Pacer Trendpilot Fund of Funds ETF (TRND)

The Pacer Trendpilot Fund of Funds ETF (ticker TRND) holds a portfolio of other exchange-traded funds and rebalances them using a quantitative trend-following algorithm — a rules-based system that tries to catch rising trends in equities and shift toward bonds or alternatives when markets show weakness.

Unlike a traditional balanced fund that might stay 60% stocks and 40% bonds forever, or a robo-advisor that rebalances annually, TRND uses mathematical trend signals to move money between asset classes dynamically. When stocks are rising on broad momentum, the algorithm weights equities heavily; when that momentum breaks, it steps back and leans more toward bonds or stable assets. The idea is not to beat the market, but to reduce exposure when conditions deteriorate and to stay invested when conditions improve.

The fund of funds structure

TRND does not own stocks or bonds directly. Instead it holds a collection of ETFs — some tracking the S&P 500, others tracking bond indices or real estate, still others tracking commodity prices or international equities. By building a portfolio of funds rather than individual securities, TRND achieves broad diversification without holding thousands of separate positions. Each underlying ETF is liquid, so the overall fund can be traded during normal market hours like any ETF.

The basket typically includes equity ETFs from large-cap U.S. stocks to emerging markets; fixed-income ETFs from government bonds to high-yield credit; real-asset ETFs tracking real estate and commodities; and occasionally alternative strategies. The exact set of underlying funds can shift as the strategy evolves, so the fund is not a static index.

The trend-following algorithm

The engine that drives the fund’s returns is the Trendpilot algorithm, which examines the recent price behavior of major asset classes and computes a trend score. If U.S. equities have been rising for the last few months and the uptrend is accelerating, the algorithm signals a higher equity weight. If that momentum flattens or reverses, the signal weakens, and the fund automatically rebalances to hold less equity exposure and more in bonds or alternatives.

This is not a market-timing scheme designed to sell at the top and buy at the bottom — that is impossible to do consistently. Instead, it is a mechanical rule that follows momentum. The logic is simple: trends tend to persist over intermediate time horizons (weeks to months), so betting on the continuation of a trend you can measure is a reasonable long-term approach. It will not save you from every crash, because crashes often happen on single bad days when the algorithm has no warning. But it can reduce exposure during the slow-motion deteriorations that make up many bear markets.

The algorithm updates regularly, often daily or weekly, and the fund rebalances accordingly. That means the fund is constantly adjusting, not holding tight to a fixed allocation. This adds to its operational complexity and costs, which shows up in the expense ratio.

Costs and the expense ratio

TRND charges more than a plain index fund because it is doing more work. An S&P 500 index fund costs roughly 0.03% per year; TRND typically costs around 0.60% to 0.80% per year, depending on the current version of the fund. That extra 0.60% is the price of the algorithm, the rebalancing activity, and the staff that maintains it. Over a decade, that compares to a 0.03% cost index, that compounds into meaningful drag.

The fund also carries trading costs, though these are mostly hidden. Every time the algorithm rebalances — shifting, say, 5% out of stocks into bonds — the fund buys and sells ETF shares, incurring bid-ask spreads and market-impact costs. Some of this comes out of the expense ratio; some gets charged directly as a transaction cost at the time of trading.

The bet embedded in the strategy

TRND’s core bet is that recent price trends contain information about future returns. This belief is well-founded in research — momentum is real and has persisted across decades and asset classes. But momentum can also reverse sharply, and the algorithm can lag behind sudden shifts. If stocks fall 5% in a day, the trend-following signal might not yet be flashing “reduce exposure,” and by the time the next rebalancing happens, some damage is done.

Moreover, the algorithm does not know what you know — that a central bank is about to cut rates, or that a company is about to announce bad news. It only knows what the price is doing. In periods of rapid regime change — a shift from high inflation to deflation, or a sudden policy reversal — the algorithm can get whipsawed, selling equities right before a rally or holding them through the early stages of a crash.

The strategy also assumes that the underlying trends it is measuring will continue to drive returns going forward. If the relationship between momentum and future returns breaks down — which can happen — the fund’s performance will suffer.

Who this is for

TRND appeals to investors who believe in trend-following as a concept and who want to own a single fund that does the work of adjusting allocations automatically. It can be useful for someone who wants to stay invested in markets but does not want to own a buy-and-hold fixed allocation, and who does not have the time or inclination to rebalance manually.

It is not for investors who are price-insensitive and willing to hold the same allocation through multiple market cycles, or who believe that trend-following is a cost-inefficient way to chase returns. It is also not appropriate for someone with a very long time horizon and high risk tolerance, because the algorithm might cause them to miss gains in extended bull markets.

Advisors sometimes use TRND as a core holding for clients who are trying to reduce emotional decision-making, but who still want some adaptation to changing conditions.

How to research TRND

Begin with the fund’s prospectus and fact sheet, which explain the Trendpilot algorithm in detail and list the underlying ETFs it holds. Compare TRND’s historical returns and drawdowns to a simple 60-40 stock-bond fund and to a pure stock index fund over a full market cycle — including a bull phase, a correction, and ideally a recovery. That comparison reveals whether the dynamic rebalancing actually reduced pain in downturns and whether the drag from higher costs and trading activity offset the benefit.

Examine the fund’s turnover ratio, which shows how frequently the algorithm rebalances. High turnover means more trading costs and more activity; lower turnover suggests a more stable allocation and less transaction drag.

Finally, assess your own conviction in trend-following. If you do not believe that momentum is a useful signal, you will resent paying for the algorithm every year. If you do believe in it, TRND offers a simple way to own the strategy without managing it yourself.