nutdnuy/webull-openapi-thai-lab — explained in plain English
Analysis updated 2026-05-18
Learn how to connect to Webull's OpenAPI using a safe test account before risking real money.
Follow step-by-step notebooks to pull stock market data and screen fundamentals.
Study a research-style workflow that turns historical stock bars into backtest-style notebooks.
Use the AI-assistant entry files to have Claude or Codex help navigate the Webull API docs.
| nutdnuy/webull-openapi-thai-lab | yashwanthadventure/brain_tumor | inbatamilan18/identification-of-tamil-dialects-using-wav2vec-2.0- | |
|---|---|---|---|
| Stars | 54 | 54 | 55 |
| Language | Jupyter Notebook | Jupyter Notebook | Jupyter Notebook |
| Setup difficulty | moderate | moderate | moderate |
| Complexity | — | 3/5 | 3/5 |
| Audience | vibe coder | researcher | researcher |
Figures from each repo's GitHub metadata at analysis time.
Needs a Webull UAT test account and Python virtual environment, but works offline first.
This project is a Thai-language guide and set of example code for learning how to use the Webull OpenAPI safely. Webull is a stock trading platform, and this repository walks a learner through creating an API key, connecting to a test account, pulling market data, previewing orders, and setting up safeguards before ever touching real trades. The material is built around Jupyter notebooks. A beginner notebook starts in an offline mode that runs without needing any credentials, so a new user can explore the basic ideas first, then switch on a live mode to call real Webull data once they are ready. Beyond that, there are separate notebooks for each topic: getting an access token, reading stock market data, screening stocks and fundamentals, checking a watchlist in read-only mode, and reviewing account, asset, and order information. There is also a set of notebooks styled after Quantopian-style research workflows, which use Webull's historical price data as the input. Safety is a strong theme throughout. The project defaults to a UAT test environment rather than a live trading account, and it warns against committing API keys, secrets, or account IDs to the repository. Placing a real order requires deliberately setting an environment variable to confirm the user understands the risk. The author is explicit that this is a learning resource, not a production trading system. The repository also includes a small set of documentation pages covering the learning path, API key setup, making the first API call, and using AI coding assistants like Claude or Codex to help work with the Webull documentation. A GitHub Actions workflow runs the research notebooks automatically and publishes results to a static dashboard page.
A Thai-language teaching kit of Jupyter notebooks for learning Webull's stock trading API safely, from getting an API key to previewing orders.
Mainly Jupyter Notebook. The stack also includes Python, Jupyter Notebook, GitHub Actions.
Setup difficulty is rated moderate, with roughly 30min to a first successful run.
Mainly vibe coder.
This repo across BitVibe Labs
Verify against the repo before relying on details.