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Install in OpenClaw
/install strategy-voting
Description
多策略投票交易系统。当用户说"投票策略"、"多策略"、"策略投票"、"综合信号"时触发。整合突破、RSI均值回归、MACD金叉、布林带四个策略，加权投票决定交易信号。
Usage Guidance
What to consider before installing: (1) This skill appears to do exactly what it claims — generate weighted voting signals — and does not exfiltrate data or request credentials. (2) Operational notes: the script requires numpy and pandas but the skill provides no install steps; install those packages in the agent environment or add a dependency spec. (3) SKILL.md runs the script via a hard-coded workspace path — ensure the agent environment has the file at that location or adjust the path. (4) The included script uses a generated test dataset and has no broker/exchange integration — if you plan to connect it to live trading, review and add secure, explicit code for authentication and order execution (never paste API keys into untrusted skills). (5) If you need the agent to call this autonomously, be aware autonomous invocation is allowed by default; because this skill requests no secrets the risk is low, but always review integration code before giving it access to real funds.
Capability Analysis
Type: OpenClaw Skill
Name: strategy-voting
Version: 1.0.0
The skill bundle implements a standard multi-strategy technical analysis voting system for trading, including Breakout, RSI, MACD, and Bollinger Bands strategies. The code in scripts/voter.py is well-structured, uses standard libraries (numpy, pandas), and contains no evidence of data exfiltration, malicious execution, or prompt injection.
Capability Assessment
Purpose & Capability
Name/description (多策略投票交易系统) align with the included Python implementation: four strategies (breakout, RSI reversion, MACD cross, Bollinger) and weighted voting logic are implemented. The skill does not request unrelated credentials or binaries.
Instruction Scope
SKILL.md tells the agent to run the bundled script at /root/.openclaw/workspace/skills/strategy-voting/scripts/voter.py which matches the provided file. The runtime instructions do not attempt to read unrelated files or environment variables. Note: the instructions only run a self-contained test dataset — they do not explain how to feed live market data or integrate with an exchange, and they do not declare required Python packages.
Install Mechanism
There is no install spec (instruction-only), so nothing external is downloaded. The code imports numpy and pandas but the skill does not declare or install these dependencies; this is an operational omission (may cause runtime errors) rather than a security red flag. No external URLs or archives are used.
Credentials
The skill declares no required environment variables or config paths and the code does not access environment secrets or network endpoints. There are no credential requests — proportional to being a local signal generator.
Persistence & Privilege
always is false and the skill does not attempt to modify other skills or system configuration. It only prints a report and does not persist data or enable itself globally.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install strategy-voting - After installation, invoke the skill by name or use
/strategy-voting - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
多策略投票交易系统：突破、RSI、MACD、布林带加权投票
Metadata
Frequently Asked Questions
What is Strategy Voting?
多策略投票交易系统。当用户说"投票策略"、"多策略"、"策略投票"、"综合信号"时触发。整合突破、RSI均值回归、MACD金叉、布林带四个策略，加权投票决定交易信号。 It is an AI Agent Skill for Claude Code / OpenClaw, with 147 downloads so far.
How do I install Strategy Voting?
Run "/install strategy-voting" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Strategy Voting free?
Yes, Strategy Voting is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does Strategy Voting support?
Strategy Voting is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Strategy Voting?
It is built and maintained by huiLI203 (@huili203); the current version is v1.0.0.
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