TrendPilot · ETF Trend Trading Systems

Category: ETF Comparisons

  • Best Free ETF Screener for Momentum Trading in 2026 — Compared

    If you trade ETFs using momentum, you need a screener that can filter by recent performance, RSI, moving average crossovers, and relative strength — all without a monthly subscription. The best free ETF screeners for momentum in 2026 are TradingView (for technical filters and global coverage), Finviz (for its 98 ETF filter categories on the free tier), and ETF.com (for holdings overlap and fund-flow analysis). Each covers different angles of a momentum screening workflow.

    The Short Version

    • TradingView — best for technical momentum filters (RSI, MACD, moving average ribbons). Free tier covers global ETFs. Supports saved screeners and multi-chart comparison.
    • Finviz — 98 ETF filter categories on the free plan. Includes performance (1-day through 52-week), volume, and technical indicators. No registration required to screen.
    • ETF.com — 60+ free filters including total return periods, expense ratio, AUM, and holdings overlap. Best for narrowing a shortlist to the single best fund.
    • ETF Database (ETFdb) — simple free screener with asset class, yield, and performance filters. Good for quick scans but limited technical screening.
    • JustETF — the best choice for European (UCITS) ETF momentum screening. Free tier covers 2,400+ funds with performance and cost filters.

    Below is a head-to-head comparison of the five platforms, followed by a step-by-step workflow for using them to find momentum ETFs.

    What Makes a Good Free ETF Screener for Momentum?

    A momentum screener needs more than just a “performance” column. To identify ETFs with genuine trending behaviour, look for platforms that offer:

    • Multiple time-frame returns — 1-month, 3-month, 6-month, 12-month, and year-to-date views. Momentum persistence varies by lookback window.
    • Technical indicator filters — RSI (Relative Strength Index), MACD (Moving Average Convergence Divergence), and moving average crossovers. These help distinguish trending funds from random noise.
    • Relative strength ranking — the ability to compare an ETF’s recent return against its category or a broad benchmark.
    • Volume and liquidity filters — average daily volume, AUM (assets under management), and bid-ask spread estimates. Low-liquidity ETFs produce slippage that eats into momentum profits.
    • Export or save functionality — so you can track your screener results over time and backtest selection rules.

    Most free screeners offer a subset of these. The trick is combining two or three tools to cover what each one misses.

    Free ETF Screener Comparison Table

    ScreenerFree FiltersTechnical Filters (Free)Global ETFsExportBest For
    TradingViewUnlimited (custom)RSI, MACD, MA, Stochastic, 100+ indicatorsYesScreenshot, share linkTechnical momentum analysis
    Finviz98 ETF filtersRSI, MA crossover, volume signalsNo (US-listed)CSV (paid)Quick ETF screening with technical overlays
    ETF.com60+ filtersNo technical indicators (fundamental only)US-listed onlySave screen (free account)Holdings overlap and fund comparison
    ETF DatabaseBasic (asset class, yield, performance)NoneNo (US-listed)CSV (Pro)Simple initial screening
    JustETFFull (asset class, region, provider, cost)No technical indicatorsYes (UCITS focus)CSV (free)European ETF momentum screening

    How to Screen for Momentum ETFs Using TradingView (Free)

    TradingView’s free ETF screener offers the best technical filter set without a paywall. Here is the step-by-step workflow:

    1. Go to TradingView → Products → Screeners → ETF Screener. The free tier covers all US-listed ETFs and many international exchanges.
    2. Add the following momentum filters in the top panel:
      • Return 1 Month → set a minimum, e.g. > 3%
      • Return 3 Months → set > 5%
      • RSI (14) → range 50–80 (avoids overbought extremes while confirming upward momentum)
      • Volume → minimum 100,000 daily average (ensures liquidity)
      • AUM → minimum $50M (avoids micro-ETFs that may close)
    3. Sort the results table by Return 3 Months descending. The top 10–20 funds are your momentum candidate list.
    4. Click any ticker to open a multi-timeframe chart. Check whether the price is above both its 50-day and 200-day moving average — a classic trend-confirmation signal.
    5. Save the screener configuration (requires a free TradingView account) so you can re-run it weekly.

    TradingView also lets you plot the screener results directly on a heatmap or watchlist, which is useful for tracking changes week over week.

    How to Use Finviz for ETF Momentum Screening (Free)

    Finviz’s free screener does not require registration and provides 98 ETF-specific filter categories. Here is how to configure it for momentum:

    1. Open Finviz.com → Screener → ETF tab (next to the “All” tab).
    2. In the Performance group, set:
      • Performance (Week) → Over 0% (eliminates short-term losers)
      • Performance (Quarter) → Over 5%
      • Performance (Half Year) → Over 8%
    3. In the Technical group, set:
      • RSI (14) → Between 50 and 80
      • 20-Day Simple Moving Average → Price above SMA20 (confirms short-term trend)
      • 50-Day Simple Moving Average → Price above SMA50 (confirms medium-term trend)
    4. Use the Volume filter to set a minimum (e.g., 200,000 average volume). This avoids illiquid ETFs that produce poor fills.
    5. Click the column headers to sort by any metric. The free view displays the data inline — no CSV export without Elite, but the screen is usable for manual review.

    Finviz’s strength is speed: the interface loads fast and the results table is dense with information. For a quick weekly scan, it is hard to beat.

    What About ETF.com and ETF Database?

    ETF.com (owned by VettaFi) offers 60+ free filter criteria but no technical indicator filters. It is best used after you have a shortlist from TradingView or Finviz. Its holdings overlap tool and fund-flow data (30-day, 90-day, 1-year) help you decide between two ETFs that track similar themes. For example, if your momentum screen returns two technology ETFs with similar performance, ETF.com can show you whether their top 10 holdings overlap by 80% — if they do, buy the cheaper one.

    ETF Database (ETFdb) provides the simplest interface for a quick asset-class or yield-based screen. It lacks technical filters entirely, so it is not suitable as a standalone momentum tool. Use it to generate candidate ideas, then cross-reference against TradingView or Finviz for technical confirmation.

    Can You Screen European (UCITS) Momentum ETFs for Free?

    Yes — JustETF is the dominant free screener for UCITS ETFs listed on European exchanges. It covers 2,400+ funds with filters for asset class, region, provider, fund size, and ongoing charges. Performance data (1-month, 3-month, 1-year, 3-year) is available for every fund on the free tier. You cannot filter by RSI or moving averages, but you can sort by trailing returns and then check the underlying index methodology to confirm a momentum strategy. JustETF also lists which brokers offer savings-plan access to each ETF — a practical detail that US-focused screeners ignore.

    For European traders who need technical screening, the best approach is: find candidate UCITS ETFs on JustETF, then chart them on TradingView (which covers European ETFs on its free tier) to check the moving average and RSI setup.

    How to Build a Weekly Momentum Screening Routine

    A repeatable weekly routine keeps your momentum watchlist current without hours of manual work each time. Here is a practical 15-minute workflow:

    1. Monday morning: Run your saved TradingView ETF screener (the five-filter configuration from the section above). Export the top 15 results by 3-month return to a watchlist.
    2. Monday morning (part 2): Cross-reference the top 5–7 candidates on Finviz. Confirm they pass the moving average and RSI filters. Remove any that triggered a volume spike that looks like a blow-off top rather than sustained momentum.
    3. Wednesday: Use ETF.com’s holdings overlap tool to compare your top 2–3 candidates. If they are essentially the same basket of stocks, drop the one with the higher expense ratio.
    4. Friday: Review the week’s performance of your watchlist. Note any that broke below their 50-day moving average — these are candidates to remove the following Monday.

    This routine uses only free tools and takes under 15 minutes once the screener configurations are saved.

    Quick Self-Check

    Test your understanding before you start screening.

    Why is an RSI filter important when screening for momentum ETFs?

    RSI helps distinguish genuine trending funds from overbought spikes. A filter of 50–80 captures ETFs with confirmed upward momentum (above 50) while avoiding those that may be due for a pullback (above 80). Without it, your momentum screen might simply return the most volatile funds rather than the most consistently trending ones.

    Which free screener works best for European-listed UCITS momentum ETFs?

    JustETF is the best free tool for UCITS ETFs, covering over 2,400 funds with performance data across multiple time frames. It lacks technical filters, so pair it with TradingView (which supports European ETFs on its free tier) to check RSI and moving average confirmation before entering a position.

    What is the minimum AUM and volume I should set in a momentum ETF screener?

    A minimum AUM of $50 million and average daily volume of 100,000–200,000 shares avoids micro-ETFs that may close or produce wide bid-ask spreads. Low-liquidity ETFs can erode momentum profits through slippage alone, especially if you trade positions larger than a few thousand dollars.

    Frequently Asked Questions

    Is Finviz’s ETF screener really free?

    Yes, the basic Finviz ETF screener is free and requires no registration. It provides 98 ETF filter categories including performance, volume, and technical indicators. The free tier displays results inline with column sorting. CSV export and real-time data require the paid Elite plan ($39.50/month or $299.50/year as of 2026), which includes a 7-day free trial.

    Can I screen global ETFs for momentum, or only US-listed?

    TradingView’s free ETF screener covers US-listed funds and many international exchanges. JustETF is the best option for European UCITS ETFs. Finviz and ETF.com are limited to US-listed ETFs. If you trade ETFs on Asian or Australian exchanges, TradingView or exchange-specific screeners (e.g., ASX’s ETF screener) are your best free options.

    How often should I re-run my momentum ETF screener?

    Weekly is the standard cadence for momentum screening. Monthly is too slow — you will miss trend reversals. Daily is overkill for most traders because momentum tends to persist on a weekly time frame (Jegadeesh and Titman, 1993). A consistent Monday morning screen gives you a fresh watchlist before the trading week begins.

    Do free ETF screeners include leveraged or inverse ETFs?

    All five tools listed in this guide include leveraged and inverse ETFs in their results. Most allow you to filter them out via the asset class or category filter. ETF Database has a quick “Leveraged” toggle. Finviz lets you exclude them under the “Descriptive” filter group. For momentum screening, it is generally wise to exclude leveraged ETFs — their daily rebalancing creates decay that makes multi-month momentum signals unreliable.

    Can I screen for ETFs with a specific moving average crossover pattern?

    Finviz offers moving average relationship filters (price vs SMA20, SMA50, SMA200) on the free tier. TradingView allows full customisation of moving average crossovers as screener conditions. Neither ETF.com nor ETF Database support MA crossover filtering. For a golden cross (SMA50 crossing above SMA200) or death cross strategy, TradingView is the best free choice.

    Educational content only — not financial advice. Past performance does not guarantee future results. Trading involves risk of loss. Disclaimer · Affiliate Disclosure

  • What Is Sector Rotation With ETFs? A Practical Guide for ETF Trend Traders

    Quick answer: Sector rotation with ETFs means shifting your portfolio between different industry sectors — technology, energy, healthcare, financials, and others — as the economy moves through its cycle. ETFs let you execute these shifts in a single trade per sector, at a low cost, without buying individual stocks.

    The short version

    • Sector rotation is one of the oldest active investment strategies — re-packaged for the ETF era.
    • The core idea: different sectors lead during different phases of the economic cycle.
    • ETFs make rotation practical — one ticker per sector, low expense ratios, high liquidity.
    • Two paths: build your own system using sector ETFs, or buy a dedicated rotation fund (XLSR, SECT).
    • The hard part: identifying where we are in the cycle is much harder in real time than in hindsight.

    What is sector rotation?

    Sector rotation is a strategy that moves capital between economic sectors based on the current or expected phase of the business cycle. The premise is straightforward: cyclically sensitive sectors like financials and industrials tend to outperform during early economic recoveries, while defensive sectors like utilities and consumer staples hold up better during contractions.

    The strategy is not new — institutional investors have used it for decades. What changed in the last 15 years is that ETFs now let retail traders implement the same approach with one trade per sector, full diversification inside each sector, and expense ratios as low as 0.08%.

    How do the economic cycle phases map to sector performance?

    The business cycle is typically divided into four phases. Each phase has historically favored different sectors. These patterns are not guaranteed to repeat, but they are grounded in how each sector’s earnings respond to economic conditions.

    Early cycle (recovery)

    GDP starts growing again after a recession. Interest rates are low or falling. Cyclical sectors lead: Financials (XLF), Industrials (XLI), and Consumer Discretionary (XLY) tend to outperform as economic activity picks up and corporate borrowing resumes.

    Mid-cycle (expansion)

    The economy grows steadily. Corporate profits improve, unemployment falls, and consumer confidence rises. Growth-oriented sectors take over: Technology (XLK) and Communication Services (XLC) typically lead, with Industrials and Materials (XLB) also performing well. This is historically the longest phase of the cycle.

    Late cycle (peak)

    Growth slows, inflation rises, and the central bank starts tightening. Energy (XLE) and Materials (XLB) often lead as commodity prices rise. Defensives begin to emerge as early rotation targets.

    Contraction (recession)

    Economic output contracts. Defensive sectors that provide essential goods and services hold up best: Utilities (XLU), Consumer Staples (XLP), and Healthcare (XLV). Real Estate (XLRE) also tends to be more defensive than cyclicals.

    Cycle PhaseLeading SectorsKey ETFHistorical Tendency
    Early (Recovery)Financials, Industrials, Consumer Disc.XLF, XLI, XLYStrong cyclical outperformance
    Mid (Expansion)Technology, Communication, IndustrialsXLK, XLC, XLIGrowth and momentum dominate
    Late (Peak)Energy, MaterialsXLE, XLBCommodity-linked sectors lead
    Contraction (Recession)Utilities, Staples, HealthcareXLU, XLP, XLVDefensives preserve capital

    Source: Fidelity Investments, “An Introduction to Sector Rotation Strategies” (historical sector leadership patterns). Past performance does not guarantee future results.

    Which ETFs are used for sector rotation?

    The most common toolkit is the SPDR Select Sector ETF series — 11 ETFs that map directly to the 11 GICS sectors. Each tracks an S&P 500 sector index and carries an expense ratio of 0.08%.

    TickerSectorExpense RatioAUM (approx.)
    XLKTechnology0.08%$121B
    XLFFinancials0.08%$55B
    XLEEnergy0.08%$40B
    XLVHealthcare0.08%$44B
    XLIIndustrials0.08%$33B
    XLPConsumer Staples0.08%$18B
    XLUUtilities0.08%$18B
    XLYConsumer Discretionary0.08%$22B
    XLBMaterials0.08%$5B
    XLCCommunication Services0.08%$17B
    XLREReal Estate0.08%$7B

    Expense ratios and AUM sourced from State Street Global Advisors (SSGA.com) as of September 2026. AUM figures are approximate.

    Many brokers also offer commission-free trading on these ETFs, making rotation cheap to execute. Vanguard sector ETFs (VGT, VFH, VDE, etc.) are another option with similar expense ratios.

    Are there dedicated sector rotation ETFs?

    Yes. A handful of actively managed ETFs do the rotation work for you. They hold a portfolio of sector ETFs (or sector derivatives) and adjust exposures based on quantitative models or fundamental analysis.

    XLSR — State Street US Sector Rotation ETF

    Expense ratio: 0.70%. AUM: ~$1.05B. Inception: April 2019. XLSR is an actively managed fund that uses a quantitative model to rotate among S&P 500 sectors. It typically holds 20–30 positions (sector ETFs and individual stocks). The fund targets capital appreciation through the cycle. Note the 0.70% expense ratio is significantly higher than buying the sector ETFs directly (0.08% each).

    SECT — Main Sector Rotation ETF

    Expense ratio: 0.69% (net). AUM: ~$2.88B. Inception: September 2017. SECT is a fund-of-funds that invests in sector ETFs based on fundamental analysis, valuation, and catalyst identification. Its top holdings as of mid-2026 included XLK (Technology, ~40%) and XLF (Financials, ~20%), reflecting a growth tilt. SECT has a longer track record than XLSR and slightly higher AUM.

    These funds remove the timing decision from the investor. The trade-off is the expense ratio — roughly 8x the cost of a do-it-yourself approach using the SPDR Select Sector ETFs.

    How do you build a sector rotation system with ETFs?

    A systematic approach to sector rotation involves four steps:

    1. Choose your universe. Start with the 11 SPDR Select Sector ETFs or a subset (e.g., exclude Real Estate if you don’t need it).
    2. Define your ranking signal. The most common approach is momentum — rank sectors by their trailing 1-month, 3-month, or 6-month total return. Some traders use relative strength vs. the S&P 500. Others overlay valuation or economic indicators.
    3. Select the top N sectors. A typical rule: hold the top 3 or top 5 ranked sectors each month. Rebalance monthly or quarterly.
    4. Execute and manage. Sell whatever you held, buy the new top-ranked sectors. Most brokers execute this as standard ETF trades. Track performance against a buy-and-hold S&P 500 benchmark.

    Important: backtested results for these strategies often look impressive on historical data but suffer from look-ahead bias, survivorship bias, and regime-dependence. A rotation system that worked during the 2009–2020 bull market may produce very different results in a low-correlation, regime-shifting environment.

    What are the main risks of sector rotation with ETFs?

    • Timing risk. The biggest challenge. Economic data is backward-looking. By the time you have confirmed the cycle phase, the market may have already priced in the rotation.
    • Transaction costs. Each rotation trade incurs a bid-ask spread and potentially a commission. Frequent monthly rotation can eat into returns, especially with smaller position sizes.
    • Tax drag. In a taxable account, rotation generates short-term capital gains. Holding sector ETFs in a tax-advantaged account (IRA, SIPP) is strongly recommended.
    • Concentration risk. If you hold only the top 3 sectors, you are heavily betting on a narrow slice of the market. A rotation system that gives the wrong signal can significantly underperform.
    • Behavioral risk. The hardest risk to manage. Rotating out of a sector that has just rallied only to watch it continue higher is psychologically painful. Most retail traders abandon rotation strategies within the first two years.

    Does sector rotation beat buy-and-hold over the long term?

    There is no consensus. SPIVA scorecards from S&P Dow Jones Indices show that the majority of active managers — including those using sector rotation — fail to beat their benchmark over 5- and 10-year periods. Some quantitative studies show that a simple relative-strength rotation among sectors can add 1–3% annualized in certain market regimes, but the edge varies significantly by time period and implementation.

    The honest answer: sector rotation is a valid framework for thinking about market leadership, but it is not a reliably market-beating system. Its real value may lie in helping you understand why certain parts of the market are moving and in providing a disciplined framework for making allocation decisions — rather than in generating alpha on autopilot.

    Quick self-check

    Test your understanding with these three questions. Click each question to reveal the answer.

    What is the main advantage of using ETFs for sector rotation instead of individual stocks?

    ETFs give you diversified exposure to an entire sector in a single trade. Instead of researching and buying 20+ individual stocks in the technology sector, one ticker (XLK) covers the whole sector. This makes rotation cheaper, faster, and easier to execute than stock-level rotation.

    Which sectors typically lead during the early-cycle recovery phase?

    Financials (XLF), Industrials (XLI), and Consumer Discretionary (XLY) tend to lead in early-cycle recoveries. These cyclical sectors benefit from rising economic activity, low interest rates, and increased corporate and consumer spending as the economy emerges from recession.

    Why is identifying the current cycle phase harder than it seems?

    Economic data is backward-looking — GDP, employment, and inflation figures report what already happened. By the time you have statistical confirmation that the economy is in a new phase, the market has often already rotated and priced in the transition. This “timing lag” is the main reason sector rotation underperforms in practice for most retail traders.

    FAQ

    What is the cheapest way to implement sector rotation with ETFs?

    The SPDR Select Sector ETFs (XLK, XLE, XLF, etc.) charge 0.08% expense ratios, making them among the cheapest options. Vanguard sector ETFs are similarly priced. Commission-free trading at most brokers keeps execution costs low. This DIY approach is roughly 8x cheaper than dedicated rotation funds (XLSR at 0.70%, SECT at 0.69%).

    How often should you rebalance a sector rotation portfolio?

    Monthly and quarterly are the two most common frequencies. Monthly rebalancing captures momentum more precisely but generates higher transaction costs and tax drag. Quarterly rebalancing reduces turnover but may miss shorter rotation windows. Many systematic strategies use a 3-month (quarterly) lookback for ranking and rebalance monthly.

    Can sector rotation work in a bear market?

    Yes, but the objective changes. In a bear market, sector rotation shifts from seeking outperformance to minimizing losses. Defensive sectors (XLU, XLP, XLV) typically hold up better than cyclicals. Dedicated rotation funds like XLSR and SECT are designed to reduce downside capture, though their track records during severe bear markets are limited.

    What is the difference between sector rotation and momentum investing?

    Sector rotation is typically driven by the economic cycle — you rotate into sectors that should benefit from the current macro environment. Momentum investing is purely price-driven — you buy whatever has gone up the most, regardless of why. The two overlap when you use momentum as your ranking signal for rotation, but they are conceptually different. Momentum is a tool; sector rotation is the framework.

    Are there any sector rotation ETFs that track an index?

    Most sector rotation ETFs are actively managed. XLSR (State Street) and SECT (Main Management) both use active strategies. There is no widely adopted passive index for sector rotation, because rotation is inherently an active decision. If you want passive, the closest alternative is to build your own systematic rotation using the Select Sector SPDR ETFs, which are themselves passive index funds.

    Educational content only — not financial advice. Past performance does not guarantee future results. Trading involves risk of loss. Full disclaimer · Affiliate disclosure

  • DBMF vs KMLM: Which Managed Futures ETF Belongs in Your Portfolio in 2026?

    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):

    MetricDBMF
    Expense ratio0.85%
    AUM~$4.0 billion
    InceptionMay 7, 2019
    ManagementActive (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 KMLM0.50–0.53

    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):

    MetricKMLM
    Expense ratio0.90%
    AUM~$386 million
    InceptionDecember 1, 2020
    ManagementPassive (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 schemeEqual-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?

    1. 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.
    2. 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.
    3. 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.
    4. 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?

    1. 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.
    2. 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.
    3. 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.
    4. 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?

    MetricDBMFKMLMEdge
    Expense ratio0.85%0.90%DBMF
    AUM~$4.0B~$386MDBMF
    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)2.231.27DBMF
    Dividend yield (TTM)~5.0%~4.4%DBMF
    Management styleActivePassiveDepends on preference
    InceptionMay 2019Dec 2020DBMF (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.

  • MTUM vs SPMO: Which Momentum ETF Belongs in Your Portfolio? (2026 Comparison)

    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

    FeatureMTUMSPMO
    IssuerBlackRock (iShares)Invesco
    InceptionApril 16, 2013October 9, 2015
    Expense Ratio0.15%0.13%
    AUM~$25.3 billion~$22.2 billion
    Underlying IndexMSCI USA Momentum SR VariantS&P 500 Momentum
    Number of Holdings~126100
    Market Cap UniverseLarge + Mid CapLarge Cap (S&P 500)
    RebalancingQuarterlySemi-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.

    YearMTUM ReturnSPMO ReturnS&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:

    1. 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.
    2. 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.
    3. 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…ChooseWhy
    Maximum momentum exposureSPMOHigher concentration in top momentum names, stronger historical returns
    Lower feesSPMO0.13% vs 0.15% — small but meaningful at scale
    Broader diversificationMTUM126 holdings including mid-caps, quarterly rebalancing
    Faster adaptation to market regime changesMTUMQuarterly rebalancing can rotate in/out of sectors faster
    Lower portfolio turnover / tax efficiencySPMO44% turnover vs 116% — less trading and fewer taxable events
    Mid-cap exposureMTUMMSCI 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.

  • How to Backtest an ETF Trend Trading System Without Lookahead or Survivorship Bias

    A backtest for an ETF trend trading system is a simulation that shows how your rules would have behaved on past data. It does not prove the rules will work in the future. Its real job is to catch rules that would have failed, so you stop trading a broken idea before it costs you money.

    Two mistakes quietly ruin most do-it-yourself backtests: lookahead bias and survivorship bias. Both make a bad system look good. This guide shows you how to avoid both, step by step.

    What lookahead bias is

    Lookahead bias happens when your backtest uses information that was not available on the day the trade was decided. A classic case: you use a price that was only revised or published later to make a decision you claimed happened earlier. The result is a test that could never be traded in real life, because real trading happens in the moment.

    • Revised data. Index and fund data gets restated. Use the version of the data that existed at the time.
    • Same-day signals. If your rule uses the closing price to trigger a trade, you cannot also buy at that same close. The close has not printed yet when you decide. Trade the next open or the next close.
    • Adjusted prices that were not adjusted yet. Splits and distributions are applied retroactively. Know which prices your signal actually saw.

    What survivorship bias is

    Survivorship bias happens when you test only the ETFs that still exist today. Funds that underperformed, merged, or were liquidated have already been removed from the list. By testing only survivors, you make the strategy look better than it really was, because the losers are missing.

    • Include ETFs that later closed or merged.
    • Use a universe that was defined at the start of the test, not the list you can buy today.
    • Remember that index changes, where a fund switches its benchmark, also distort history.

    Build the test in the right order

    1. Start with total-return data

    Trend rules are usually decided on price, but performance must include dividends. Use total-return data, or price plus reinvested distributions. Price-only data understates what a buy-and-hold investor earns and distorts any comparison against it.

    2. Fix your signal timing

    Decide on the signal at the close of day N, and trade at the close of day N+1, or the next open. State this rule once and apply it everywhere. This single choice removes most lookahead bias.

    3. Add costs

    Include commissions, bid-ask spread, and slippage on every trade. High-turnover systems look great until costs are added. If your rule trades monthly, the drag compounds fast.

    4. Model rebalancing and position sizing

    If your system holds more than one ETF, model how capital is split and when it is rebalanced. Equal weight versus volatility weighting changes the result, so pick one and write it down.

    5. Validate out of sample

    Never judge a system on the same data you used to tune it. Hold out a period you did not look at, or use a walk-forward test where you repeatedly re-optimize on a moving window and test on what follows. A system that only works in-sample is overfit, not effective.

    A validation checklist you can reuse

    1. Data is total return and does not ignore dividends.
    2. The signal uses only information available at decision time.
    3. Trades execute next open or next close, not same-close.
    4. Costs, spread, and slippage are included.
    5. The universe includes funds that later closed.
    6. Rebalancing and position sizing are explicit.
    7. The result holds on a period the rules never saw.

    Run your rules through our free ETF Trend System Rule Tester to document them and flag the assumptions you still need to check. For the rules themselves, start with how to build an ETF trend trading system.

    FAQ

    What is lookahead bias in a backtest?

    Lookahead bias is using information in a backtest that was not available when the trade was decided, such as revised data or a same-day closing price used for both the signal and the fill. It makes results look better than they could be in live trading.

    What is survivorship bias in ETFs?

    Survivorship bias is testing only the ETFs that still exist today. Funds that were liquidated or merged are missing, so the strategy appears stronger than it truly was.

    Should I use price or total return for backtesting?

    Use total return, which includes reinvested dividends. Price-only data understates what a buy-and-hold benchmark earns and distorts any comparison against it.

    What is out-of-sample validation?

    Out-of-sample validation means testing a system on data it was not tuned on, usually by holding out a period or using a walk-forward test. It is the main defense against overfitting.

    Disclaimer: Educational content only, not financial advice. Backtesting tests ideas; it does not predict future results. Full disclaimer and affiliate disclosure.