Openclaw Wangcai

by @DongCoder7

openclaw-wangcai Summary This workspace provides OpenClaw configuration, operation rules, data source integration guides, and quantitative trading extensions...

README

openclaw-wangcai

Summary

This workspace provides OpenClaw configuration, operation rules, data source integration guides, and quantitative trading extensions specifically tailored for investment analysis and research workflows. It emphasizes strict operational standards, robust documentation, and practical case-based learning to ensure reliable, repeatable analysis and automation.

Included Assets

  • Core Configuration & Standards

    • AGENTS.md – Agent operation protocols, session routines, and memory usage guidance
    • SOUL.md – Agent mission, methodology, and operational code of conduct
    • MEMORY.md – Long-term memory and enforced Python virtual environment rules
    • TOOLS.md – Local environment customization and tool mapping
  • Quantitative System Integration

    • skills/quant-integration/README.md – qteasy and quant system integration guide
  • Research & Study Modules

    • study/README.md – K-line technical analysis learning plan
    • study/practice/case_studies/README.md – Practice and case study log template
  • Data Source Configuration & Docs

    • config/mcporter.json – External MCP service URL configuration
    • config/zsxq_source.md – Zsxq (industry info source) integration and usage instructions
    • docs/DATA_SOURCES_KEY_POINTS.md – Knowledge base data acquisition and usage highlights
    • docs/DATA_SOURCE_GUIDE.md – Guide to selecting and using data sources (Longbridge, Tushare, Tencent, etc.)
    • docs/QA_DATA_SUPPLEMENT.md – Data quality assurance for historical supplement
  • Reference & Operation

    • docs/QUICK_REFERENCE.md – Common commands, troubleshooting, and path index
    • docs/SECTOR_ANALYSIS_METHODOLOGY.md – Sector investment analysis methodology
    • docs/SKILL_TRIGGER_REMINDER.md – Triggers and reminders for critical skill execution
  • Error Management & Analysis

    • docs/ERROR_ANALYSIS_NO_REAL_PRICE.md – Error analysis: missing real stock price
    • docs/ERROR_SUMMARY_ANALYSIS.md – Summary of report mistakes and how to prevent them
  • API Setup Guides

    • docs/LONGBRIDGE_SETUP.md – Longbridge API configuration and troubleshooting

How to Use

  1. Start a Session

    • Review AGENTS.md for the correct workflow/setup each time you start a new session.
    • Check SOUL.md and MEMORY.md for role, strategy, and operational boundaries.
  2. Enforce Python venv Usage

    • All Python operations must run inside the prescribed virtual environment (/root/.openclaw/workspace/venv/bin/python3).
    • Use provided scripts (venv_runner.sh, check_venv_compliance.sh, etc.) and never run with the system Python interpreter.
  3. Data Source Use

    • Integrate stock, sector, or industry intelligence using the guides in docs/ and the configuration in config/.
    • Prefer Longbridge API for real-time data; fallback on Tushare or Tencent for history or redundancy, as described in docs/DATA_SOURCE_GUIDE.md.
    • Use the Zsxq data integration for industry and company insights per config/zsxq_source.md.
  4. Quantitative Analysis

    • Reference skills/quant-integration/README.md to extend OpenClaw with qteasy for fast strategy verification and live trading.
  5. Learning & Practice

    • Structured study guides and practical templates are in study/—leverage these to systematically improve technical analysis and real-world trading review.
  6. Error Review and Continuous Improvement

    • Consult error analyses in the docs/ folder to avoid repeating past mistakes in investment research and automation.

Notes

  • Security: Personal and session-specific information in MEMORY.md is for main/chat agent use only—not to be loaded in shared or public contexts.
  • Customization: Use TOOLS.md for local notes; keep environment details out of shared skill modules.
  • Documentation: The docs/ directory is comprehensive—review appropriate documents for troubleshooting, rules, and data workflows before modifying automated routines.
  • Data Freshness: Carefully follow update frequencies and historical backfill rules, especially when using premium or time-sensitive sources.

Adhering to the documented workflow and operational iron rules is mandatory for consistent, reliable use of this OpenClaw workspace.

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