A zero-dependency cryptocurrency technical analysis engine that generates multi-indicator reports for spot pairs and on-chain contract addresses.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install ta-radar
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install ta-radar using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is TA Radar?
TA Radar is a powerful technical analysis skill designed for AI agents to deliver institutional-grade market insights without the bloat of heavy libraries. It provides a robust framework for calculating essential trading indicators such as Exponential Moving Averages (EMA), Relative Strength Index (RSI), and Moving Average Convergence Divergence (MACD) using pure Python. This skill, as part of the Openclaw Skills ecosystem, is specifically engineered for reliability, featuring automatic data source failover and GFW compatibility for global accessibility.
By bridging the gap between raw market data and human-readable reports, TA Radar allows developers to integrate sophisticated financial analysis into their agent workflows. Whether analyzing major assets on Binance or discovering emerging tokens via contract addresses on decentralized exchanges, this skill provides the objective data needed for informed decision-making.
TA Radar Use Cases
- Automated generation of daily cryptocurrency market trend reports for traders.
- Real-time monitoring of RSI and Bollinger Band volatility for automated trading triggers.
- Deep-dive technical analysis of DeFi tokens using on-chain contract addresses.
- Providing financial context to AI coding agents during the development of fintech applications.
- Enabling market analysis in network-restricted regions using the built-in Gate.io fallback logic.
How TA Radar Works
- The agent captures the user's requested symbol (ticker or contract) and preferred timeframe (1h, 4h, or 1d).
- If a contract address is detected, the skill utilizes a DexScreener proxy to resolve the base token symbol.
- Historical K-line data is retrieved from Binance; if unreachable, the system automatically falls back to the Gate.io API.
- The internal mathematical engine processes the price data to calculate technical indicators without external dependencies.
- A comprehensive report is rendered, including support and resistance levels alongside plain-language trend explanations.
- The resulting technical analysis is presented to the user through the Openclaw Skills interface.
TA Radar Setup
To get started with this skill in your environment, ensure you have Python 3 installed. No third-party libraries like pandas are required.
# Clone the repository and navigate to the directory
git clone https://github.com/clawhub/ta-radar
cd ta-radar
# The skill runs as a standalone Python script
# Set your environment variables and execute
TA_SYMBOL="BTCUSDT" TA_INTERVAL="4h" python3 ta_radar_run.py
TA Radar Data Schema & Taxonomy
The TA Radar skill within the Openclaw Skills collection organizes data into a structured report format. Below is the metadata and data taxonomy used:
| Attribute | Description | Examples |
|---|---|---|
| SYMBOL | The asset identifier or EVM contract address | BTC, 0xabc..., ETHUSDT |
| INTERVAL | The timeframe used for candle data | 1h, 4h, 1d |
| INDICATORS | Mathematical outputs from the TA engine | RSI (14), EMA (7/25/99), MACD |
| SOURCES | The primary and secondary data providers | Binance, Gate.io, DexScreener |
| LEVELS | Identified price pivot points | Upper Bollinger, EMA99 Support |
TA Radar Advanced Features
- Dual-Source Data Fallback: Automatically switches from Binance to Gate.io to ensure high availability and bypass regional network restrictions.
- On-Chain Resolution: Seamlessly resolves decentralized contract addresses into tradable symbols using the DexScreener proxy.
- Beginner-Friendly Annotations: Every technical signal includes an objective, plain-language explanation to make reports accessible to non-technical users.
- Zero-Dependency Math Engine: Performs complex calculations for RSI, MACD, and Bollinger Bands using only native Python libraries, ensuring maximum portability within Openclaw Skills.
- Composite Trend Voting: Uses a multi-indicator weighted system to provide a definitive trend label (Bullish, Bearish, or Neutral).
SKILL.md
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