Revised 19 October 2026 · v1.0.0 · what changed

Code: github.com/herrrickshaw/chemical_import_substitution

Watch the walkthrough

Watch on YouTube: youtu.be/8c9sOTgIJRE

Why it is useful

• Industrial-policy analysts and investors scanning for import-substitution gaps get a pre-sorted list of 827 chemical HS-8 lines instead of eight raw TRADESTAT chapter downloads.
• It replaces a manual rule of thumb with explicit signals: A scissors (value and volume falling, price rising), B inelastic target, D deepening, E no clear signal.
• Evidence from the run: the committed results show plastics (ch. 39), organic chemicals (ch. 29), fertilisers (ch. 31) and inorganic chemicals (ch. 28) carry $59.3 bn of the $67.9 bn deficit, and 59.6% of codes have no clear one-year signal, a useful reminder of how little one year of data can say.
• The top lines are fertilisers and precious-metal compounds, which the repo's own rationale tags call structurally import-dependent rather than substitutable.

What it is for

The repo has one analysis script (scripts/hsn8_signal_analysis.py) and its output (data/chem_hsn8_signals.json, plus an HTML registry of codes). For each HS-8 code in chapters 28, 29, 31, 32, 33, 34, 38 and 39 with an FY2025-26 import-minus-export gap of at least $0.5 million, it computes value, volume and unit-price growth and assigns a signal and a keyword-based rationale tag.

The script needs the raw TRADESTAT Excel downloads, which are not in the repo, so this post reads the committed result file.

Who it is for

• Industrial-policy and PLI analysts
• Chemical-sector researchers and equity analysts looking for gaps
• Students of import substitution
• Developers wanting a worked HS-8 classification example

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/chemical_import_substitution.git
cd chemical_import_substitution
claude      # or: gemini

Install

1. Clone the repository

   git clone https://github.com/herrrickshaw/chemical_import_substitution.git
   cd chemical_import_substitution

2. Optional, for re-running the script

   python3 -m venv .venv && source .venv/bin/activate
   pip install openpyxl

Quick start: commands and what you should see

Summarise the committed results by chapter and signal

python3 - <<'E'
import json
from collections import Counter
d=json.load(open('data/chem_hsn8_signals.json'))
for ch,v in sorted(d['by_chapter'].items()):
    print(ch, d['ch_names'][ch], len(v), round(sum(r['deficit_fy26'] for r in v)/1e3,1), dict(Counter(r['is_signal'][0] for r in v)))
E

One line per chapter: code count, deficit in US$ billion and a count of signal letters.

List the active-substitution (scissors) codes

python3 - <<'E'
import json
d=json.load(open('data/chem_hsn8_signals.json'))
r=[x for v in d['by_chapter'].values() for x in v if x['is_signal']=='A_SCISSORS']
for x in sorted(r,key=lambda x:-x['deficit_fy26'])[:5]: print(x['hs'],x['name'][:40],x['imp_fy25'],x['imp_fy26'],x['qty_growth_pct'],x['price_growth_pct'])
E

Five HS-8 codes with falling value and volume and rising price.

Sample outputs

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

HS-chapter substitution table (FY2025-26, US$ billion)

ch  name                                   codes  imports  exports  deficit  signals (A/B/C/D/E)
28  Inorganic Chemicals & Fert. Intermed.    113   12.75    0.72    12.03   5/7/0/36/65
29  Organic Chemicals                        305   19.73    4.07    15.66   11/16/16/66/196
31  Fertilisers                               20   14.58    0.15    14.43   0/3/0/9/8
32  Dyes, Pigments, Tanning & Colouring       59    2.32    0.42     1.90   1/3/0/16/39
33  Essential Oils, Cosmetics, Perfumery      28    0.89    0.40     0.49   2/2/0/11/13
34  Soaps, Waxes, Polishes, Surfactants       24    1.13    0.27     0.86   3/3/0/4/14
38  Miscellaneous Chemical Products           77    7.25    1.93     5.32   5/11/9/15/37
39  Plastics & Articles Thereof              201   20.90    3.69    17.21   7/21/0/52/121

Totals over the 827 qualifying codes (FY2025-26 import minus export of at least $0.5 million). Signal letters: A scissors, B inelastic target, C insufficient data, D deepening, E no clear signal.

Largest deficits by HS-8 code (US$ million)

31053000 Diammonium hydrogen orthophosphate (DAP)   imports 4871.6  deficit 4867.4  D_DEEPENING
28433000 Gold compounds                              imports 4012.3  deficit 4012.2  B_INELASTIC_TARGET
31021010 Fertiliser grade, HS 3102 10 10           imports 3868.9  deficit 3868.8  D_DEEPENING
28092010 Phosphoric acid                             imports 2674.2  deficit 2666.0  E_NO_CLEAR_SIGNAL
39041020 Suspension-grade PVC resin                  imports 1632.6  deficit 1632.1  E_NO_CLEAR_SIGNAL

Names are cleaned up slightly from TRADESTAT's own spellings, which contain typos such as 'DIAMONM'.

Scissors signal examples

38231900 industrial monocarboxylic fatty acids  imports 659.9 -> 548.9  qty -24%  price +9.5%
34011920 soap flakes, chips and powder           imports 141.3 ->  44.8  qty -71%  price +8.5%
28258000 antimony oxides                          imports  76.4 ->  68.1  qty -60%  price +122.9%

A falling quantity with a rising price is the script's marker of substitution; it can equally reflect supply shocks or a price spike.

Ways to use it

Sample prompts for your AI assistant

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

• Load data/chem_hsn8_signals.json and list the top 15 B_INELASTIC_TARGET codes by FY2025-26 deficit, with the rationale text for each.
• Explain the signal definitions in scripts/hsn8_signal_analysis.py and what each one can and cannot tell a policy analyst.
• Group the D_DEEPENING codes by chapter and show the five largest per chapter. Which look like feedstock dependence?
• Which scissors (A) codes might be false positives caused by price spikes? Suggest a check using a second fiscal year.
• Draft a methodology note for a report using this file, listing the limits stated in the script docstring.

Limits and honest caveats

Sources: the chemical_import_substitution scripts/hsn8_signal_analysis.py docstring and data/chem_hsn8_signals.json (DGCI&S TRADESTAT FY2024-25 and FY2025-26); Python 3 standard-library queries run on 19 October 2026 on the committed files. The script itself was not run.

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.