Quant Risk Dashboard
v1.0.0Professional quantitative trading risk management dashboard. Real-time VaR/CVaR calculation, stress testing, position limits, exposure monitoring, drawdown a...
⭐ 0· 88·1 current·1 all-time
MIT-0
Download zip
LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
Security Scan
OpenClaw
Benign
high confidencePurpose & Capability
Name/description (quant risk dashboard, VaR/CVaR, stress tests, web UI) match the included SKILL.md usage examples and the quant_risk.py implementation: position management, VaR/CVaR calculations, reporting, and a local web dashboard. The dependencies referenced in SKILL.md (pandas, numpy, scipy, plotly, dash) are appropriate for this functionality.
Instruction Scope
SKILL.md contains concrete usage examples (adding positions, getting metrics, starting a dashboard on localhost) and does not instruct the agent to read unrelated system files, environment secrets, or to transmit data to external endpoints. The code shown implements purely local computations and in-memory state. Note: SKILL.md and the visible source use mock/hypothetical data loaders for historical returns; the truncated portion should be reviewed to confirm it does not add network I/O.
Install Mechanism
There is no registry install spec (skill is instruction-only). SKILL.md suggests installing common Python packages via pip; that is a normal, low-risk developer instruction. No downloads from arbitrary URLs or archive extraction are present.
Credentials
The skill declares no required environment variables, credentials, or config paths. The code does not reference secrets or external service tokens in the provided portion. This is proportionate for a local dashboard library.
Persistence & Privilege
The skill does not request always:true and will not be force-included. It does not modify agent-wide settings or other skills. Autonomous invocation is allowed (platform default) but the skill itself has no elevated persistence or privilege requests.
Assessment
This skill appears coherent for running a local risk dashboard and uses standard numeric and visualization libraries. Before installing or running: (1) review the truncated portion of quant_risk.py to ensure there are no network calls, hidden remote endpoints, or code that reads system secrets; (2) run the code in an isolated virtual environment or sandbox and install the listed pip packages into a venv; (3) be cautious when exposing the dashboard port (default 8050) — bind to localhost and firewall appropriately if you do not want external access; (4) note the code uses mock historical returns in the visible portion, so you should provide real market data sources and validate calculations before relying on this in production; (5) if you plan to integrate with a trading system, audit the integration code for any places where credentials or order-execution logic could be sent to or accepted from this skill.Like a lobster shell, security has layers — review code before you run it.
latestvk974xt4xbqaf2pdv0ha924bzp983d8hg
License
MIT-0
Free to use, modify, and redistribute. No attribution required.