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 factor-researchgit 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/factor-research)<a href="https://agentmods.dev/skills/hkuds/vibe-trading/factor-research"><img src="https://agentmods.dev/badge/skills/hkuds/vibe-trading/factor-research/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/factor-research"><img src="https://agentmods.dev/badge/skills/hkuds/vibe-trading/factor-research.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 | $0.00032 | $0.02672 |
| Opus 5.5 | $0.00013 | $0.01069 |
| Sonnet 5.5 | $0.00006 | $0.00534 |
| Haiku 4.5 | $0.00003 | $0.00267 |
Grade A, and why
factor-research 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 6d 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:
- factor-research — 88% identical, 73 lines differ
How it starts
The opening of the file, as written. The whole thing — 230 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Factor Research Framework
Purpose
Systematically evaluates the predictive power of single or multiple factors. Uses IC/IR statistical tests and quantile backtests to determine whether a factor has stock-selection power, and to guide factor screening and combination.
Applicable scenarios:
- Single-factor validity testing (momentum, value, quality, volatility, and more)
- Determining weights for multi-factor combination
- Factor decay analysis (IC changes across different holding periods)
- Comparing factor differences across industries and markets
Workflow
- Calculate factor values: compute factor exposures for each instrument on the cross-section, and output a factor CSV (
index=date,columns=codes) - Calculate returns: compute each instrument's forward N-day return, and output a return CSV (same structure)
- Call the
factor_analysistool: pass in the factor CSV, return CSV, and output directory - Interpret the results: judge factor validity based on IC/IR criteria and quantile backtest results
- Factor screening / combination: keep effective factors and combine them with equal weights or IC-based weights
Key point: the rows (dates) and columns (instrument codes) of the factor CSV and return CSV must align exactly. Returns must be forward returns after the factor-observation date (to avoid look-ahead bias).
factor_analysis Tool Parameters
| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
| factor_csv | string | Yes | - | Path to the factor-value CSV |
| return_csv | string | Yes | - | Path to the return CSV |
| output_dir | string | Yes | - | Output directory for results |
| n_groups | integer | No | 5 | Number of quantile groups |
Output Files
| File | Contents |
|---|---|
| ic_series.csv | Daily IC series |
| ic_summary.json | IC mean, IC standard deviation, IR, proportion of IC > 0 |
| group_equity.csv | Cumulative equity curves for each quantile group |
What ships with it
2 files 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.
- 6d ago Changed · +29 lines 3fa68f5cf37c
- 9d ago Changed · +40 lines ea2defa3cdee
- 1mo ago First seen · 161 lines · 32 tokens per session scan A 52451596b94f
factor-research is a skill published in the GitHub repository HKUDS/Vibe-Trading (36,887 stars, last pushed today), licensed MIT. It adds 32 tokens to every session and 2,672 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-08-30.
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