ths-advanced-analysis

What This Skill Does

The ths-advanced-analysis skill is a robust financial analysis toolkit designed for OpenClaw agents, built upon the powerful thsdk library. It provides high-level financial data processing and visualization capabilities, ranging from technical analysis of stock price movements (including 1m to 120m interval K-lines) to macro-level market overview via industry indices and concept sectors. The skill is engineered to handle complex requirements like cross-asset comparison (including correlation heatmaps), intraday auction monitoring, big-order flow tracking, and institutional-grade depth analysis. It serves as a comprehensive bridge between raw market data and actionable investment insights.

Installation

To integrate this skill into your environment, use the OpenClaw management command:

clawhub install openclaw/skills/skills/bensema/ths-advanced-analysis

Ensure your local environment meets the requirements (Python 3.9+). For direct library dependencies, you may also install the underlying SDK via pip install --upgrade thsdk. Ensure that the THS connection context is properly managed within your code using the provided with THS() as ths: pattern to ensure thread safety and handle API sessions correctly.

Use Cases

1. Quantitative Research: Execute batch data requests for multiple tickers to generate normalized trend charts and correlation heatmaps to identify asset relationships.
2. Short-Term Trading: Utilize real-time depth and big_order_flow analysis to monitor market microstructure and detect anomalies in call-auction sessions.
3. Market Sentiment Analysis: Leverage the wencai_nlp integration to filter stocks based on complex technical indicators (e.g., MACD golden cross) or fundamental criteria (e.g., ROE thresholds).
4. Industry Rotation Strategy: Automatically pull sector constituents and benchmark them against major indices to evaluate relative strength.

Example Prompts

1. "Analyze the 5-minute K-line of CATL and check for any recent large-order inflows."
2. "Compare the trend performance of BYD, NIO, and XPeng over the last month and show me a correlation heatmap."
3. "Find me the current top-performing stocks in the AI concept sector using Wencai and provide their current 5-level market depth."

Tips & Limitations

• Clarification is Key: When a user request is ambiguous (e.g., "Analyze stock X"), always prompt the user to specify whether they need technical data, funding flow, or institutional research. This prevents irrelevant data overload.
• Asset Mapping: Always use the search_symbols method before executing data pulls. The system relies on specific THSCODE prefixes (e.g., USHA for Shanghai A-shares, URFI for sectors). Do not assume ticker symbols are universal.
• Data Volume: For batch operations involving more than 10 stocks, consider using pagination or batch processing to avoid latency issues in the underlying SDK interface.