tradingview-screenerScreen markets across 6 asset classes using TradingView data. API pre-filters + pandas computed signals. YAML-driven strategies.
Install via ClawdBot CLI:
clawdbot install hiehoo/tradingview-screenerScreen stocks, crypto, forex, bonds, futures, and coins using TradingView's market data. Zero auth required.
Before first use, run the install script to create a venv and install dependencies:
bash skills/tradingview-screener/install.shThis creates .venv/ inside the skill directory with all required packages.
All scripts use the skill's own venv:
skills/tradingview-screener/.venv/bin/python3 skills/tradingview-screener/scripts/<script>.py [args]Windows:
skills/tradingview-screener/.venv/Scripts/python.exe skills/tradingview-screener/scripts/<script>.py [args]| Mode | Description | Script |
|------|-------------|--------|
| Screen | One-time scan with filters, columns, sort | screen.py |
| Signal | YAML-driven signal detection (pre-filters + computed signals) | signal-engine.py |
skills/tradingview-screener/.venv/bin/python3 skills/tradingview-screener/scripts/screen.py \
--asset-class stock --limit 20 \
--filters '[{"field":"MARKET_CAPITALIZATION","op":">","value":1000000000}]' \
--columns NAME,PRICE,CHANGE_PERCENT,VOLUME \
--sort-by VOLUME --sort-order desc# List available signals
skills/tradingview-screener/.venv/bin/python3 skills/tradingview-screener/scripts/signal-engine.py --list
# Run a signal
skills/tradingview-screener/.venv/bin/python3 skills/tradingview-screener/scripts/signal-engine.py --signal golden-cross| Class | Screener | Field Enum |
|-------|----------|------------|
| stock | StockScreener | StockField |
| crypto | CryptoScreener | CryptoField |
| forex | ForexScreener | ForexField |
| bond | BondScreener | BondField |
| futures | FuturesScreener | FuturesField |
| coin | CoinScreener | CoinField |
| Type | Description | Key Params |
|------|-------------|------------|
| crossover | Fast field crosses slow field | fast, slow, direction |
| threshold | Field crosses a value | field, op, value |
| expression | Pandas expression on DataFrame | expr |
| range | Field between min/max bounds | field, min, max |
>, >=, <, <=, ==, !=, between (value: [min, max]), isin (value: [...])
NAME, PRICE, CHANGE_PERCENT, VOLUME, MARKET_CAPITALIZATION, SECTOR,
SIMPLE_MOVING_AVERAGE_50, SIMPLE_MOVING_AVERAGE_200, RELATIVE_STRENGTH_INDEX_14,
MACD_LEVEL_12_26, AVERAGE_VOLUME_30_DAY
Use StockField.search("keyword") in Python to discover more fields (13,000+ available).
| Signal | File | Description |
|--------|------|-------------|
| Golden Cross | state/signals/golden-cross.yaml | SMA50 above SMA200 (bullish) |
| Oversold Bounce | state/signals/oversold-bounce.yaml | RSI < 30 + price rising |
| Volume Breakout | state/signals/volume-breakout.yaml | Volume > 2x avg + momentum |
**Stock Screener** | 15 results | Sorted by VOLUME desc
| NAME | PRICE | CHANGE_PERCENT | VOLUME |
|------|-------|----------------|--------|
| AAPL | 185.50 | 2.3 | 80000000 |
...1, 5, 15, 30, 60, 120, 240, 1D, 1W, 1M
Pass --timeframe 60 to apply hourly interval to technical indicators.
Generated Mar 1, 2026
Individual investors can use this skill to screen stocks based on market cap, price changes, and technical indicators like moving averages and RSI. It helps identify potential buy or sell opportunities by filtering thousands of stocks quickly without requiring TradingView authentication.
Crypto traders can screen cryptocurrencies for signals such as volume breakouts or oversold conditions to optimize entry and exit points. The skill supports multiple asset classes, allowing for cross-market analysis and risk diversification in volatile crypto markets.
Financial analysts at firms can automate market scans across stocks, bonds, and forex to generate reports on trends like golden crosses or sector performance. The YAML-driven strategies enable customizable, repeatable analysis for client presentations or internal decision-making.
Quantitative developers can integrate this skill into trading bots to compute real-time signals using pandas expressions and pre-filters. It supports backtesting by applying technical indicators across different timeframes, enhancing automated trading strategies.
Finance educators and students can use the skill to demonstrate market screening concepts, such as filter operators and signal types, in a hands-on way. It provides access to extensive field data for learning about asset classes and technical analysis without cost barriers.
Offer a cloud-hosted version of the skill with enhanced features like real-time alerts, advanced analytics, and API access for a monthly fee. Target retail investors and small funds seeking automated screening tools without setup overhead.
License the skill to financial institutions and brokerages to integrate into their platforms, providing clients with built-in screening and signal capabilities. Customize it with branded interfaces and additional data sources.
Provide a free basic version for individual use, with paid upgrades for advanced signals, historical data exports, and priority support. Monetize through upselling to power users and professional traders.
💬 Integration Tip
Ensure the virtual environment is correctly set up by running the install script first, and use the provided Python paths to avoid dependency conflicts when executing scripts.
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