The S&P 500 is battling the 200-day SMA with four crosses over the last eleven days. We are also seeing a rise in volatility as this market benchmark plunged 5.86% in nine days (18-27 October) and then surged 5.85% the last five days. With such conditions, it is a good time to step back and look for ways to filter the noise.

The Composite Breadth Model (CBM) turned negative on Monday (-1) and the weight of the evidence is now bearish for stocks. This development means risk in stocks is above average. Strategies that trade stocks and stock-based ETFs perform poorly when risk is above average

Today’s report will test the Trend Composite & Normalized ROC Strategy on stocks. We will first review the testing parameters and signals for the strategy. I will then test on stocks in the S&P 500 and three other indexes. The overall returns are positive, but the

Today’s article will show how Normalized ROC performs in a rotational trading strategy. This rotational strategy builds on the prior strategy by using the same indicators (Trend Composite, eSlope, Normalized ROC).

We are making progress. After a big hiccup on Thursday, I reworked the strategy and will cover the basics in this article. Here are some of the changes. First, the strategy trades stocks in the Russell 1000 to capture more beta. Second, I tightened the volatility filter by requiring the standard deviation to be below 50%. Third, I added price,

Sometimes what seems logical and helpful, is not and needs to be reconsidered. This is my conclusion with the sector breadth models. They are logical, and perhaps helpful at times, but they do not add value when it comes to timing trends in the sector SPDRs. A simple StochClose strategy performed better overall. This article will quantify signals for three breadth models using the sector SPDRs.

The turn of the month shows a strong bullish bias with an extremely stable equity curve that really took off the last few years. This strategy, which is only invested 38% of the time, outperformed buy and hold with a higher Compound Annual Return. Overall, the eight day percentage change at the turn of the month is positive 68% of the time for SPY. Despite strong numbers overall, February is weakest month when testing over the last twenty years, and we just happen to be in February.

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This article will explore and backtest different moving average combinations on the S&P 500 SPDR over the last twenty years. Most moving average strategies work great when SPY trends, regardless of the period settings. However, SPY (aka, the market) does not always trend and trends are not uniform. Some are short and fast, while others are long and steady. This means we need moving averages that can best adapt to different environments.