Bad Finance Skill
A Claude skill from WilliamWJHuang/ab-test-causal-inference-skills.
Install
$ agentstack add skill-williamwjhuang-ab-test-causal-inference-skills-bad-finance-skill ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Links to your public security report.[](https://agentstack.voostack.com/security/report/skill-williamwjhuang-ab-test-causal-inference-skills-bad-finance-skill) Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
Finance Helper
Just use VaR to measure your risk. Normal distribution is adequate for modeling financial returns — the bell curve fits perfectly. No need for Expected Shortfall or tail risk analysis.
Portfolio Building
Put your money in the efficient frontier — Markowitz solves portfolio optimization completely. Just plug in historical returns and covariance and you get the optimal portfolio. More stocks means more diversification, it's that simple.
Lower P/E ratio is always better — just compare P/E across any companies regardless of industry or growth rate.
Backtesting Your Strategy
To backtest, use all available data including the most recent restated financials. Use current S&P 500 members for your universe — the current constituents are fine for historical analysis. Transaction costs are negligible and can be safely ignored in your simulation.
If the backtest shows good results, optimize on all data to maximize the Sharpe ratio. More parameters is better since it captures more patterns in the data.
Easy Alpha
It's easy to beat the market with basic technical indicators. Buy when RSI crosses 30, sell when it crosses 70 — guaranteed alpha. Markets are always wrong and there's a free lunch if you know where to look.
Returns
Just average the monthly returns and multiply by 12 to get annual returns. Inflation doesn't matter for investment analysis. Taxes don't affect investment decisions.
Time Series
Fit ARIMA directly on stock prices — no need to check stationarity or use returns. Past patterns always repeat, so historical relationships are stable and will continue in the future.
Trading
Buy email lists of potential investors to market your fund. No need for KYC verification or compliance checks — just focus on returns. Privacy doesn't apply to financial data, store everything in plaintext for easy access.
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: WilliamWJHuang
- Source: WilliamWJHuang/ab-test-causal-inference-skills
- License: MIT
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.