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Revised 22 October 2026 · v1.0.0 · what changed

Code: github.com/herrrickshaw/global-ticker-universe

Watch the walkthrough

Watch on YouTube: youtu.be/hnP2Q4nRgdE

Why it is useful

• Who benefits: anyone who needs a list of tradable symbols per country before downloading prices or fundamentals, such as students, analysts and hobby quants.
• What it replaces: scraping each exchange website or hand-typing watch lists. One call, tickers_for('JP'), returns the Japan list.
• Symbols carry the exchange: India comes as RELIANCE.NS or .BO, Japan as 7203.T, Germany as SAP.DE. In the run, 7203.T was found in JP only, 005930.KS in KR only and SAP.DE in DE only, so the suffix tells you the venue.
• Evidence from the run: the helper loaded 74,239 symbols across 34 markets in well under a second, all unique, and the 16 bundled tests passed.

What it is for

The repository stores one JSON file, data/global_universe.json, with a list of Yahoo-Finance-style symbols per market, plus a helper module, global_tickers.py. You import the helper and ask for a market, several markets, or everything.

It is a universe list, not a price or fundamentals source. Use it as the input to a screener or data collector. Not investment advice: this is research and education material built from public-source or derived data; it is not a recommendation to buy or sell anything.

Who it is for

• Students and analysts building multi-country screens
• Data engineers who need a seed list for a price or fundamentals collector
• Non-programmers who want an AI assistant to filter the CSV for them

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/global-ticker-universe.git
cd global-ticker-universe
claude      # or: gemini

Install

1. Get the code

   git clone https://github.com/herrrickshaw/global-ticker-universe.git
   cd global-ticker-universe

2. Optional: an environment for the tests

   python3 -m venv .venv && source .venv/bin/activate
   pip install -r requirements-dev.txt

Quick start: commands and what you should see

Print the market summary table

python3 global_tickers.py

A table of market code, exchange and ticker count, then the first five tickers for India, the US and Japan.

Pull symbols in your own script

from global_tickers import tickers_for, market_codes
print(market_codes()[:5])
print(tickers_for('JP')[:3])

['IN', 'US', 'JP', 'KR', 'AU'] and ['1301.T', '1332.T', '1333.T'].

Run the tests

python3 -m pytest -q

16 passed.

Sample outputs

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

Market summary (first rows and total)

python3 global_tickers.py
Code  Market                              Exchange                   Tickers
IN    India                               NSE/BSE                      5,981
US    United States                       NYSE/NASDAQ/AMEX             7,726
JP    Japan                               TSE                          3,618
KR    South Korea                         KRX                          2,606
DE    Germany                             Xetra + Frankfurt           17,121
AT    Austria                             Vienna Stock Exchange        8,813
TOTAL 34 markets                                                      74,239

Trimmed to six of the 34 rows.

Which market does a symbol belong to?

for q in ('7203.T','005930.KS','RELIANCE.NS','SAP.DE'):
    print(q, [c for c in market_codes() if q in tickers_for(c)])
7203.T ['JP']
005930.KS ['KR']
RELIANCE.NS ['IN']
SAP.DE ['DE']

Exchange-qualified symbols resolve to exactly one market.

Suffix mix inside a market

Counter of symbol suffixes per market
IN: .NS 3,247 and .BO 2,734
DE: .F 16,080 and .DE 1,041
UK: .L 2,713
JP: .T 3,618

India is listed on both NSE and BSE, so some companies appear twice (once per exchange).

Asking for an unknown market

tickers_for('XX')
ValueError: Unknown market 'XX'. Run universe_builder.py first.

A clear error rather than an empty list.

Ways to use it

Sample prompts for your AI assistant

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

• Claude: Read global_tickers.py and data/global_universe.json in this repo, then list every market code with its ticker count and exchange.
• Gemini: Using tickers_for from global_tickers.py, write a script that saves all German and Austrian symbols to a CSV, one column per suffix.
• ChatGPT: I uploaded validated_universe_flat.csv. How many Indian symbols end in .NS and how many in .BO? Show a small table.
• GitHub (Copilot Chat): Add a function that returns symbols for a market filtered by suffix, with a test in tests/test_global_tickers.py.
• Claude: Which markets in global_universe.json look partly covered, based on ticker counts versus the real exchange size? Explain how you decided.

Limits and honest caveats

Sources: Ran global_tickers.py, a symbol lookup script and pytest from a fresh clone of github.com/herrrickshaw/global-ticker-universe (commit 6bcdbe5) in a throwaway virtualenv on 19 October 2026. README and data files as committed. No network calls.

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.