tv-scraper 1.1.0

A powerful Python library for scraping real-time market data, indicators, and ideas from TradingView.

TV Scraper

A powerful, real-time Python library for extracting financial data, indicators, and ideas from TradingView.com.

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Attribution

This project is based on mnwato/tradingview-scraper. Thanks to the original author for the foundational work.

📚 Documentation

For complete documentation, installation guides, API references, and examples, visit:

📖 Full Documentation

Quick Links

• 🚀 Quick Start Guide
• 📦 Installation
• 📊 Supported Data
• 🔧 API Reference

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🚀 Quick Start

This library requires Python 3.11+ and uses uv for dependency management.

Installation

Install from PyPI (recommended):

pip install tv-scraper

Or install with uv (developer / alternate):

# Clone the repository for development
git clone https://github.com/smitkunpara/tv-scraper.git
cd tv-scraper
```,oldString:

# Install runtime deps (uv auto-creates virtual environment)
uv sync

If you prefer to install the published package using uv:

uv add tv-scraper

Basic Usage Examples

Fetching Technical Indicators

Get RSI and Stochastic indicators for Bitcoin on Binance:

from tv_scraper import Technicals

# Initialize scraper
technicals = Technicals()

# Scrape indicators for BTCUSD
result = technicals.scrape(
    exchange="BINANCE",
    symbol="BTCUSD",
    timeframe="1d",
    technical_indicators=["RSI", "Stoch.K"]
)

if result["status"] == "success":
    print(result["data"])

Scraping Trading Ideas

Get popular trading ideas for Ethereum:

from tv_scraper import Ideas

# Initialize scraper
ideas = Ideas()

# Scrape popular ideas for ETHUSD
result = ideas.scrape(
    exchange="CRYPTO",
    symbol="ETHUSD",
    start_page=1,
    end_page=1,
    sort_by="popular"
)

if result["status"] == "success":
    print(f"Found {len(result['data'])} ideas.")

✨ Key Features

• 📊 Real-Time Data: Stream live OHLCV and indicator values via WebSocket
• 📰 Comprehensive Coverage: Scrape Ideas, News, Market Movers, and Screener data
• 📈 Fundamental Data: Access detailed financial statements and profitability ratios
• 🔧 Advanced Tools: Symbol Markets lookup, Symbol Overview, and Minds Community discussions
• 📋 Structured Output: All data returned as clean JSON/Python dictionaries
• 🌍 Multi-Market Support: 260+ exchanges across stocks, crypto, forex, and commodities
• ⚡ Fast & Reliable: Built with async support and robust error handling

📋 What's Included

Core Modules

• Indicators: 81+ technical indicators (RSI, MACD, Stochastic, etc.)
• Options: Fetch option chains by expiration or strike price
• Ideas: Community trading ideas and strategies
• News: Financial news with provider filtering
• Real-Time: WebSocket streaming for live data
• Screener: Advanced stock screening with custom filters
• Market Movers: Top gainers, losers, and active stocks
• Fundamentals: Financial statements and ratios
• Calendar: Earnings and dividend events

Data Sources

• 260+ Exchanges: Binance, Coinbase, NASDAQ, NYSE, and more
• 16+ Markets: Stocks, Crypto, Forex, Futures, Bonds
• Real-Time Updates: Live price feeds and indicators
• Historical Data: Backtesting and analysis support

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🛠️ Development & Testing

For contributors and developers, this project includes comprehensive tooling for local testing.

Quick Commands

# Run all quality checks before committing
make check

# Full CI simulation with coverage
make ci

# Individual checks
make lint          # Run ruff linter
make format        # Auto-format code
make type-check    # Run mypy type checker
make test          # Run tests

Pre-commit Hooks

Pre-commit hooks automatically run on every commit to enforce code quality:

# Install hooks (one-time setup)
make install-hooks

Full Documentation

See LOCAL_TESTING.md for complete details on:

• Makefile commands
• Pre-commit hook configuration
• Running GitHub Actions locally with act
• CI/CD workflow testing

Publishing to PyPI

This project is configured to use Trusted Publishing (OIDC) via GitHub Actions. See PUBLISHING.md for step-by-step instructions on setting up your PyPI project.

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🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

• 🐛 Bug Reports: Open an issue
• 💡 Feature Requests: Start a discussion

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📄 License

This project is licensed under the MIT License - see the LICENSE file for details.