Pandas Ta Indicators

基于 pandas-ta 库计算技术分析指标（RSI、MACD、布林带、KAMA 等），支持多市场数据可视化与自定义参数调整。。

This skill looks like a documentation/blueprint for a ZVT-based TA pipeline rather than a small, self-contained 'pandas-ta' helper. Before installing or running it: - Treat it as code-generation + orchestration, not only a plotting tool. Expect the agent to run Python commands, check or create ~/.zvt, and (depending on host integration) attempt package installs. - Do not provide broker or data-provider credentials (joinquant, brokers, etc.) unless you verify exactly where and how they'll be used. The SKILL.md mentions trading_execution but requests no credentials — ask the author how trade execution is implemented and which credentials would be needed. - Inspect seed.yaml and references locally (they are included) to understand required packages and the precondition checks the skill will perform. If you permit the agent to run precondition commands, run them in a sandbox or review the exact commands first. - If you only want indicator calculations/visuals, prefer running the code the skill generates manually in a controlled environment rather than letting the agent execute installs or backtests automatically. If you want me to, I can: (1) extract and list the concrete precondition commands and filesystem paths the skill would run/access, (2) summarize which packages you would need to install manually, or (3) produce a minimal, offline-only example that computes RSI/MACD/Bollinger using pandas-ta without any ZVT/backtest/trading steps.

Type: OpenClaw Skill Name: pandas-ta-indicators Version: 0.3.3 The skill bundle is a highly structured framework for financial technical analysis and quantitative trading based on the 'zvt' and 'pandas-ta' libraries. While it contains numerous 'fatal' constraints and 'semantic locks' (e.g., in SKILL.md and references/seed.yaml), these are domain-specific safety guards intended to prevent financial modeling errors such as look-ahead bias, incorrect volatility calculations, or improper order sequencing. There is no evidence of malicious intent, data exfiltration, or unauthorized system access; the instructions provided to the AI agent are focused on maintaining the integrity and accuracy of the financial pipeline.