tvfinance 26.0.0

Typed sync and async tools for financial market data research

tvfinance

tvfinance is a typed, async-first Python toolkit for financial market data research. One domain implementation powers synchronous Python, native async Python, CLI, and optional MCP interfaces.

Python 3.10 through 3.14 are supported.

Features

• Symbol search, quote snapshots, batch quotes, live quote updates, and screeners.
• Historical OHLCV bars with configurable resolution and adjustment.
• Option-series discovery and automatically selected option chains.
• Economic, earnings, revenue, dividend, and IPO calendars.
• News metadata, optional article bodies, and Markdown output.
• Company profile, financials, forecasts, technicals, bonds, ETFs, holdings, filings, and community ideas.
• Ticker, Tickers, AsyncTicker, and AsyncTickers facades.
• Typed immutable models, strict validation, retry, cache, and structured errors.
• Optional persistent SQLite TTL/LRU cache with no additional dependency.

Installation

pip install tvfinance
pip install "tvfinance[mcp]"

No MCP dependency is imported by the base package.

Quick start

import tvfinance

quote = tvfinance.quote("NASDAQ:AAPL")
bars = tvfinance.history("NASDAQ:AAPL", resolution="1D", count=30)
profile = tvfinance.profile("NASDAQ:AAPL")

Native async calls live in tvfinance.aio:

from tvfinance import aio

quote = await aio.quote("NASDAQ:AAPL")
bars = await aio.history("NASDAQ:AAPL", count=30)

Reuse connections for multiple operations:

from tvfinance import AsyncClient

async with AsyncClient() as client:
    quote = await client.quote("NASDAQ:AAPL")
    news = await client.news("NASDAQ:AAPL", fetch_body=True)

CLI and MCP entry points:

tvfinance --help
tvfinance history NASDAQ:AAPL --count 30
tvfinance-mcp

Development

uv sync --group dev
uv run ruff format .
uv run ruff check .
uv run mypy src tests
uv run pytest
uv build

Status

Version 26.0.0 is the first production release of the rebuilt package. Provider endpoints are unofficial and may change. This project is for research and does not provide financial advice. The project was substantially created with LLM assistance; read the AI disclosure before use or contribution.