Vibe-Trading is a personal trading agent that gives an AI system tools for market analysis, algorithmic trading, backtesting, and related workflows. It is for users who want an agent to research and evaluate trading strategies or manage simulated and other trading activities. The catalogue contains skills that expose these trading capabilities to compatible agents.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add HKUDS/Vibe-Trading --skill volatilitygit clone --depth 1 https://github.com/HKUDS/Vibe-TradingWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/hkuds/vibe-trading/volatility)<a href="https://agentmods.dev/skills/hkuds/vibe-trading/volatility"><img src="https://agentmods.dev/badge/skills/hkuds/vibe-trading/volatility/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/hkuds/vibe-trading/volatility"><img src="https://agentmods.dev/badge/skills/hkuds/vibe-trading/volatility.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk pass
- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00029 | $0.00505 |
| Opus 5 | $0.00015 | $0.00253 |
| Sonnet 5 | $0.00006 | $0.00101 |
| Haiku 4.5 | $0.00003 | $0.00051 |
Grade A, and why
volatility scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
1 near-identical copy found in the catalogue:
- volatility — 100% identical, 0 lines differ
What it actually says
Volatility Strategy
Purpose
Uses percentile ranking of historical volatility (HV) to capture volatility mean reversion: build positions in low-volatility regimes while waiting for volatility expansion, and exit or short in high-volatility regimes to capture contraction.
Signal Logic
- Compute HV: annualized standard deviation of returns over the past
hv_windowdays - Percentile ranking: percentile position of HV within the past
lookbackdays (0-100) - Signal generation:
- Percentile <
low_pct→ go long (volatility is low, waiting for expansion) - Percentile >
high_pct→ exit / go short (volatility is high, waiting for contraction) - Middle region → keep the current position
- Percentile <
Key Implementation Details
- HV =
returns.rolling(hv_window).std() * sqrt(252)(annualized) - Percentile =
hv.rolling(lookback).rank(pct=True) * 100 - For cryptocurrencies, use 365 instead of 252 as the annualization factor
Parameters
| Parameter | Default | Description |
|---|---|---|
| hv_window | 20 | Historical volatility calculation window |
| lookback | 120 | Lookback period for percentile ranking |
| low_pct | 20.0 | Low-volatility threshold (percentile) |
| high_pct | 80.0 | High-volatility threshold (percentile) |
| annualize | 252 | Annualization factor (252 for China A-shares, 365 for crypto) |
Common Pitfalls
- Before the lookback window is filled, there is not enough data to compute percentiles, so the signal should be 0 (
fillna) - Volatility is not direction. Going long in low-volatility regimes does not guarantee price appreciation; it only means volatility expansion is statistically more likely
- Cryptocurrencies trade 7x24, so
annualizeshould be set to 365
Dependencies
pip install pandas numpy
Signal Convention
1= long (low-volatility regime),-1= short (high-volatility regime),0= stand aside
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 52 lines · 29 tokens per session scan A 28f512107497
volatility is a skill published in the GitHub repository HKUDS/Vibe-Trading (33,177 stars, last pushed yesterday), licensed MIT. It adds 29 tokens to every session and 505 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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