What Programming Language Is Used in AI Berkshire?

AI Berkshire is built entirely in Python, leveraging the language’s data‑science ecosystem to power financial research pipelines.

The open‑source repository xbtlin/ai-berkshire implements a complete value‑investing research stack. Every data scraper, valuation checker, and report generator is written as a Python module, making the project accessible to data scientists and quant analysts familiar with the PyData stack.

Core Language and Architecture

The codebase is Python‑centric. All executable logic resides in .py files that use standard libraries (asyncio, json, pathlib) alongside third‑party packages for numerical computing and web automation.

Key architectural characteristics include:

• Synchronous and asynchronous patterns – Blocking I/O for local file operations and async/await for web scraping.
• Modular tool design – Individual scripts in tools/ handle discrete tasks such as valuation, screening, and scraping.
• Codex integration – Helper scripts in scripts/ generate OpenAI Codex‑compatible skill packages from Markdown definitions.

Key Python Modules and Source Files

Financial Validation Engine

The [tools/financial_rigor.py](https://github.com/xbtlin/ai-berkshire/blob/main/tools/financial_rigor.py) module contains deterministic functions for sanity‑checking financial statements.

def verify_market_cap(price, shares, reported_cap, currency=""):
    """Validate that the market‑cap computed from price × shares
    matches the reported market‑cap within a tolerance."""
    computed = price * shares
    diff_pct = abs(computed - reported_cap) / reported_cap * 100
    return diff_pct < 2.0   # passes if within 2 %

This function exemplifies the straightforward, math‑heavy Python used throughout the project.

Stock Screening Utilities

[tools/stock_screener.py](https://github.com/xbtlin/ai-berkshire/blob/main/tools/stock_screener.py) implements momentum and value grading.

def check_momentum(prices):
    """Calculate a simple momentum signal from historic price series."""
    if len(prices) < 20:
        return None
    recent = prices[-1]
    past   = prices[-20]
    return (recent - past) / past

Async Web Scraping

For data ingestion, [tools/xueqiu_scraper.py](https://github.com/xbtlin/ai-berkshire/blob/main/tools/xueqiu_scraper.py) uses Playwright via Python’s asyncio API:

async def browser_fetch_json(page, url, timeout_s=15):
    await page.goto(url, timeout=timeout_s * 1000)
    content = await page.content()
    return json.loads(content)

Workflow Automation Scripts

The [scripts/sync-codex-skills.py](https://github.com/xbtlin/ai-berkshire/blob/main/scripts/sync-codex-skills.py) and [scripts/sync-codex-prompts.py](https://github.com/xbtlin/ai-berkshire/blob/main/scripts/sync-codex-prompts.py) files parse Markdown skill definitions and emit JSON payloads suitable for OpenAI’s Codex CLI.

Why Python for Financial Research?

Python dominates quantitative finance because of its rich package ecosystem (pandas, NumPy, requests) and readability. The AI Berkshire maintainers exploit these strengths to:

• Rapidly prototype valuation algorithms without compilation overhead.
• Integrate browser automation (Playwright) and data parsing in a single runtime.
• Generate Markdown reports programmatically using standard string templating.

Summary

• AI Berkshire is a Python‑only repository – every tool, script, and automation workflow is implemented in Python.
• Core logic resides in tools/financial_rigor.py and tools/stock_screener.py, utilizing pure Python functions for quantitative checks.
• Data acquisition relies on async Python patterns in tools/xueqiu_scraper.py.
• Integration scripts convert Markdown skills into Codex packages using Python file I/O and JSON serialization.

Frequently Asked Questions

Is AI Berkshire written in any language other than Python?

No. The repository contains exclusively Python source files (.py) and Markdown documentation (.md). All executables, including the web scrapers and financial validators, are invoked via the Python interpreter.

Which Python version does AI Berkshire target?

While the repository does not explicitly pin a Python version in the provided analysis, the use of async/await syntax and type hints suggests Python 3.8+ compatibility, which is standard for modern data‑science projects using Playwright and Pandas.

Can I run AI Berkshire without installing Python?

No. You must have a Python 3 environment to execute the scripts in tools/ and scripts/. The README (located at [README.md](https://github.com/xbtlin/ai-berkshire/blob/main/README.md)) typically provides installation instructions for the required dependencies.

Does AI Berkshire use Jupyter Notebooks for analysis?

Based on the source file structure, the project favors plain Python scripts over Jupyter Notebooks. This choice supports version control and automation workflows, allowing the code to run headlessly in CI/CD pipelines or scheduled cron jobs.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}