Systematic Trading Strategies and Next Level Trading Setups
System Trader – Reports
Regretfully, I must discontinue the mean-reversion strategies. Performance does not justify the effort. The last update for these signal tables will be on Thursday, June 27th.
Today’s report will put the Zweig Breadth Thrust indicators for the various indexes to the test. My default breadth index is the S&P 1500 because it covers all bases and represents a broad swath of the US stock market. We can also generate ZBT signals using
Today’s report features the Zweig Breadth Thrust, which was developed by the late, and great, Marty Zweig. We will show how it works and look at some recent signals. Despite a long history, the indicator is missing something and we will propose a modern day alternative.
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,
Impulse signals offer the chance to catch a trend as soon as it starts, but in-state signals give traders the opportunity to add momentum to their strategy. Today’s article compares a strategy using impulse signals for the Trend Composite against one that uses in-state signals. The latter
Today’s report will dive into the market filter, which is used to define bull and bear markets. Market filters are an important part of trend-following and momentum strategies that trade stocks and stock-based ETFs. The Composite Breadth Model has not worked well over the last 15 months, but this is a relatively short timeframe in the grand scheme
This report will update the Composite Breadth Model signals and compare signals to the 5/200 cross for the S&P 500. The CBM outperforms this cross and does so with few positions (whipsaws). The CBM also performs in
This is the first article in a new strategy series that will extend over the next several weeks. We will start by defining the Trend Composite indicator and then work our way towards a systematic trading strategy. The strategy is based on signals from the Trend Composite.
This article will dive into trend following. We will start by going over some key assumptions and expectations to consider when implementing a trend-following strategy. What are realistic Win Rates and Profit/Loss ratios? Attention then turns to selecting a timeframe suitable to trend-following. I will then explain 10 trend-following indicators
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.