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 execution-modelgit 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/execution-model)<a href="https://agentmods.dev/skills/hkuds/vibe-trading/execution-model"><img src="https://agentmods.dev/badge/skills/hkuds/vibe-trading/execution-model/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/execution-model"><img src="https://agentmods.dev/badge/skills/hkuds/vibe-trading/execution-model.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 | $0.00041 | $0.03229 |
| Opus 5.5 | $0.00016 | $0.01292 |
| Sonnet 5.5 | $0.00008 | $0.00646 |
| Haiku 4.5 | $0.00004 | $0.00323 |
Grade A, and why
execution-model 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 1mo 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.
How it starts
The opening of the file, as written. The whole thing — 336 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Trade Execution Modeling
Overview
Provide more realistic execution assumptions for backtests, including slippage models, market-impact estimation, and execution-algorithm principles. This skill is for backtest simulation only and does not involve live order execution.
Slippage Models
Why Slippage Models Are Needed
Idealized backtest: filled at the close, zero slippage
Real world:
1. The order book has a bid-ask spread
2. Large orders push prices (market impact)
3. Execution is delayed (there is latency from signal to fill)
No slippage model -> overly optimistic backtest -> losses in live trading
Do not retype these models. All four are implemented and tested in
src/quantlib/impact.py; import them. The tested versions validate their inputs —
a zero ADV raises instead of dividing by zero, and a negative delay_bars raises
instead of silently introducing look-ahead bias.
from src.quantlib.impact import fixed_slippage, linear_impact, sqrt_impact, delayed_execution
1. Fixed Slippage Model
fixed_slippage(price=100.0, direction=1, bps=5.0) # 100.05 (buy pays up)
fixed_slippage(price=100.0, direction=-1, bps=5.0) # 99.95 (sell receives less)
direction is 1 to buy or -1 to sell, and must be exactly one of those — it
multiplies the impact, so an unchecked 2 would silently double the modelled cost.
bps defaults to DEFAULT_SLIPPAGE_BPS (5.0).
Reference fixed-slippage assumptions by market:
| Market | Instrument | Suggested Slippage (bps) | Notes |
|---|---|---|---|
| China A-share large cap | CSI 300 constituents | 3-5 | Good liquidity |
| China A-share small cap | CSI 1000 constituents | 5-10 | Average liquidity |
| China micro-cap | market cap < 5 billion RMB | 10-30 | Poor liquidity |
| US large cap | AAPL / MSFT | 1-3 | Excellent liquidity |
| Hong Kong stocks | Hang Seng constituents | 5-10 | Less liquid than A / US |
| BTC spot | BTC-USDT | 2-5 | Good OKX liquidity |
| ETH spot | ETH-USDT | 3-8 | Slightly worse than BTC |
| Small altcoins | other -USDT pairs |
10-50 | Liquidity varies widely |
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.
- 1mo ago First seen · 336 lines · 41 tokens per session scan A da126eefad69
execution-model is a skill published in the GitHub repository HKUDS/Vibe-Trading (36,591 stars, last pushed yesterday), licensed MIT. It adds 41 tokens to every session and 3,229 once invoked, about $0.0002 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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