Planetary MAs

markd

W.D. Gann believed the movement of planets influenced prices (see Gann entries in Wikipedia). Maybe or maybe not, but I’ve noticed prices do sometimes seem to be sensitive to Earth-based calendar intervals, like a month (about 21 trading days), quarter (about 63 trading days) and year (about 251 trading days).

NOTE: NYSE publishes the actual number of trading days per interval here:

https://www.nyse.com/publicdocs/Trading_Days.pdf

So, I thought it would be fun to see if MAs based on full or fractional orbit intervals of other planets might be useful for planning entries or exits (or go/no go signals) in the major stock indexes. Here’s a 100-year MONTHLY chart of the SP500 index with MAs based on a Jupiter quarter orbit (36 months - blue), a Jupiter annual orbit (142 months – red) and a Uranus quarter orbit (252 months – green).

https://stockcharts.com/h-sc/ui?s=$SPX&p=M&yr=100&mn=0&dy=0&id=p49500299785

Judge for yourself. Maybe other combinations work better. Also, there may be numbers or combinations that seem to be effective on daily or weekly charts. I haven't looked. I'm guessing that if they work at all, it would be on indexes, ETFs or large cap stocks that are most influenced by the course of the economy as a whole, rather than small caps most influenced by their individual prospects. But that's just a guess.

If you would like to play with this idea, the numbers for each planet’s partial and full orbits are shown below, in Earth days/weeks/months, adjusted for weekends and holidays. Note that the MA length limit on Stockcharts is 600 bars, so the outer planet numbers cannot be plotted here.

Raw data for planetary orbit durations are here:

https://nineplanets.org/solar-system/

The adjustment math is a little complicated.

For the number of Earth days in a planetary month: divide the annual orbit by 12

Adjust for weekends: For the number of trading days in a planetary month: divide the month by 7 (days in an Earth week), multiply the quotient by 2 (number of days in a weekend), subtract the result from the number of days in the planetary month.

Adjust for holidays: If the result after adjusting for weekends is greater than 21, we have to account for holidays, so multiply the weekend adjusted number by .75 (9 holidays in 12 months 9/12 = .75).

For example:
Mars:

Orbit: 687 earth days

Mars month: 687/12 = 57.25

Adjust for weekends: 57.25/7 = 8.18 weeks x 2 weekend days = 16.36 weekend days from 57.25 days = 40.89 ~ 41 days

Adjust for holidays: 41/21 = 1.94 x .75 =1.46 from 40.89 = 39.43 ~ 39 trading days in 1 Mars month

For Earth weeks in a month, divide the month in days figure by 5; for months, divide by 21.

To get quarterly figures, multiply the adjusted month by 3; for annual, multiply by 12.

lmkwin

I wonder if the position of the Cycle might play a vitally important part of the analysis. Likely they all don't "start" at the same time. To get the YTD performance numbers for a Mars cycle, would the Mars cycle start on January 1st like the Earth YTD cycle would or somewhere different? Or did Gann's Earth cycle line up with Seasons? Fascinating stuff.

https://library.cryptotradercentral.com/Victor_Ledeboer_-_Master_of_Time.pdf

markd

Thanks for the link.

Not sure about identifying the start/stop point of the cycles. If you apply price channels to individual symbols, you see that different symbols respect a particular channel length (e.g. 21 days) on different dates, so that there are channel intersections of different periods (21, 63, 251, for instance) every day for the shorter lengths, and often, but not quite so often, for the longer lengths. You would expect more clustering, especially for the longer lengths if there were a controlling external "force". It seems more likely there is some psychological effect at work - humans need reward within some time frame, and when they form a crowd around some symbol, their concerted actions influence price. In other words, its not the movement of planets in real time, but the intervals baked into our psyches as a result of evolving in an environment influenced by those planets (speculating wildly here). Larger institutional stocks in similar established industries seem more likely to move together, while newer, smaller or in some way more unique symbols have their own rhythms, each reflecting the characteristics of the crowd that trades them.