tradingview-screener

What This Skill Does

The tradingview-screener skill is a powerful, autonomous market research tool for OpenClaw users. It bridges the gap between complex financial market data and actionable insights by leveraging TradingView's massive backend data infrastructure. Unlike manual browser-based screener tools, this skill uses a programmatic approach to fetch, filter, and analyze market data across six distinct asset classes: stocks, crypto, forex, bonds, futures, and coins. By utilizing YAML-driven strategy configurations, it allows for the execution of complex technical analysis signals—such as golden crosses or custom pandas-based expressions—without requiring direct API authentication, making it a zero-friction tool for quantitative research.

Installation

Installation is streamlined to ensure environmental stability via an isolated Python virtual environment. First, navigate to the skill directory within your OpenClaw installation and execute the provided setup script: bash skills/tradingview-screener/install.sh This script handles the heavy lifting of dependency management and environment isolation. For subsequent execution, ensure you target the local .venv interpreter as outlined in the documentation to maintain compatibility with required data analysis libraries like pandas and NumPy.

Use Cases

This skill is perfect for traders and analysts who require consistent, reproducible data scans. Common use cases include:

• Automated Market Surveillance: Periodically scanning the entire US stock market for tickers with a market capitalization exceeding $1B and high relative volume.
• Signal Backtesting & Detection: Defining custom YAML strategies to identify entry or exit points based on Moving Average crossovers or RSI thresholds.
• Cross-Asset Analysis: Monitoring volatility across different asset classes, such as comparing bond yield movements against crypto market fluctuations.
• Data Export: Generating clean CSV or JSON reports from raw market data for integration into secondary trading models or spreadsheets.

Example Prompts

• "Hey OpenClaw, use the tradingview-screener to find the top 20 crypto coins by volume where the RSI-14 is currently below 30, signaling potential oversold conditions."
• "OpenClaw, run the 'golden-cross' signal strategy for the S&P 500 stock index and provide me with a list of companies that triggered the signal in the last 24 hours."
• "Can you scan the forex market for pairs where the price is between 1.05 and 1.10 and the daily change is greater than 2%? Sort the results by volume."

Tips & Limitations

• Field Discovery: Use StockField.search("keyword") if you are unsure of the specific field string to use. With over 13,000 fields available, precise keyword searches will save significant time.
• Signal Logic: When creating custom YAML signal strategies, ensure your expression syntax follows standard pandas/NumPy conventions, as the signal-engine maps these to dataframe operations.
• Rate Limiting: While this skill requires no authentication, be mindful of excessive frequency. Use scheduled tasks rather than high-frequency loops to avoid potential disruption by TradingView's servers.
• Environment: Always execute scripts via the local .venv path to avoid dependency conflicts with the core OpenClaw environment.