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 behavioral-financegit 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/behavioral-finance)<a href="https://agentmods.dev/skills/hkuds/vibe-trading/behavioral-finance"><img src="https://agentmods.dev/badge/skills/hkuds/vibe-trading/behavioral-finance/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/behavioral-finance"><img src="https://agentmods.dev/badge/skills/hkuds/vibe-trading/behavioral-finance.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 | $0.00042 | $0.02377 |
| Opus 5 | $0.00021 | $0.01189 |
| Sonnet 5 | $0.00008 | $0.00475 |
| Haiku 4.5 | $0.00004 | $0.00238 |
Grade A, and why
behavioral-finance 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 11d 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:
- behavioral-finance — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 209 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Behavioral Finance Applications
Overview
Translate behavioral-finance theory into quantifiable trading signals and risk-control rules. Core assumption: market participants systematically deviate from rational decision-making, and these biases can be predicted and exploited.
Applicable scenarios:
- Behavioral interpretation and parameter optimization for momentum / reversal strategies
- Contrarian signals when market sentiment becomes extreme
- Debiasing mechanisms in portfolio construction
- Capturing behavior patterns specific to retail-driven China A-share markets
Core Concepts
Overreaction and Underreaction
Underreaction → momentum effect:
Mechanism: anchoring bias + conservatism
Investors anchor on old information and update insufficiently to new information
After an earnings beat, the stock price digests it gradually rather than all at once
China A-share evidence:
- Earnings-guidance beats still produce 3-5% excess return over the following 20 days
- After analyst rating upgrades, momentum often persists for 1-3 months
Quant signal:
SUE (standardized unexpected earnings) > 2σ -> buy and hold for 60 days
Top 10% 20-day return -> continue holding for 20 days (China A-share momentum cycles are shorter)
Overreaction → reversal effect:
Mechanism: representativeness heuristic + availability bias
Investors extrapolate recent trends too aggressively and ignore mean reversion
Panic / euphoria drives reactions beyond what fundamentals support
China A-share evidence:
- Rebounds after consecutive limit-downs (after 3 limit-downs, the average 20-day rebound is 8%)
- Big annual losers often earn 5-10% excess return the next year
Quant signal:
Bottom 10% of 250-day return -> buy and hold for 250 days
RSI(5) < 10 -> short-term rebound signal (5-10 days)
Key distinction:
| Dimension | Underreaction (Momentum) | Overreaction (Reversal) |
|---|---|---|
| Time scale | 1-12 months | <1 week or >12 months |
| Information type | Clear events (earnings / announcements) | Ambiguous information (sentiment / trend) |
| Best China A-share window | 20-60 days | 5-10 days (short term) / 1 year (long term) |
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.
- 11d ago First seen · 209 lines · 42 tokens per session scan A 1ecc91cc4d5c
behavioral-finance is a skill published in the GitHub repository HKUDS/Vibe-Trading (33,177 stars, last pushed yesterday), licensed MIT. It adds 42 tokens to every session and 2,377 once invoked, about $0.0002 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 2026-08-30.
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