Trading Agents Cn

基于 LLM 的 A 股多智能体交易分析框架，支持批量选股对比、回测信号生成和因子研究，自带 OpenAI 兼容 API 适配器模板。

This skill claims to be a full trading/backtest framework but the manifest omits many real runtime requirements. Before installing or invoking it: - Ask the publisher (or inspect the full source) for an explicit list of required environment variables and secrets (TUSHARE_TOKEN, JQDATA credentials, TUSHARE_PRO_TOKEN, QMT/BROKER tokens, MONGODB/REDIS URIs, and any LLM provider API keys). Do not supply secrets until you know exactly what will use them and where they'll be stored. - Review the examples/crawlers referenced (social_media_crawler, internal_message_crawler). Confirm whether the 'internal' crawler requires access to corporate systems or private message stores; if so, avoid granting broad network or filesystem access. - Because the skill expects Python packages (zvt, data recorders, SDKs), install and run it first inside an isolated environment (VM or sandbox container) and inspect what network connections it makes when executed. Do not run on a production machine with secrets mounted. - Confirm license and provenance. There is no homepage/source URL; prefer packages with a verifiable upstream repository or published releases. The SKILL.md claims 'Proprietary' license but no LICENSE file in registry metadata — ask for it. - Verify semantic locks and constraints (T+1, next-bar execution, locked MACD params) align with your intended use; those are hard constraints in the documentation and might halt or change execution. Given the clear mismatch between declared and implied requirements, treat this skill as untrusted until you can verify its required credentials, inspect the example crawler code, and run it in a controlled sandbox.

Type: OpenClaw Skill Name: trading-agents-cn Version: 0.3.3 The bundle is a comprehensive and highly structured framework for A-share trading analysis using the zvt library. It provides an AI agent with extensive domain knowledge, including financial regulatory constraints (e.g., T+1 settlement, price limits in CONSTRAINTS.md), technical anti-patterns (ANTI_PATTERNS.md), and logical 'semantic locks' (e.g., SL-01, SL-02 in LOCKS.md) designed to prevent common quant finance errors like look-ahead bias. The instructions in SKILL.md and seed.yaml are focused on maintaining analysis quality and ensuring reproducible results. There are no indicators of malicious intent, data exfiltration, or unauthorized execution; the bundle even includes security-conscious features such as shell operator restrictions and warnings against hardcoding credentials.