Archived MoonshotScore snapshot: coverage, versioning and data quality (August 2026)

Correction — September 12, 2026: This article previously described high and low MoonshotScores as the "safest" and "riskiest" stocks. That interpretation was not supported by the snapshot. It also treated an extreme raw price-change value as a market event. Those claims have been removed. The analysis below describes an archived dataset and its limitations.

A stock score is not a probability of losing money. A useful data pipeline needs to preserve that distinction all the way from an API response to a published article.

We rechecked Stock Expert AI's archived snapshot from August 29, 2026. It contains 6,213 distinct instrument records, of which 4,675 have a positive value in the legacy moonshotScore field. The median of those positive legacy values is 47; the minimum is 34 and the maximum is 74. These are descriptive statistics about the stored records, not a ranking of investment safety.

What the snapshot contains

Check	Result
Instrument records in the archive	6,213
Records with a positive legacy moonshotScore	4,675
Remaining records	1,538
Median positive legacy score	47
Mean positive legacy score, rounded to two decimals	47.96
Minimum / maximum positive legacy score	34 / 74
Records with a numeric moonshotScoreV2 field	786

The archive was fetched at 20:20:02 UTC on August 29, 2026. Its collection log records 66 page requests and marks the run as completed. Completion of that retrieval does not establish that every instrument on the platform, or every US-listed stock, was captured. The endpoint's ordering can change during pagination; the collector deduplicates by ticker, but deduplication cannot recover records missed while pages shift.

The 1,538 remaining records are not automatically "bad stocks." They simply do not meet this article's positive-legacy-score filter. Missing scores, zero values and instrument eligibility need separate treatment.

Keep score versions separate

The snapshot holds both moonshotScore and moonshotScoreV2. This article's summary statistics use only positive numeric values in the legacy field. The 786 numeric V2 records are reported separately and are not mixed into those statistics. Numeric presence alone does not establish current eligibility, freshness or comparability.

The platform's current scoring methodology describes a sector-relative, five-pillar system. It is the reference for understanding today's score, not evidence that an older stored value was calculated using the same version. Historical comparisons need the engine version, calculation time and eligible instrument set alongside the score.

Outliers need investigation before interpretation

The original article repeated an implausible raw daily percentage change and described it as volatility. We have withdrawn that interpretation. A single stored percentage is insufficient to distinguish a real return from a unit error, stale quote, tiny denominator or corporate-action adjustment.

We therefore omit daily-return rankings, price charts and sector-level investment conclusions from this correction. The selected sector labels in the original package were not an independently validated classification of the full captured dataset.

Reproducing the summary

This JavaScript calculation shows the exact selection and aggregation logic. snapshot refers to our archived JSON, not the current API response; fetching today's API cannot reproduce an August snapshot.

const rows = snapshot.stocks;
const scores = rows
  .map(row => row.moonshotScore)
  .filter(value => typeof value === 'number' && Number.isFinite(value) && value > 0)
  .sort((a, b) => a - b);

const middle = Math.floor(scores.length / 2);
const median = scores.length % 2
  ? scores[middle]
  : (scores[middle - 1] + scores[middle]) / 2;

console.log({
  records: rows.length,
  scored: scores.length,
  median,
  mean: scores.reduce((sum, value) => sum + value, 0) / scores.length,
  minimum: scores[0],
  maximum: scores.at(-1)
});

The archive is retained internally with the correction record. This article does not offer a public raw-data download, so readers should treat the table as a publisher-reported summary, not an independently reproduced benchmark. We will only describe a dataset as publicly reproducible when its underlying data and usage terms are actually available at a working public URL.

Public aggregate audit

The expanded stock-score audit on Stock Expert AI publishes the complete aggregate score frequencies and JavaScript to reconstruct the count, mean and median from those frequencies. It also separates snapshot coverage, model version and analysis date. These checks reproduce the aggregate arithmetic; they do not independently validate the original instrument records or source values.

What to take away

For developers building a financial-data publishing pipeline, three checks matter here: label the score version, measure collection coverage without equating it to the whole market, and investigate outliers before writing a story around them. A completed job or a valid JSON response cannot substitute for those checks.

Stock Expert AI is an educational stock-research platform. Its methodology explains the current scoring model and its limits. This archived snapshot is not a recommendation to buy or sell any instrument and does not validate future performance.

Disclosure: Published by Stock Expert AI. This correction was prepared with AI assistance and calculations against the archived dataset.