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 hedging-strategygit 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/hedging-strategy)<a href="https://agentmods.dev/skills/hkuds/vibe-trading/hedging-strategy"><img src="https://agentmods.dev/badge/skills/hkuds/vibe-trading/hedging-strategy/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/hedging-strategy"><img src="https://agentmods.dev/badge/skills/hkuds/vibe-trading/hedging-strategy.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 | $0.00035 | $0.02774 |
| Opus 5 | $0.00017 | $0.01387 |
| Sonnet 5 | $0.00007 | $0.00555 |
| Haiku 4.5 | $0.00003 | $0.00277 |
Grade A, and why
hedging-strategy 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:
- hedging-strategy — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 266 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Hedging Strategy Design
Overview
Design systematic hedging plans for existing positions, covering linear hedges (futures / ETFs) and nonlinear hedges (options). Output hedge ratios, cost estimates, and execution plans. Core principle: hedging does not eliminate risk; it exchanges unknown losses for known costs.
Core Concepts
1. Beta Hedging (Futures / ETFs)
Principle: hedge portfolio systematic risk (beta) with index futures or ETFs while preserving single-stock alpha.
Hedge ratio calculation:
# Minimum-variance hedge ratio
hedge_ratio = beta_portfolio * (portfolio_value / futures_value)
# Example: hold a 10 million RMB China A-share portfolio, beta = 1.2
# CSI 300 futures (IF) contract value = index level × 300
# IF level = 4000, contract value = 4000 × 300 = 1.2 million
# Required number of short contracts = 1.2 × (1000 / 120) = 10
# Beta estimation method
import numpy as np
# OLS regression: portfolio_returns = alpha + beta * index_returns + epsilon
beta = np.cov(portfolio_returns, index_returns)[0][1] / np.var(index_returns)
China A-share beta hedging instruments:
| Instrument | Code | Contract Multiplier | Margin | Suitable Scale |
|---|---|---|---|---|
| IF (CSI 300 futures) | IF2403 | 300 RMB / point | ~12% | > 5 million RMB |
| IC (CSI 500 futures) | IC2403 | 200 RMB / point | ~14% | > 3 million RMB |
| IM (CSI 1000 futures) | IM2403 | 200 RMB / point | ~15% | > 3 million RMB |
| CSI 300 ETF (510300) | 510300.SH | — | Unlevered | Any size |
Note: stock-index futures have basis (spot-futures spread). Shorting futures when they trade at a discount brings extra return (basis convergence), while premium pricing adds extra cost.
2. Option Hedging Strategies
Protective Put
Hold the underlying + buy a put option
- Cost: option premium (typically 1-3% of underlying value per month)
- Protection range: fully protected below the strike price
- Applicable scenario: worried about a large drawdown but do not want to sell the position
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 · 266 lines · 35 tokens per session scan A fd11996aeed0
hedging-strategy is a skill published in the GitHub repository HKUDS/Vibe-Trading (33,177 stars, last pushed today), licensed MIT. It adds 35 tokens to every session and 2,774 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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