Which means: don’t do anything rash, but keep an eye out. Ten straight trading days below the 75 threshold and it turns red.
P.S. I haven’t posted lately, because I have had no brilliant ideas. I might get brilliant again in the future though. Or at least have an idea.
05/31/ 16 update: it’s back to yellow.
The indicator has turned red. Which might seem strange since it was such a big ‘up’ day today. But none the less, we have had ten days where the breadth diffusion indicator has been below the 75 threshold.
The good news is that breadth seems to be improving. So it might turn yellow and then green again over the next month…who knows?
The “rules” of this “trading system” ** say buy at the next open after the first green day, and sell at the next open after the first red day, which is tomorrow. So I don’t know what the official return would be. But if you’d sold at today’s close, you’d be up a paltry 1.78% from the April 12th open. Which beats a kick in the teeth I suppose.
** not really a trading system. This is just a general market health indicator.
For the first time since developing The Indicator, it has turned green. FWIW.
Matt’s Breadth Indicator (as I like to call it) has turned yellow for the first time since I developed it. We have been solidly in ‘red’ territory since May 2015. What does yellow mean? It means maintain the status quo (in this case, stay out of the market). Ten days above the threshold in a row and we move to ‘green’ territory. Stay tuned, fingers crossed…
Read more here.
So in this last post, I data-mined the hell out of the S&P500 index (well ok SPY) and found an “anomaly”: every time SPY drops more than 1% from the previous close to the current close, you wait (that’s Day 0). You then buy at the close 13 days later, and sell at the close of Day 14. This showed significantly better return than if you did the same thing but owned all the Day 16s instead. Here’s the graph from the last post.
But with only 177 samples of data between 2010-2015, that’s probably just a fluke….right?
Continue reading Data Mining vs Out of Sample Data