Trend-Momo Bull-Bear ETF Strategy (part 9) Update – Impulse vs In-State Signals, Understanding Entries, Leaders at the Start of a Bull Market

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This is an update to the Trend-Momentum strategy that trades the All Weather ETF List. This article starts with a review of the strategy and the importance of the All Weather ETF list. We then examine some signals to understand the methodology and update performance. I expanded on some key points so there is something new, even for those who have read the prior articles.

First introduced in February 2022, this strategy benefits from bull markets by focusing on stock-based ETFs when the Composite Breadth Model turns bullish. It shuns stock-based ETFs during bear markets by shifting its focus to non-stock ETFs (alternatives). The broadness of the All Weather ETF list and the ability to shift focus allow for true diversification across different asset classes.

I will add a video for this article later in August (ran out of time in July).

Trend Composite Strategy Series

Note that the Trend Composite is part of the TIP Indicator Edge Plugin for StockCharts ACP. I use Amibroker because this is where I can build the indicators, test the indicators and fully customize the chart views.

The Trend-Momo Bull-Bear Strategy with All Weather ETF List

The Trend-Momo Bull-Bear ETF Strategy uses broad market timing, ETF trend signals and ETF momentum rankings to trade ETFs in different market regimes (bull market or bear market). The strategy uses the Composite Breadth Model to define the market regime for stocks, the Trend Composite to define the trend for each ETF and StochClose to rank momentum for ETFs that are in uptrends.

The strategy trades the All Weather list of 50 ETFs, which is divided into bull market ETFs (stock-based ETFs) and bear market ETFs (non-stock ETFs). The strategy takes trend-momentum signals in any ETF during bull markets, but only takes trend-momentum signals in non-stock ETFs during bear markets. It is important to avoid signals in stock-based ETFs during bear markets because stocks are highly correlated to the broad market environment. Avoiding stock-based ETFs during bear market will help preserve capital.

ETFs that are in uptrends (positive Trend Composite) and that have the highest StochClose values are bought for the portfolio, which has 14 equal-weight positions. Note that the strategy does not sell all stock-based ETFs when the market regime turns bearish. Instead, it sells when the Trend Composite turns negative for the individual ETFs. This is the basic strategy in a nutshell. Now for the details.

The All Weather List

It is important to define our ETF universe and make sure this universe aligns with our objectives. The objective here is to emulate a trend-momentum strategy that trades a diversified basket of future contracts. As such, I want a universe that covers as many asset classes as possible using passively managed ETFs that are liquid and have a long trading history. I also want a relatively small and manageable universe, as opposed to a universe with over 100 ETFs.

The first step is to narrow the universe. ETFDB.com has some 2900 ETFs in its universe. Of these, around 2000 are equity-based. Of the 2000 equity-based ETFs, around 1500 are passively managed and 500 are actively managed. SPY, QQQ and the sector SPDRs are passively managed ETFs. The ARK ETFs, such as ARKK, ARKG, ARKF and ARKW, are actively managed ETFs, which makes them dependent on the portfolio manager’s skill or strategy. I favor passively managed ETFs because they cut out any biases the manager may have. Actively managed ETFs are only as good as the portfolio manager.

The All Weather List is a highly curated list of 50 passively managed ETFs with 37 stock-based ETFs (74%) and 13 non-stock ETFs (26%). This is a minimal list designed to cover most areas of the market and still allow for diversification. The vast majority of these ETFs have trading history back to at least 2006. HYG, UUP, FXY, IEI, DBE, DBB and DBA have data back to the first half of 2007. A long trading history is important when testing. Note that the majority of the 2900 ETFs in the ETFDB database were launched after 2011.

Of the 36 stock-based ETFs in the All Weather List, there are 6 broad market ETFs (SPY, MDY, IJR, QQQ, RSP, IWC), 10 sector SPDRs, the REIT ETF (IYR), 18 industry group ETFs and one dividend ETF (DVY). The High-Yield Bond ETF (HYG) sometimes acts more like a stock-related ETF so I decided to group it with the stock-based ETFs. Thus, 37 stock-based ETFs plus one (HYG). The other 13 stock alternatives include five bond-based ETFs (AGG, IEI, IEF, TLT, LQD), five commodity-based ETFs (DBE, GLD, SLV, DBB, DBA) and three currency ETFs (UUP, FXE, FXY).

36 Stock-Based ETFs (plus HYG):

SPY, RSP, MDY, IJR, IWC, QQQ, DVY, XLK, XLY, XLF, XLI, XLC, XLV, XLP, XLE, XLB, XLU, IYR, FDN, SOXX, IGV, ITB, XRT, KIE, KRE, ITA, PBW, IBB, IHF, IHI, XES, XOP, GDX, XME, PHO, PBJ, HYG

13 Alternative ETFs (without HYG):

AGG, IEI, IEF, TLT, LQD, GLD, SLV, DBE, DBB, DBA, FXE, FXY, UUP

The Nature of Trend-Following

Before looking at some signal examples and performance tables, note that trend-followers assume no predictive powers and do not second guess signals. Instead, we devise a strategy, take the signals and let the chips fall where they may. Some signals will capture extended trends (big profits). Many, however, will fizzle and result in losses or insignificant gains. The strategy is successful over time because it catches a few good trends that more than make up for the losses. Note that we have no idea which signals will result in losses and which will lead to big trends.

Most of the profits for this strategy come from stock-based ETFs during bull markets. The alternative ETFs are there to pick up the slack during bear markets and capture macro shifts, such as a move into commodities or bonds. Performance from October 2016 to October 2018 and from June 2020 to the fourth quarter of 2021 was driven by big gains in tech-related ETFs. In contrast, 2022 performance was driven by big moves in energy-related ETFs and the Dollar Bullish ETF.

Signal Examples

Some signal examples will help us fully understand the mechanics of this strategy. The first example shows the Energy SPDR (XLE), which is a stock-based ETF. The Trend Composite turned positive in late September, but it was not added to the portfolio because there were already 14 positions (no open spots). A position opened up in mid January and XLE was added because it had the highest StochClose value of the ETFs that were in uptrends. The market regime was also bullish at the time.

Even though the Composite Breadth Model turned negative three times after this signal and XLE is a stock-based ETF, it was not sold because the Trend Composite stayed positive. XLE fell sharply from its high in June and this seriously eroded unrealized profits. This is the nature of trend-following because signals lag and occur after sizable moves. More aggressive traders could consider taking partial profits by selling 1/3 to 1/2 of a position after a sizable gain.

The next signal example is with the Gold SPDR (GLD). The Trend Composite turned positive on November 11th, but GLD was not added because there were already 14 positions in the portfolio. A position opened up in late January because something was sold and GLD filled this position because it had the highest StochClose value of ETFs that were in uptrends and not already in the portfolio.

Notice how GLD raced above 190 (blue oval), reversed and fell back below the buy point. This example also shows when taking partial profits can lock in some gains and leave the rest to ride. The Trend Composite turned bullish on May 24th and GLD was again added because there was place in the portfolio. This signal did not last long as the Trend Composite turned negative a few weeks later (whipsaws happen).

The third example shows an extended uptrend with QQQ. These are the signals that pay for the losses, make the whipsaws tolerable and drive the profits. The Trend Composite turned bullish on 17-Apr-2020, but QQQ was not bought because the Composite Breadth Model was still bearish. Bullish signals in stock-based ETFs are ignored when the CBM is bearish. The CBM turned bullish on 29-May-2020 and QQQ was added to the portfolio the next day (June 1st) because it had one of the highest StochClose values on May 29th. QQQ was sold on January 20th, the day after the Trend Composite turned negative (54% gain).

Key Idea

The key idea here is to become fully invested in stock-based ETFs as soon as the Composite Breadth Model (Market Regime) turns bullish, provided there are enough ETFs in uptrends. As soon as the CBM turns bullish, the system fills all portfolio spots with ETFs that are currently in uptrends and have the highest StochClose values.

We can fill positions immediately using “in-state” signals, as opposed to “impulse” signals. An impulse signal is when the Trend Composite moves from negative to positive (crosses above the zero line). Similarly, an impulse signal triggers when the 5-day SMA crosses above the 200-day SMA. The signals are only active on the day of the cross (an impulse).

In-state signals, on the other hand, tell us the state of the current signal (uptrend or downtrend). The Trend Composite is above zero (uptrend) or the 5-day SMA is above the 200-day SMA. In-state signals keep the impulse signals active. The example above shows IHI with impulse signals generated by Trend Composite crosses of the zero line and in-state signals when the indicator is simply positive or negative. Traders using impulse signals would have to wait for the next cross (Trend Composite crossing from negative to positive territory).

In-state signals tell us which ETFs are in existing uptrends. This is important because leading ETFs often trigger uptrend signals before the Composite Breadth Model turns bullish (see previous example with QQQ). Their impulse signals (from downtrend to uptrend) have already passed. Traders waiting for an impulse signal would have to wait for the next cross and miss the true leaders.

Market Regime Change from Bearish to Bullish

The table below shows the 14 holdings in the hypothetical portfolio on June 2nd, 2020. The first nine positions are bear market ETFs that were bought when the Composite Breadth Model was bearish (red shading). This was a defensive portfolio with non-stock ETFs. The Composite Breadth Model turned bullish on May 29th and five ETFs were added the next trading day (green shading). These ETFs were already in uptrends (in-state signals) because there impulse signals had long passed.

Five ETFs were added the next trading day (positions 10 to 14 on table above). These ETFs were already in uptrends (in-state signals) because their impulse signals had long passed. On June 1st, the Internet ETF (FDN), Biotech ETF (IBB) , Software ETF (IGV), Nasdaq 100 ETF (QQQ) and Healthcare SPDR (XLV) were added to the portfolio because they were in uptrends and had the highest StochClose values. The table below shows the ETFs with the highest StochClose values. The green arrows show those with the highest values that were NOT already in the portfolio. GDX was not selected because there was no more room (portfolio had 14 positions after XLV was added).

The chart below shows FDN with a line on May 29th, 2020, the day the Composite Breadth Model turned positive (+1). The Trend Composite was positive (5) and StochClose was 99.80. StochClose (125,5) tells us the location of the close relative to its 125-day range (six months). Values above 90 mean price is near a six month high, while values below 10 mean price is near a six month low. The indicator is smoothed with a 5-day SMA. With a StochClose of 99.80, FDN was at or near a six month high and leading when the bull market started.

Conclusions

Stocks are the place to be during bull markets because this is when we can generate outsized returns. This is why it is important to become fully invested as soon as the bull market starts. In-state trend signals give traders the chance to choose ETFs that already triggered impulse signals and are leading. The alternative is to wait for impulse signals and gradually become fully invested.

Bear markets are another story and we are usually better off avoiding stock-based ETFs (and HYG). Alternative ETFs are an option during bear markets, but these must be in uptrends to qualify for the trend-momentum strategy. The Composite Breadth Model takes care of the Market Regime, the Trend Composite defines the existing uptrend and StochClose chooses the uptrends with the strongest momentum.

The next article will update performance for this strategy. We will look at the Compound Annual Return (CAR), the June drawdown, historical drawdowns and more. The signals are daily and can occur anytime during the week. Some of us cannot or do not want to watch the market every day so I will test delayed entries as well as entries taken on the first and last day of the trading week.

Thanks for tuning in and have a great day!

I will add a video for this article later in August (ran out of time in July).