ClawTrust
1.9k Downloads
Overview
Screen and filter markets across six asset classes (stocks, crypto, forex, bonds, futures, and coins) using TradingView screener data, with optional pandas-based computed signals driven by YAML-defined strategies.
Key Advantages
1.Covers six major asset classes from a single interface, enabling cross-market screening workflows.
2.Uses TradingView screener data with zero authentication, simplifying setup and avoiding API key management.
3.Flexible filter system with many operators (>, >=, <, <=, ==, !=, between, isin) and access to a very large field universe (13,000+ stock fields).
4.Supports both one-off screening (screen.py) and a signal engine (signal-engine.py) for YAML-driven, reusable strategies.
5.Computed signal types (crossover, threshold, expression, range) allow non-trivial technical logic on top of raw screener data via pandas DataFrames.
- Pre-built YAML signals (golden cross, oversold-b
Use Cases
- Running one-off market scans (e.g., top-volume stocks with minimum market cap and specific technical conditions) across different asset classes.
- Building and maintaining YAML-based trading or alerting strategies (e.g., golden cross, volume breakout) and running them on demand via the signal engine.
- Scanning for technical setups like oversold bounces, moving-average crossovers, or volume breakouts without writing Python logic directly.
- Automating periodic screening tasks in an agent workflow, such as daily pre-market scans or hourly technical condition checks on watchlists.
- Exploratory research and idea generation, using field discovery (e.g., StockField.search) and rich filters to find securities matching complex criteria.
Evaluation Scores
8.3
/ 10
Reliability
7.2
Functionality
9.2
Usability
8.1
Safety
8.8
Performance
8.0
Compatibility
8.3
Based on 1 evaluation · Latest: 3/19/2026
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8.3/103/19/2026▼
OS: linux-arm64LLM: google/gemini-2.5-flash-lite
Overall: Strong, flexible market screener skill with multi-asset coverage and YAML-driven signal logic, well-suited for systematic scanning and signal detection in agent workflows.
Key strengths
- Broad coverage: stocks, crypto, forex, bonds, futures, and coins via TradingView’s screeners.
- High flexibility: rich filter operators, thousands of fields, and a separate signal engine for YAML-defined strategies and pandas-based signals.
- Low-friction setup: zero-auth data access and an isolated venv installer simplify deployment under OpenClaw.
Main risks / limitations
- External dependency: relies on TradingView’s unofficial/undocumented screener endpoints; upstream changes, rate limits, or blocking can break or degrade the skill.
- Latency and robustness: performance and reliability depend on network conditions and TradingView load; large scans or many signals may be slower or occasionally fail.
- Financial context risk: outputs can be misused as trading advice; agents should treat results as data/ideas, not as recommendations, and avoid promising outcomes.
Recommended scenarios
- Agents that need to run repeatable market scans (e.g., daily stock/crypto screens) based on fundamentals and technical indicators.
- Quant-style or rule-based workflows where signals are defined declaratively in YAML and evaluated programmatically.
- Research or idea-generation tools that surface candidates matching specific technical, volume, or capitalization patterns across multiple asset classes.
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