# The Predictive Value of the Number and Magnitude of Recent Up/Down Days: UDIDSRI

Rummaging through my bottomless “TO DO” list, I found this little comment:

# of up/dn days in period, then re-scale that using percentrank….with net % move?

An interesting way to spend Sunday afternoon, and another blog post!

After playing around with the concept for a while, I ended up creating a simple indicator that, as it turns out, is impressively good at picking out bottoms1, and has very strong 1-day and acceptable medium-term predictive power. In an attempt to come up with the most awkward-sounding acronym possible, I decided on the name “Up/Down and Intensity Day Summation Rank Indicator”, or UDIDSRI. Here’s what I did:

### The first iteration

I started out with a very simple concept:

• If the day closes up, movement = 1, otherwise movement = -1.
• Sum movement over the last 20 days.
• UDIDSRI is the % rank of today’s sum, compared to the last 50 days of sums.

The case that presents the most interest is when UDIDSRI is equal to zero (i.e. the lowest value in 50 days), and we’ll have a look at how this works further down. I felt that this indicator could be significantly improved by adding a bit of nuance. Not all down days are equal, so I thought it would be a good idea to take into account the magnitude of the moves as well as their direction.

### The second iteration

The second version of the algorithm:

• If the day closes up, movement = 1, otherwise movement = -1.
• Multiply movement by (1 +abs( return))5
• Sum the movements for the last 20 days.
• UDIDSRI is the % rank of today’s sum, compared to the last 50 days of sums.

The choice of the 5th power is completely arbitrary and un-optimized (as are the 20-day summation, and 50-day ranking) and can probably be optimized for better performance.

Here’s a chart comparing the two versions on the last few months of SPY (yellow is 1st iteration, red is 2nd):

You can clearly see that the 2nd iteration doesn’t like to stay at 0 for so long, and tends to respond a bit faster to movements. As such, the 2nd iteration gives far fewer signals, but they’re of much higher quality. I’ll be using the 2nd version for the rest of this post.

Note that this approach is completely useless for going short. The indicator hitting its maximum value provides no edge either for mean reversion or trend following.

A quick test around the globe to make sure I wasn’t curve fitting:

That turned out rather well…

Thus far we have only looked at the short-term performance of UDIDSRI. Let’s see how it does over the medium term after hitting 0:

There seems to be a period of about 30 trading days after UDIDSRI hits 0, during which you can expect above-average returns. Let’s take a look at a strategy that crudely exploits this:

• Buy SPY at close if UDIDSRI = 0.
• Use a 2% trailing stop (close-to-close) to exit.

The trailing stop makes us exit quickly if we haven’t entered at a bottom, but stays with the trend if the entry is good. Here are the stats and equity curve for this strategy applied to SPY, using \$100k per trade, without commissions or slippage:

Finally here are some trades this strategy would have taken in 2011 and early 2012:

The most significant problem with the tight trailing stop is that it exits at pullbacks instead of tops (which is particularly painful during heavy bear markets), so one easy improvement would be to add an indicator for over-extension and use that to time the exit. But I’ll leave that as homework.

All in all I’m quite satisfied with the UDIDSRI. I was really surprised at how it manages to pick bottoms with such accuracy, and I will definitely add it to the repertoire of signals that I use for swing trading.

If you want to play with the UDIDSRI yourself, I have uploaded an excel worksheet as well as the indicator and signal for MultiCharts .NET.

Footnotes
1. For SPY, UDIDSRI gave signals on both the 2002 and 2009 lowest days[]

• ##### qusma says:

Thanks Bill.

The Yahoo finance data should be sufficient. You can download it as a .csv file, open it with excel and copy it over to the spreadsheet.

• ##### Shaun Overton says:

What you’ve created is a digital filter for the 20 period Lyapunov exponent. It’s extremely common in non-linear neural networks.

Thanks for the posts. You’ve given me something to chew on.

• ##### 2013: Lessons Learned and Revisiting Some Studies : QUSMA says:

[…] original post can be found here. Performance in 2013 hasn’t been as good as in the past, but was still reasonably OK. I think […]

• ##### 2013: Lessons Learned and Revisiting Some Studies says:

[…] original post can be found here. Performance in 2013 hasn’t been as good as in the past, but was still reasonably OK. I think […]

• ##### Niels says:

Really great. Question of a newbie: Why do you use the adj. Close in the Excel-Sheet? And how do you calculate it? Thanks.

• ##### The Art of Crafting an Automated Trading Strategy - Forex Alchemy says:

[…] The WDS can predict when a price is set to bounce either lower from a high or higher from a low. This is a classic contrarian strategy designed to take the opposite direction to the latest trend when the market has temporarily bottomed or topped out. This highly relevant article further dissects the WDS, or as it was originally called, UDIDSRI. […]

• ##### Andi winata says:

Great post, thank you