Does the Market Really Reverse on Perihelion and Aphelion? We Checked 76 Years of Data

The Conclusion

Everything below is how we got there — the definitions, the base rates, the controls, and an interactive table of all 153 events where you can open the chart for every date and check us.

The Claim

Perihelion is the point in Earth's orbit closest to the Sun. It arrives every year between January 2 and January 5. Aphelion is the farthest point, arriving between July 3 and July 6. The idea circulating in astro-trading circles is simple: these two astronomical extremes mark market turning points, and if you look back over the last decade you will find a reversal on every single one.

This is exactly the kind of claim we like, because it is checkable. It names specific dates, a specific market behavior, and a specific lookback window. So we checked it — not just for the last 10 years, but for all 76 years of daily S&P 500 history from 1950 to 2026.

One methodological note up front: we did not assume perihelion is “always January 4.” The exact date drifts from year to year, so we computed the true Earth–Sun distance minimum and maximum for every year from an ephemeris, then snapped each one to the nearest S&P 500 trading day. That matters more than you would think — aphelion has a habit of landing on July 4th, when the market is closed.

Test 1: The Claim as Stated — and It Passes

First we tested the claim the way its proponents likely check it: call it a reversal if the market puts in a local swing high or swing low (the highest or lowest close of a ±3-day window) within two trading days of the event.

By that standard, the last decade looks stunning. Perihelion “hit” in 10 of 11 years. Aphelion hit in 9 of 10. If you scrolled through charts checking these dates by eye, you would walk away a believer.

So the observation is honest. Anyone who looked at those dates would genuinely see reversals on almost all of them. The problem is not the observation — it is the control group nobody ran.

The Full 76-Year Record

Extending the same loose test across every year from 1950 gives the events far more chances to prove themselves. They don't.

Nothing here clears any conventional significance threshold. The mild perihelion lean (81.6% vs. 73.7%, p = 0.07) is the most interesting number in the table, and even it has a mundane explanation we will get to below. Meanwhile, on the exact astronomical day with a stricter swing definition, aphelion performs worse than a random date.

Check Every Event Yourself: All 153 Dates Since 1950

Don't take the aggregates on faith. Below is every perihelion and aphelion since 1950 with its exact astronomical date, whether a 1.5% reversal started within the chosen orb (exact day, ±1 or ±2 trading days), and a chart for each one — click any row to see what the S&P 500 actually did around that date. Whatever orb you pick, compare the hit rate chip against the random-day base rate next to it: that gap, not the raw count of hits, is the entire question. The 1.5% reversal definition is spelled out in the next section.

Test 2: Require the Reversal to Mean Something

A reversal nobody could trade is not a reversal worth predicting. So we tightened the definition: a day counts as a reversal only if price moved at least 1.5% against the direction of the prior five days, within the next five trading days. No requirement on the size of the move into the day — just a tradeable counter-move out of it. On the S&P 500, 28.4% of all trading days qualify (5,462 of 19,242).

With a definition that actually filters, the two events go separate ways — aphelion collapses, and perihelion shows a lean that turns out to have nothing to do with the Sun.

Aphelion is finished at this point: on its exact day it reverses less often than a random date (27.6% vs. 28.4%), and no window separates it from chance (best p-value ≈ 0.10). In the last decade exactly one aphelion — July 2022 — saw a 1.5% counter-move begin on the day itself.

Perihelion is more interesting. Its exact-day rate of 38.2% beats the 28.4% base rate with p = 0.04 — the one result in this study that looks like a real edge. But before crediting the Sun, run one more control: how does any trading day in the January 2–6 window score, perihelion or not? Answer: 31.7%. The turn of the year is simply a reversal-prone neighborhood — new-year flows, rebalancing, the January effect. Against its own calendar neighborhood instead of the all-day base rate, perihelion's 38.2% is no longer statistically distinguishable (p ≈ 0.14). The early-July control days score 30.0%, and aphelion sits below even that.

The last decade — the window the claim was actually made about — looks like this on the exact day:

What About Trading Only When the Cycle Is Hot?

Fair question — our platform applies exactly this idea to seasonal patterns: fit a cycle to the strategy's equity curve, call the rising half of the sine wave HOT and the falling half COLD, and only trade the hot phases. So we ran the perihelion/aphelion idea through the same engine (Lomb-Scargle spectral analysis on the HP-detrended equity curve — the identical code that powers the app).

First the idea needs a testable rule. The natural one from this study: at every event, the backtest fades the prior five-day move — long when the market fell into the date, short when it rose — exiting after five trading days. That produces 152 simulated trades since 1950. Baseline result: a 51.3% win rate and +24.1% total return over 76 years. For scale, buy-and-hold returned roughly +44,000% over the same period. There is nothing here — but let's filter it anyway. The fitted cycle has a dominant period of about 26 events (≈13 years), and the regime comes from the slope of the fitted sine wave.

That looks like a rescue: the hot phases nearly triple the per-trade average, the cold phases lose money, and the drawdown halves. (For the record, the fitted wave says the pattern is COLD right now, flipping HOT for the next events at under 50% confidence.) But this table was produced by fitting the sine wave to the entire equity curve and then grading each trade with hindsight — the fit already knows where the good and bad stretches were. Even with that advantage, the hot/cold win-rate split does not reach significance (p = 0.06). The honest version is walk-forward: refit the cycle before every trade using only the data available at the time, counting a trade only when the forecast said HOT.

Most of the magic evaporates. Hot phases still edge out cold ones (58.6% vs. 48.4% win rate), but on 58-vs-64 trades that gap is comfortably inside noise (p = 0.13) — and COLD stops being a money-loser, which was the whole selling point of the in-sample table.

All figures on this page are historical backtest statistics published for research purposes only — nothing here is trading advice or a recommendation to buy or sell any security.

Why the Claim Feels So True

The Checklist for Any “Always Reverses On” Claim

This study took an afternoon, and the recipe generalizes to every date-based market claim you will ever hear:

We build and test astronomical timing models for a living — some survive this kind of scrutiny and become strategies on our platform. This one doesn't. That distinction, between patterns that survive a base-rate test and patterns that only survive eyeballing, is the entire game.

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