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Alphagbm Options Strategy

核心功能:支持15+期权策略模板,根据市场观点(多/空/中性/波动)与个股,通过风险回报、胜率等维度评分,自动筛选最优行权价与到期日,输出Top3策略及盈亏、盈亏平衡点、胜率等详细数据。典型场景:期权策略选择、财报季交易规划、多腿仓位构建、策略对比。输入需个股代码+市场观点,可选风险承受力等,支持模拟数据无需API即可体验。

clementgu
clementgu
热度 13.89
最近更新 2026-07-23 14:14:50
分类:数据依赖:第三方 API / 读写文件 / 内部调用大模型应用场景:财务 / 市场分析 / 数据处理 / 推荐

安装技能

复制下面的话术发给你的 Agent,它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill: npx secure-skills-cli add alphagbm-options-strategy
Metadata
name
alphagbm-options-strategy
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".
globs
["mock-data/*.json"]

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.