Revised 19 October 2026 · v1.0.0 · what changed

Code: github.com/herrrickshaw/india-commodity-trade-deficit

Watch the walkthrough

Watch on YouTube: youtu.be/MVZiBaQ_nFo

Why it is useful

• Economists, journalists and students who need the commodity leg of India's trade gap get 8 years of chapter-level exports and imports in two small JSON files, with no portal scraping.
• It replaces manual TRADESTAT downloads. The 4-digit file splits bullion out of chapter 71, which the 2-digit data cannot do (gold 7108 and silver 7106 separately).
• Evidence from the run: the totals reproduce the README headline (deficit widening from -$184.0 bn to -$334.3 bn), and a one-screen script ranks chapters by deficit: mineral fuels, then precious stones and metals, then electrical machinery.
• A useful catch: silver imports rose from $4.8 bn to $12.1 bn (+150%) in one year, while platinum-group imports fell 92%, which looks like trade moving between customs lines, not demand disappearing.

What it is for

The repo isolates the commodity part of India's merchandise trade deficit using DGCI&S TRADESTAT figures (US$ million). data/ holds the chapter-level export/import file and the 4-digit bare-metal file with country breakdowns, plus a validation file on listed metal recyclers. blog/ holds a finished HTML post on the recycling counter-force.

This post uses only the two TRADESTAT files, read with the Python standard library.

Who it is for

• Trade and macro analysts
• Journalists writing on gold, silver and copper imports
• Students looking for a clean, citable trade dataset
• Researchers testing the recycling-substitution argument

What you need

Get the code and an AI assistant

You do not have to be a programmer. Get the repository, give it to an AI assistant, and ask in plain language. Any of these routes works:

git clone https://github.com/herrrickshaw/india-commodity-trade-deficit.git
cd india-commodity-trade-deficit
claude      # or: gemini

Install

1. Clone the repository

   git clone https://github.com/herrrickshaw/india-commodity-trade-deficit.git
   cd india-commodity-trade-deficit

Quick start: commands and what you should see

Print the trade balance by fiscal year

python3 - <<'E'
import json
d=json.load(open('data/tradestat_hsn_export_import_2018-19_to_2025-26.json'))
t=d['totals']
for y in t['import']:
    print(y, round(t['import'][y]/1e3,1), round(t['export'][y]/1e3,1), round((t['export'][y]-t['import'][y])/1e3,1))
E

Eight rows of year, imports, exports and balance in US$ billion.

Rank chapters by FY2025-26 trade balance

python3 - <<'E'
import json
d=json.load(open('data/tradestat_hsn_export_import_2018-19_to_2025-26.json'))
i,e=d['by_chapter']['import'],d['by_chapter']['export']
y='2025 - 2026'
r=sorted((e.get(c,{}).get(y,0)-v[y],c,v['name'][:40]) for c,v in i.items())
for b,c,n in r[:8]: print(c,n,round(b/1e3,1))
E

The eight chapters with the largest deficits; see sample outputs.

Sample outputs

Real output from a run on the date shown in the sources line; trimmed to fit.

Trade balance by fiscal year (US$ billion)

FY          imports  exports  balance
2018-19      514.1    330.1   -184.0
2019-20      474.7    313.4   -161.3
2020-21      394.4    291.8   -102.6
2021-22      613.1    422.0   -191.0
2022-23      716.0    451.1   -264.9
2023-24      678.2    437.1   -241.1
2024-25      721.2    437.7   -283.5
2025-26      776.0    441.7   -334.3  (provisional)

Computed from the 'totals' block of the chapter file.

Chapter deficit table, FY2025-26 (US$ billion)

ch  name                                     imports exports balance
27  Mineral fuels, mineral oils              203.4    55.9   -147.5
71  Pearls, precious/semi-precious stones+metals 109.4  28.3    -81.1
85  Electrical machinery and equipment       104.9    54.0    -50.9
84  Nuclear reactors, boilers, machinery      74.0    36.6    -37.4
15  Animal or vegetable fats and oils         19.8     2.2    -17.5
39  Plastic and articles thereof              22.2     7.6    -14.6
31  Fertilisers                               14.6     0.2    -14.4
88  Aircraft, spacecraft and parts            13.8     1.7    -12.1

Chapter 27 plus 71 are 40.3% of FY2025-26 imports in my calculation; chapter 71 alone supplied $20.5 bn of the $54.8 bn rise in total imports (37%).

4-digit bare-metal imports (US$ billion)

hs    item                       FY25   FY26   change  share of imports FY26
7108  Gold, unwrought/semi-mfd   58.01  71.98  +24%    9.28%
7106  Silver                      4.83  12.05 +150%    1.55%
7110  Platinum group metals       5.42   0.42  -92%    0.05%
2603  Copper ores+concentrates    3.85   5.94  +54%    0.77%
7404  Copper waste and scrap      2.38   3.43  +44%    0.44%
7602  Aluminium waste and scrap   3.93   4.55  +16%    0.59%

From data/metals_hsn4_import_2024-25_to_2025-26.json (14 codes in total; trimmed here).

Ways to use it

Sample prompts for your AI assistant

Copy, paste and edit the parts in [brackets].

• Load data/tradestat_hsn_export_import_2018-19_to_2025-26.json and chart the trade balance by year, with chapters 27 and 71 shown separately.
• Which chapters contributed most to the rise in imports between FY2024-25 and FY2025-26? Show a table and explain the top three.
• From the 4-digit metals file, show gold imports by partner country for both years and flag the biggest changes.
• Compare silver (7106) and platinum group metals (7110) import values across the two years and suggest, with caveats, why they moved in opposite directions.
• Read the README and tell me which numbers in the headline table I can reproduce from the JSON files and which I cannot.

Limits and honest caveats

Sources: the india-commodity-trade-deficit README and data/ JSON files (TRADESTAT / DGCI&S via the Commerce Ministry); queries run with Python 3 standard library on 19 October 2026 on the committed files.

About this article: Researched, written and edited by Umashankar Triplicane Dwarakanathan, with AI research assistance; every figure is meant to trace to the primary source cited. See the Editorial Policy for how sourcing, AI use and corrections work.