# Does financial astrology predict the Nifty? A 10-year walk-forward backtest

> No. Over 10 years of walk-forward tests the astrological calls were right 39% of the time, against 58% for simply predicting the market's usual drift.

Published 2026-10-27 by TRINETRA.


**Date:** 2026-10-27
**Script:** `engine/scripts/bench_astro_walkforward.py`
**Output:** `data/research/BENCH_astro_walkforward.json`
**Test period:** 2016-10-27 → 2026-10-19 · NIFTY · 58 windows · 173 graded cells

## Verdict

> **NO SKILL.** 39.3% hit rate on 28 directional calls, against 57.5% for the naive rule of
> always predicting the market's own drift.
>
> **INTERVALS ARE TOO NARROW.** The 80% band contained the outcome **44.5%** of the time.

| metric | result | target / benchmark |
|---|---|---|
| Hit rate (directional) | **39.3%** (11/28) | 57.5% naive |
| Brier | 0.2399 | 0.25 coin flip |
| **80% interval coverage** | **44.5%** | **80%** |
| Mean absolute error | 2.81 pp | — |
| Directional calls | 28 of 173 cells | — |
| NEUTRAL calls | 145 (84%) | — |

## Why this backtest is valid, and the obvious one is not

The naive version — replay 2016-2026 against `BENCH_astro.json` — would be **grading the model
on its own training data**. Those benchmark cells were computed on 1985/2000-2026 history, and
the last ten years are roughly half the episodes in every factor:

| factor | total episodes | ~in test period |
|---|---|---|
| Mars retrograde | 12 | 4 |
| Saturn retrograde | 27 | 10 |
| Venus retrograde | 17 | 7 |
| Mars-Ketu | 16 | 7 |
| Eclipse season | 57 | 21 |

Any "hit rate" from that is circular. This script instead recomputes each forecast from
**only the episodes that closed strictly before that window opened**, including a point-in-time
baseline. No future information enters any forecast — the same discipline the live scorecard has
by construction.

## What actually failed

**1. The direction calls are worse than a coin flip, though not significantly so.**
11 of 28 is 39%, but against a 50% null that is **p = 0.34** — this is consistent with noise,
not with reliable anti-skill. The honest statement is "no demonstrated skill", not "reliably
wrong". With 28 calls the test cannot distinguish those.

**2. The forecasts are heavily biased bearish, and that is where the loss comes from.**

| issued | n | hit rate |
|---|---|---|
| BEARISH | 24 | 42% |
| BULLISH | 4 | 25% |

86% of all directional calls were bearish, in a market that rose 57.5% of the time. A standing
bearish tilt against an upward-drifting index loses by construction — and the shrinkage does not
correct it, because the shrinkage is symmetric about zero while the *market* is not.

**3. Per-factor, nothing stands out above noise:**

| factor | directional calls | hit rate |
|---|---|---|
| Mars-Ketu alignment | 11 | 55% |
| Jupiter retrograde | 10 | 40% |
| Venus retrograde | 3 | 33% |
| Saturn retrograde | 3 | 0% |
| Mars retrograde | 1 | 0% |

Mars-Ketu at 55% on n=11 is the best of them and is still below the 57.5% naive rule. This is
the one factor that survived the transmission test, and walk-forward it adds nothing.

**4. The intervals are the clearest, most actionable failure.**

44.5% coverage against an 80% target is not a sample-size artifact — it is a **systematic
mis-specification**. The bands understate uncertainty by roughly a factor of two. The 3-month
check hinted at this (2 of 5 inside); 173 cells confirm it.

The cause is in `forecast._interval()`: the width is driven by `max(|edge|×0.8, skew_gap×2,
1.5)`, which is anchored to the *estimate* rather than to the dispersion of actual outcomes.
Mean absolute error is 2.81pp while the typical half-band is ~1.5pp.

## What this changes

**Already correct, and the backtest confirms it:**
- 84% of cells published NEUTRAL — the shrinkage is doing its job of refusing to call what it
  cannot support
- Brier 0.2399 is *marginally better* than a coin flip, because the probabilities stay near the
  base rate rather than making confident wrong calls

**Must change:**

| | action |
|---|---|
| Intervals too narrow by ~2× | widen `_interval()`; anchor the band to realised dispersion, not to the point estimate |
| Systematic bearish tilt | the shrinkage centres on zero EXCESS, but the asset's drift is positive — a NEUTRAL call should mean "tracks the base rate", and a bearish call must clear the drift, not just zero |
| Hit rate 39% vs 57.5% naive | the live page must show **both** numbers; a hit rate alone looks respectable and is not |

**Must NOT change:** the published estimates themselves. Shrinking them further to chase a
better score would be fitting to this sample. The forecasts are already small; the problem is
that their *uncertainty* is understated and their *sign* is tilted.

## Honest limits of this test

- **28 directional calls over 10 years.** These events are rare by design. p=0.34 against a
  coin flip means this cannot establish anti-skill, only the absence of demonstrated skill.
- **One asset (NIFTY).** The live layer forecasts six; this scores the one with the longest
  clean history.
- **Price data ends 2026-10-19**, so windows opening after ~2026-09 are ungradeable at 20d.
- **The factor DEFINITIONS were chosen with knowledge of the full history** even though their
  parameters here are point-in-time. A fully clean test would have pre-registered the factor
  set too.

## Relationship to the other astro benchmarks

| benchmark | question | result |
|---|---|---|
| `BENCH_astro` | do 7,560 factor/asset cells predict returns? | 0 survived FDR |
| `BENCH_astro_transmission` | do factors move markets through a causal chain? | 21 of 24 refuted; 1 held |
| `BENCH_astro_sectors` | does a sector move when its karaka is prominent? | 0 of 118 survived |
| **`BENCH_astro_walkforward`** | **does the shipped forecast layer beat a naive rule?** | **No — 39% vs 57.5%** |

Four independent tests, four negatives. The forecast layer was built to be falsifiable; this is
it being falsified, a week before the live record would have started producing the same answer
more slowly.


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