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Access dataset files directly from scripts, code, or AI agents.
Each file has a stable URL (r-link) that you can use directly in scripts, apps, or AI agents. These URLs are permanent and safe to hardcode.
Start with these files — they give you everything you need to understand and access the dataset.
- 1. Fetch datapackage.json to inspect schema and resources
- 2. Download data resources listed in datapackage.json
- 3. Read README.md for full context
Data Views
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monthly-processed
Schema
| name | type | format | description |
|---|---|---|---|
| Date | date | any | |
| Price | number | Gold price in USD per troy ounce |
annual
Schema
| name | type | format |
|---|---|---|
| Date | yearmonth | default |
| Price | number | default |
monthly
Schema
| name | type | format |
|---|---|---|
| Date | yearmonth | default |
| Price | number | default |
Data Files
| File | Description | Size | Last modified | Download |
|---|---|---|---|---|
monthly-processed | 42.6 kB | 22 days ago | monthly-processed | |
annual | 2.38 kB | 22 days ago | annual | |
monthly | 35.6 kB | 22 days ago | monthly |
| Files | Size | Format | Created | Updated | License | Source |
|---|---|---|---|---|---|---|
| 3 | 80.6 kB | csv | 22 days ago | Worldbank gold prices |
FRESHNESS CHECK — gold-prices
- Repo:
gold-prices - Checked on:
2026-03-03
1. Repository Structure
Files found:
| File | Path |
|---|---|
| README | README.md |
| Data Package | datapackage.json |
| Process Script | scripts/process.py |
| Requirements | scripts/requirements.txt |
| Local Data (monthly) | data/monthly.csv |
| Local Data (annual) | data/annual.csv |
| Cached XLS (monthly) | cache/monthly.xls |
| Cached XLS (annual) | cache/annual.xls |
| Cached PDF | cache/historical_gold_prices.pdf |
2. Dataset Description
Gold Prices — Monthly and annual gold prices in USD since 1833.
Data is sourced from two upstream origins:
- Timothy Green's Historical Gold Price Table (PDF) — covers 1833–1959. Hosted at
https://nma.org/wp-content/uploads/2016/09/historic_gold_prices_1833_pres.pdf - World Bank Commodity Markets ("Pink Sheet") — covers 1960 to present. Main page:
https://www.worldbank.org/en/research/commodity-markets
The scripts/process.py script:
- Scrapes the World Bank commodity markets page with BeautifulSoup to find the current XLS download links for monthly and annual prices
- Downloads
CMO-Historical-Data-Monthly.xlsxandCMO-Historical-Data-Annual.xlsx - Downloads the historical PDF from NMA
- Merges PDF data (pre-1960) with the World Bank data (1960+) into
data/monthly.csvanddata/annual.csv
3. Local Data — Latest Dates
Scanned data/monthly.csv (2,312 rows) and data/annual.csv (192 rows).
| File | Latest Date | Latest Price (USD) |
|---|---|---|
data/monthly.csv | 2025-07 | 3,340.15 |
data/annual.csv | 2024 | 2,387.702 |
The monthly CSV is the higher-resolution dataset. Its latest entry is 2025-07 (July 2025).
4. Upstream Source Probing
Step 4a — World Bank Commodity Markets Page
- URL probed:
https://www.worldbank.org/en/research/commodity-markets - HTTP status: 200 OK
- Result: Page loaded successfully. Parsed all
<a>tags with BeautifulSoup.
Found active download links:
- Monthly prices XLSX:
https://thedocs.worldbank.org/en/doc/74e8be41ceb20fa0da750cda2f6b9e4e-0050012026/related/CMO-Historical-Data-Monthly.xlsx - Annual prices XLSX:
https://thedocs.worldbank.org/en/doc/74e8be41ceb20fa0da750cda2f6b9e4e-0050012026/related/CMO-Historical-Data-Annual.xlsx - Pink Sheet PDF:
https://thedocs.worldbank.org/en/doc/74e8be41ceb20fa0da750cda2f6b9e4e-0050012026/related/CMO-Pink-Sheet-February-2026.pdf
The document ID 74e8be41ceb20fa0da750cda2f6b9e4e-0050012026 and the Pink Sheet PDF filename both indicate the February 2026 release.
Step 4b — Direct HEAD request on Monthly XLSX
- URL:
https://thedocs.worldbank.org/en/doc/74e8be41ceb20fa0da750cda2f6b9e4e-0050012026/related/CMO-Historical-Data-Monthly.xlsx - HTTP status from HEAD: 404 (CloudFront CDN quirk — the file requires a standard GET with a User-Agent)
- HTTP status from GET with User-Agent: 200 OK — downloaded successfully (779 KB
.xlsxfile)
Step 4c — Parsing the Monthly XLSX
Downloaded and opened the XLSX with openpyxl. Sheet structure:
AFOSHEET(metadata)Monthly Prices← gold price data hereMonthly IndicesDescriptionIndex Weights
Key metadata from Monthly Prices sheet:
- Row 3:
"Updated on February 03, 2026" - Row 4: Column headers; Gold is at column index 69
Scanned all data rows (format YYYYMNN). Last 10 rows with Gold prices:
| Date (upstream) | Gold Price (USD/troy oz) |
|---|---|
| 2025M04 | 3,217.64 |
| 2025M05 | 3,309.49 |
| 2025M06 | 3,352.66 |
| 2025M07 | 3,340.15 |
| 2025M08 | 3,368.03 |
| 2025M09 | 3,667.68 |
| 2025M10 | 4,058.33 |
| 2025M11 | 4,087.19 |
| 2025M12 | 4,309.23 |
| 2026M01 | 4,752.75 |
Latest upstream date with gold data: January 2026 (2026-01)
5. Comparison
| Monthly CSV | Annual CSV | Upstream (World Bank) | |
|---|---|---|---|
| Latest date | 2025-07 | 2024 | 2026-01 |
| Gap | 6 months behind | — | — |
The local monthly dataset is 6 months behind the upstream World Bank data.
Missing months in local data: 2025-08, 2025-09, 2025-10, 2025-11, 2025-12, 2026-01
6. Verdict
The dataset IS STALE.
- Latest local date:
2025-07 - Latest upstream date:
2026-01 - Staleness: 6 months behind (missing 6 monthly data points)
- The World Bank Pink Sheet was last updated on February 3, 2026 and contains gold price data through January 2026.
- Running
python scripts/process.pywould refresh the data to the current upstream state.