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Quant Strategy Bundle
by
jason-aka-chen
· GitHub ↗
· v1.0.0
· MIT-0
134
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0
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Active Installs
1
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Install in OpenClaw
/install quant-strategy-bundle-chen
Description
Quantitative trading strategy bundle - Contains multiple verified A-stock quantitative trading strategy frameworks. Includes momentum strategies, reversal st...
Usage Guidance
This skill is inconsistent: the README expects a local 'strategy' Python module and a Tushare API token but provides neither and does not declare required credentials. Before installing or running anything: (1) obtain the actual strategy code from a trusted source (the skill package lacks it); (2) do not paste your Tushare token into public places — store it securely and consider using environment variables rather than unchecked config files; (3) prefer installing dependencies into an isolated virtual environment (venv/conda) rather than system-wide; (4) verify the strategy implementation code for unsafe behaviour (network calls, data exfiltration, or executing arbitrary shell commands); (5) if you cannot obtain the missing code or a trustworthy source, avoid running the pip installs or executing unverified scripts. These inconsistencies are plausibly benign poor packaging, but they could hide risk, so proceed with caution.
Capability Assessment
Purpose & Capability
The name/description (quant trading strategies, backtesting, signal generation) match the high-level instructions, but the SKILL.md expects a 'strategy' Python module and a config.json with a Tushare token that are not included and not declared in requirements. The skill therefore does not contain the code it claims to document, which is an incoherence.
Instruction Scope
Runtime instructions tell the user/agent to pip install packages and then import from 'strategy' (MomentumStrategy, etc.) and to place a Tushare token in config.json. The skill does not provide that module or an example config, and it does not declare the Tushare token as a required credential. This gives the agent/user broad discretion to install packages and use external tokens without guidance or packaged code.
Install Mechanism
There is no install spec (instruction-only). SKILL.md recommends 'pip install pandas numpy xgboost tushare' which will download code from PyPI — normal for Python projects but worth noting because large packages (xgboost) may require build steps; the skill does not provide a controlled install or pinned versions.
Credentials
The instructions require a Tushare token (sensitive API credential) in config.json, but the skill declares no required environment variables or primary credential. Asking for a third‑party API token without declaring it or explaining storage/usage is disproportionate and a transparency gap.
Persistence & Privilege
always is false and there is no install script or persistent modification of agent/system configuration. The skill does not request elevated or persistent privileges.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install quant-strategy-bundle-chen - After installation, invoke the skill by name or use
/quant-strategy-bundle-chen - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
Initial release
Metadata
Frequently Asked Questions
What is Quant Strategy Bundle?
Quantitative trading strategy bundle - Contains multiple verified A-stock quantitative trading strategy frameworks. Includes momentum strategies, reversal st... It is an AI Agent Skill for Claude Code / OpenClaw, with 134 downloads so far.
How do I install Quant Strategy Bundle?
Run "/install quant-strategy-bundle-chen" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Quant Strategy Bundle free?
Yes, Quant Strategy Bundle is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does Quant Strategy Bundle support?
Quant Strategy Bundle is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Quant Strategy Bundle?
It is built and maintained by jason-aka-chen (@jason-aka-chen); the current version is v1.0.0.
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