Agent SkillsAlphaGBM/skills › alphagbm-options-strategy

alphagbm-options-strategy

GitHub

基于市场观点推荐最优期权策略,支持多种模板并计算盈亏概率。用于制定期权交易计划、构建多腿头寸或对比策略替代方案。

skills/alphagbm-options-strategy/SKILL.md AlphaGBM/skills

Trigger Scenarios

询问特定股票的期权策略 根据看涨/看跌/中性观点寻找策略 针对财报事件规划交易 构建多腿期权组合

Install

npx skills add AlphaGBM/skills --skill alphagbm-options-strategy -g -y
More Options

Use without installing

npx skills use AlphaGBM/skills@alphagbm-options-strategy

指定 Agent (Claude Code)

npx skills add AlphaGBM/skills --skill alphagbm-options-strategy -a claude-code -g -y

安装 repo 全部 skill

npx skills add AlphaGBM/skills --all -g -y

预览 repo 内 skill

npx skills add AlphaGBM/skills --list

SKILL.md

Frontmatter
{
    "name": "alphagbm-options-strategy",
    "globs": [
        "mock-data\/*.json"
    ],
    "description": "Recommends optimal multi-leg option strategies based on your market view (bullish, bearish, neutral, volatile). Supports 15+ strategy templates including spreads, condors, straddles, and income plays. Returns full P&L profile, breakevens, and probability of profit. Use when: choosing an options strategy, planning a trade around earnings, building a multi-leg position, comparing strategy alternatives. Triggers on: \"options strategy for AAPL\", \"bullish strategy NVDA\", \"what's the best play on TSLA earnings\", \"iron condor SPY\", \"bear put spread META\", \"income strategy for GOOGL\", \"neutral play on QQQ\".\n"
}

AlphaGBM Options Strategy

Prerequisites

  • API Key: Set env ALPHAGBM_API_KEY (format agbm_xxxx...).
  • Base URL: Default https://alphagbm.zeabur.app. Override with env ALPHAGBM_BASE_URL.

What This Skill Does

Given a market view and a ticker, recommends the best multi-leg option strategies ranked by risk/reward profile. Selects optimal strikes and expirations automatically using AlphaGBM's scoring engine.

Four Core Strategies and Trend Alignment

Strategy Ideal Trend Max Profit Max Loss
Sell Put Neutral / Bullish Premium received Strike - Premium (assignment risk)
Sell Call Neutral / Bearish Premium received Unlimited (uncovered)
Buy Call Bullish Unlimited Premium paid
Buy Put Bearish Strike - Premium Premium paid

Trend alignment scoring: The scoring model rewards contracts that match the prevailing trend. For Sell Put, a downtrend scores 100 (counter-intuitive: you want to sell puts into weakness for higher premium), while an uptrend scores 30. For Buy Call, bullish momentum is weighted at 25%.

Supported Strategy Templates (15+)

Category Strategies
Bullish Bull Call Spread, Bull Put Spread, Long Call, Covered Call, Synthetic Long
Bearish Bear Put Spread, Bear Call Spread, Long Put, Synthetic Short
Neutral Iron Condor, Iron Butterfly, Short Straddle, Short Strangle, Calendar Spread
Volatile Long Straddle, Long Strangle, Butterfly Spread, Reverse Iron Condor
Income Covered Call, Cash-Secured Put, Collar, Jade Lizard

Risk-Return Profiles

Style Typical Win Rate Typical Return
steady_income 65-80% 1-5%/month
balanced 40-55% 50-200%
high_risk_high_reward 20-40% 2-10x
hedge 30-50% 0-1x

Strategy Selection Logic

  1. Match user's market view to candidate strategies
  2. Filter by IV environment (high IV favors selling premium; low IV favors buying)
  3. Score each candidate using risk/reward, probability of profit, and capital efficiency
  4. Rank and return the top 3 recommendations with full details

API Endpoints

Strategy Templates

List all available strategy templates:

GET /api/options/tools/strategy/templates

Strategy Builder

Build a strategy from a template with specific parameters:

POST /api/options/tools/strategy/build
Content-Type: application/json

{
  "mode": "template",
  "template_id": "bull_call_spread",
  "spot": 150.0,
  "expiry_days": 30,
  "strikes": [140, 145, 150, 155, 160]
}

Options Scanner

Scan across tickers for strategies matching your criteria:

POST /api/options/tools/scan
Content-Type: application/json

{
  "strategies": ["covered_call", "cash_secured_put"],
  "tickers": ["AAPL", "NVDA"],
  "min_yield_pct": 1.0
}

How to Use

Input

  • Required: Ticker symbol + market view (bullish / bearish / neutral / volatile)
  • Optional: Max capital, target expiration, risk tolerance (conservative / moderate / aggressive)

Output Structure

{
  "ticker": "AAPL",
  "price": 218.45,
  "market_view": "bullish",
  "iv_environment": "moderate",
  "recommendations": [
    {
      "strategy": "Bull Call Spread",
      "rank": 1,
      "score": 8.5,
      "legs": [
        {"action": "buy", "type": "call", "strike": 215, "expiry": "2026-04-18", "price": 7.20},
        {"action": "sell", "type": "call", "strike": 225, "expiry": "2026-04-18", "price": 3.40}
      ],
      "max_profit": 620,
      "max_loss": 380,
      "breakeven": [218.80],
      "probability_of_profit": 0.58,
      "risk_reward_ratio": 1.63,
      "net_debit": 380,
      "greeks": {
        "delta": 0.32,
        "gamma": 0.012,
        "theta": -0.08,
        "vega": 0.14
      },
      "rationale": "Moderate bullish exposure with capped risk. IV is fair -- debit spread preferred over naked call."
    }
  ]
}

Example Queries

User Says What Happens
"Options strategy for AAPL" Infers view from stock analysis, returns top 3 strategies
"Bullish strategy NVDA" Filters to bullish strategies, ranks by score
"Best play on TSLA earnings" Selects volatile strategies (straddle, strangle) for event
"Iron condor SPY" Builds an iron condor with optimal strikes and returns full profile
"Income strategy GOOGL" Filters to covered call, cash-secured put, collar
"Conservative bearish play on META" Bear put spread or collar with tight risk parameters

Mock Data

Demo tickers available without API key: AAPL, NVDA, SPY, TSLA, META. Strategy recommendations use realistic chain data from mock-data/.

Related Skills

  • alphagbm-options-score -- Scores the individual contracts used in each leg
  • alphagbm-pnl-simulator -- Simulate P&L over time for any recommended strategy
  • alphagbm-greeks -- Deep-dive into position Greeks for the chosen strategy
  • alphagbm-iv-rank -- Check if IV environment favors buying or selling premium

Powered by AlphaGBM -- Real-data options & research intelligence for traders and AI agents. 10K+ users.

Version History

  • c69fa1b Current 2026-07-05 20:18

Same Skill Collection

skills/alphagbm-alert/SKILL.md
skills/alphagbm-compare/SKILL.md
skills/alphagbm-earnings-crush/SKILL.md
skills/alphagbm-market-sentiment/SKILL.md
skills/alphagbm-options-score/SKILL.md
skills/alphagbm-polymarket/SKILL.md
skills/alphagbm-stock-analysis/SKILL.md
skills/alphagbm-unusual-activity/SKILL.md
skills/alphagbm-watchlist/SKILL.md
skills/alphagbm-bps-backtest/SKILL.md
skills/alphagbm-buffett-analysis/SKILL.md
skills/alphagbm-chokepoint/SKILL.md
skills/alphagbm-company-profile/SKILL.md
skills/alphagbm-duan-analysis/SKILL.md
skills/alphagbm-fear-score/SKILL.md
skills/alphagbm-greeks/SKILL.md
skills/alphagbm-health-check/SKILL.md
skills/alphagbm-hedge-advisor/SKILL.md
skills/alphagbm-investment-thesis/SKILL.md
skills/alphagbm-iv-rank/SKILL.md
skills/alphagbm-macro-view/SKILL.md
skills/alphagbm-marks-cycle/SKILL.md
skills/alphagbm-pnl-simulator/SKILL.md
skills/alphagbm-take-profit/SKILL.md
skills/alphagbm-tepper-signal/SKILL.md
skills/alphagbm-theme-research/SKILL.md
skills/alphagbm-vix-status/SKILL.md
skills/alphagbm-vol-smile/SKILL.md
skills/alphagbm-vol-surface/SKILL.md

Metadata

Files
0
Version
6ecea74
Hash
f69f3da4
Indexed
2026-07-05 20:18

Главная - Вики-сайт
Copyright © 2011-2026 iteam. Current version is 2.155.2. UTC+08:00, 2026-08-06 05:18
浙ICP备14020137号-1 $Гость$