AKQuant A-Share Backtesting
v1.0.1A-share quantitative trading backtesting using AKQuant (Rust engine) and AKShare data. Use when user asks to "backtest a stock strategy", "test trading algor...
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MIT-0
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LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
Security Scan
OpenClaw
Benign
high confidencePurpose & Capability
Name/description, SKILL.md, README and Python scripts all describe a backtesting tool using akquant and AKShare. The included scripts implement double-MA and trend-filter strategies and call AKShare for data — this matches the stated purpose. One minor mismatch: config/holdings.yaml contains a user portfolio sample (personal-looking holdings) that is not needed for running the backtests; it appears to be example data bundled with the skill.
Instruction Scope
SKILL.md instructs running local Python scripts, installing akquant/akshare/pandas/numpy if missing, and using AKShare for market data. The instructions reference local CSV cache under /root/.openclaw/workspace/data (and venv path) which is consistent with the code (load_stock_data checks that path). There are no instructions to read unrelated system config, to exfiltrate data, or to post results to unexpected external endpoints.
Install Mechanism
There is no automated install spec — the skill is delivered as files and the README/SKILL.md suggest installing Python packages via pip. No downloads from arbitrary URLs or extracted archives are present in the manifest.
Credentials
The skill declares no required environment variables, credentials, or config paths. The code uses local workspace paths and network access to AKShare (expected for historical/real-time data). No broad or unrelated secrets are requested.
Persistence & Privilege
always is false and the skill does not request special persistent privileges. It modifies nothing outside its workspace and does not claim to enable itself or alter other skills.
Assessment
This skill appears to be what it says: Python backtest scripts that fetch A‑share data from AKShare and run MA/RSI strategies. Before running: (1) inspect bundled files (notably config/holdings.yaml) — it contains personal-looking portfolio data you may not want shared; (2) run in an isolated Python virtualenv or container and avoid running as root; (3) install akquant/akshare from trusted sources (pip) and verify package names/versions; (4) be aware the scripts will access the network to download market data and may read local CSVs under /root/.openclaw/workspace/data; (5) if you plan to use real trading, do not reuse secrets or real broker credentials here — this skill does not implement live trading but only backtesting. Overall: reasonable to install/use after the above checks.Like a lobster shell, security has layers — review code before you run it.
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License
MIT-0
Free to use, modify, and redistribute. No attribution required.