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Alphagbm Hedge Advisor

CommunityPopular
AlphaGBM
alphagbm-hedge-advisor

Scenario-driven hedge recommendations for an existing stock position. Takes ticker + cost basis + purpose, auto-classifies the holding situation (falling knife / bottom-fishing / gain-protection / normal) and returns concrete Long Put, Collar, or Tier-down recommendations with live strikes and premiums from the current option chain. Triggers: "hedge my AAPL", "protect my NVDA gains", "collar strategy MSFT", "long put for TSLA", "how to hedge falling knife COIN", "reduce risk BABA", "lock in gains META", "downside protection", "portfolio hedge", "insurance for position"

Overview

PublisherAlphaGBM
Repositoryskills
Skill namealphagbm-hedge-advisor
Stars
2.7K
Forks
290
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by AlphaGBM on GitHub. Read the source before you install it.

Installation

Install the Alphagbm Hedge Advisor AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/AlphaGBM/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/alphagbm-hedge-advisor .claude/skills/alphagbm-hedge-advisor
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Alphagbm Hedge Advisor in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Alphagbm Hedge Advisor on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Alphagbm Hedge Advisor is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

AlphaGBM Hedge Advisor

"I own AAPL at $140 and it's now $180 — how do I protect the gains?"

Takes that question literally. Given a ticker + cost basis + position purpose, the skill classifies the holding into one of four scenarios and returns ready-to-trade hedge specs with strikes and costs already resolved from the live option chain.

Scenarios

ScenarioTriggerRecommended Hedge
Falling KnifeRecent drawdown ≥ 15% from 30-day high AND PnL ≤ +5%Long Put 5% OTM, 75 DTE, 100% cover, budget ~5%
Bottom FishingPnL within ±8% of cost AND purpose = just_bought or long_termLong Put 5% OTM, 90 DTE, 50-75% cover, budget ~3%
Gain ProtectionPnL ≥ 15%Collar 95/110 (zero-cost or net-credit) + Tier-down as alternative
Normal HoldFallback when no scenario firesPosition rules only, no urgent hedge

What's Returned

For each recommendation spec, the skill resolves actual strikes and prices from the live option chain:

  • Long Put: strike, DTE, cost_per_share, cost_per_contract, cost_pct_of_spot, delta, IV
  • Collar: long_put_strike, short_call_strike, put_cost, call_credit, net_cost_per_share (negative = you receive a credit), breakeven analysis
  • Tier-down / Position rules: static rules copy only

Also returns a position_rules[] array (single-name ≤20%, sector ≤30-35%, cash reserve 10-15%, etc.) for the normal-hold case.

How to Use

Input:

  • ticker (required)
  • cost_basis (required, float — your average entry price)
  • purpose (optional, default long_term) — one of long_term / short_term / pre_earnings / just_bought

Output:

  • Scenario label + reason (zh/en)
  • Current price, cost basis, unrealized P&L %, recent drawdown %
  • recommendations[] — each with type, priority, title, rationale, and resolved block containing the actual priced hedge
  • position_rules[] — always-applicable sizing rules

Example Queries:

  • hedge my AAPL at $140, now it's $180 → Gain Protection → Collar 95/110 quote
  • I just bought NVDA at $110 on the dip, should I hedge? → Falling Knife or Bottom Fishing → Long Put 5% OTM 60-90 DTE
  • how to protect my TSLA position → Gain Protection or Bottom Fishing based on PnL
  • collar MSFT at cost 340 current 410 → Full collar pricing

Mock Data

Mock responses in mock-data/hedge-advisor/ — sample across all four scenarios.

API Endpoint

GET /api/options/hedge-advisor?ticker={SYMBOL}&cost_basis={PRICE}&purpose={PURPOSE}

Query params:

  • ticker (required)
  • cost_basis (required, float > 0)
  • purpose (default long_term) — one of long_term / short_term / pre_earnings / just_bought

Response shape:

json
{
  "success": true,
  "ticker": "AAPL",
  "current_price": 180.0,
  "cost_basis": 140.0,
  "unrealized_pnl_pct": 28.57,
  "recent_drawdown_pct": 3.1,
  "purpose": "long_term",
  "scenario": {
    "scenario": "gain_protection",
    "label_zh": "浮盈怕坐电梯",
    "label_en": "Gain Protection",
    "reason_zh": "已浮盈 28.6%,需要保护已实现收益。",
    "reason_en": "Up 28.6% on cost — protect unrealized gains.",
    "unrealized_pnl_pct": 28.57
  },
  "recommendations": [
    {
      "type": "collar",
      "priority": 1,
      "title_zh": "Collar 95/110 锁定收益",
      "title_en": "Collar 95/110 lock-in",
      "rationale_zh": "...",
      "rationale_en": "...",
      "resolved": {
        "long_put_strike": 170.0,
        "short_call_strike": 200.0,
        "put_cost": 2.15,
        "call_credit": 2.45,
        "net_cost_per_share": -0.30,
        "net_cost_per_contract": -30,
        "is_credit": true,
        "dte": 62
      }
    },
    {"type": "tier_down", "priority": 2, ...}
  ],
  "position_rules": [
    {"rule_zh": "单票仓位 ≤ 20%", "rule_en": "Single ticker ≤20%", ...},
    ...
  ]
}

Pricing: 1 option-analysis credit per call; 5-min cache per (ticker, cost_basis, purpose).

Related Skills

SkillRelevance
alphagbm-options-strategyMulti-leg strategy builder (for custom hedges beyond presets)
alphagbm-greeksGreeks of the resulting hedge position
alphagbm-pnl-simulatorStress-test the hedge at various future prices

Powered by AlphaGBM — Real-data options & research intelligence. 10K+ users.

Frequently asked questions

What does the Alphagbm Hedge Advisor AI skill do?

Scenario-driven hedge recommendations for an existing stock position. Takes ticker + cost basis + purpose, auto-classifies the holding situation (falling knife / bottom-fishing / gain-protection / normal) and returns concrete Long Put, Collar, or Tier-down recommendations with live strikes and premiums from the current option chain. Triggers: "hedge my AAPL", "protect my NVDA gains", "collar strategy MSFT", "long put for TSLA", "how to hedge falling knife COIN", "reduce risk BABA", "lock in gains META", "downside protection", "portfolio hedge", "insurance for position"

Why use Alphagbm Hedge Advisor on TypingMind?

Because you install it once and use it with any model. Alphagbm Hedge Advisor is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Alphagbm Hedge Advisor in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/AlphaGBM/skills/tree/main/skills/alphagbm-hedge-advisor. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Alphagbm Hedge Advisor?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Alphagbm Hedge Advisor?

As many as you like. As long as a model supports skills, you can use Alphagbm Hedge Advisor with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Alphagbm Hedge Advisor AI skill free?

Yes. It is published on GitHub by AlphaGBM under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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