter each ETF’s ticker, its price at the start of the window, and its price today. The tool ranks them by percentage change. You can read both prices off any chart, so no data export is needed.
Momentum is the percentage change from the price you enter for N months ago to the price you enter for today. A positive number means the ETF rose; a negative number means it fell. The ETFs are then ranked highest to lowest.
What price data do I need?
Just two numbers per ETF: the price at the start of your chosen window and the price now. You can read both off any chart. The tool does not fetch or invent any market data.
Does a high rank predict future returns?
No. Momentum is a screen that shows what has been strong recently, not a promise about what comes next. Rankings change, and past strength does not guarantee future returns.
Disclaimer: Educational tool only, not financial advice. It ranks the numbers you enter; it does not predict future performance or recommend any trade. Full disclaimer and affiliate disclosure.
This free calculator tests whether a simple moving-average trend rule improved an ETF’s historical risk-adjusted performance. You paste your own price data, and it replays the rule with no lookahead bias, comparing the trend strategy against buy-and-hold. It does not fetch or invent any market data.
1Enter ETF price data
Paste two columns: date and close. Accepts headers like date, time, close, adj close. Order does not matter; blank lines are ignored.
2Configure the strategy
A transaction cost is applied every time the strategy switches between invested and cash.
3Optional benchmark
4Results
Trade and signal changes
Detailed period results
Methodology
The moving average uses prior closing prices only. The signal at each period sets the position for the NEXT period, so no future information leaks into the decision. When the strategy is invested it earns the ETF’s return; when in cash it earns the cash return per period. A transaction cost is deducted on every switch between invested and cash. Annualization uses your periods-per-year input and calendar elapsed time for CAGR. The calculator runs entirely on the prices you paste.
Limitations
Historical backtests are hypothetical. Results depend on your data and assumptions. Closing prices may not match your actual execution price. Taxes, spreads and slippage beyond the entered cost, dividends, fund fees, and liquidity effects may not be fully represented unless reflected in your price series. A trend-following rule can underperform buy-and-hold.
Use your own historical data and assumptions. This educational backtest does not predict future performance or provide investment advice.
How to use it
Download or copy daily closing prices for any ETF (Yahoo Finance and most brokers export a CSV), paste the date and close columns into step 1, set your lookback period (200 is the classic trend-following default), and run it. The calculator moves the strategy to cash whenever the price closes below its moving average, and back in when it closes above, applying your transaction cost on each switch.
The moving average at each period uses only prior closing prices, and the signal sets the position for the NEXT period. So no future information leaks into the trading decision.
What data do I need to run it?
A CSV with two columns: date and close price, pasted from any source such as Yahoo Finance or your broker. The calculator does not fetch or invent any market data.
Does it predict future returns?
No. It replays a rule on the prices you supply. Historical backtests are hypothetical and do not predict future performance.
Disclaimer: Educational tool only, not financial advice. It performs arithmetic on data you supply; it does not predict future performance or recommend any trade. Full disclaimer and affiliate disclosure.
