options-strategy-advisor

期权策略顾问

概览

此技能使用理论定价模型，提供全面的期权策略分析与教育，帮助交易者了解、分析和模拟期权策略，而无需订阅实时市场数据服务。

核心能力：

• Black-Scholes 定价：计算期权理论价格和希腊值
• 策略模拟：分析主要期权策略的盈亏
• 财报策略：结合财报日历，进行财报前波动率交易
• 风险管理：分析仓位规模、希腊值敞口、最大亏损和最大盈利
• 教育导向：详细说明策略和风险指标

数据来源：

• FMP API：股票价格、历史波动率、股息、财报日期
• 用户输入：隐含波动率（IV）、无风险利率
• 理论模型：使用 Black-Scholes 计算定价和希腊值

前置条件

必需：

• Python 3.9+，以及 numpy、scipy、requests

可选：

• FMP API 密钥（用于获取实时股价和历史波动率）
  • 通过 FMP_API_KEY 环境变量或 --api-key 参数设置
  • 无 API 密钥时：手动输入股价和波动率

安装：

pip install numpy scipy requests

快速入门示例：

# Basic call option pricing (no API key needed)
python3 scripts/black_scholes.py

# With FMP API key for real-time data
python3 scripts/black_scholes.py --ticker AAPL --api-key $FMP_API_KEY

# Custom option parameters
python3 scripts/black_scholes.py --stock-price 180 --strike 185 --days 30 --volatility 0.25

# Put option analysis
python3 scripts/black_scholes.py --stock-price 180 --strike 175 --days 30 --option-type put

何时使用此技能

在以下情况下使用此技能：

• 用户询问期权策略（“什么是备兑看涨期权？”、“铁鹰策略如何运作？”）
• 用户想要模拟策略盈亏（“看涨看涨价差的最大盈利是多少？”）
• 用户需要希腊值分析（“我的 Delta 敞口是多少？”）
• 用户询问财报策略（“财报发布前应该买入跨式组合吗？”）
• 用户想要比较策略（“备兑看涨期权和保护性看跌期权，哪个更好？”）
• 用户需要仓位规模建议（“我应该交易多少份合约？”）
• 用户询问波动率（“当前 IV 高吗？”）

请求示例：

• “分析 AAPL 的备兑看涨期权”
• “MSFT 的 $100/$105 看涨看涨价差盈亏是多少？”
• “NVDA 发布财报前，我应该交易跨式组合吗？”
• “计算我的铁鹰仓位的希腊值”
• “比较保护性看跌期权和备兑看涨期权的下行保护效果”

支持的策略

收益策略

1. 备兑看涨期权 - 持有股票并卖出看涨期权（获取收益，限制上涨空间）
2. 现金担保看跌期权 - 以现金作担保卖出看跌期权（收取权利金，并愿意买入股票）
3. 穷人版备兑看涨期权 - LEAPS 看涨期权 + 卖出近期看涨期权（资本效率更高）

保护策略

4. 保护性看跌期权 - 持有股票并买入看跌期权（保险，限制下行风险）
5. 领口策略 - 持有股票，卖出看涨期权 + 买入看跌期权（限制上涨和下跌空间）

方向性策略

6. 看涨看涨价差 - 买入较低行权价的看涨期权，卖出较高行权价的看涨期权（风险和收益有限的看涨策略）
7. 看涨看跌价差 - 卖出较高行权价的看跌期权，买入较低行权价的看跌期权（信用价差，看涨）
8. 看跌看涨价差 - 卖出较低行权价的看涨期权，买入较高行权价的看涨期权（信用价差，看跌）
9. 看跌看跌价差 - 买入较高行权价的看跌期权，卖出较低行权价的看跌期权（风险和收益有限的看跌策略）

波动率策略

10. 买入跨式组合 - 买入平值看涨期权 + 平值看跌期权（从任一方向的大幅波动中获利）
11. 买入宽跨式组合 - 买入虚值看涨期权 + 虚值看跌期权（成本低于跨式组合，但需要更大幅度的波动）
12. 卖出跨式组合 - 卖出平值看涨期权 + 平值看跌期权（从价格不变中获利，风险无限）
13. 卖出宽跨式组合 - 卖出虚值看涨期权 + 虚值看跌期权（从价格不变中获利，获利区间更宽）

区间策略

14. 铁鹰策略 - 看涨看跌价差 + 看跌看涨价差（从价格在区间内波动中获利）
15. 铁蝶策略 - 卖出平值跨式组合，买入虚值宽跨式组合（从价格在较窄区间内波动中获利）

高级策略

16. 日历价差 - 卖出近期到期期权，买入较远期到期期权（从时间衰减中获利）
17. 对角价差 - 行权价不同的日历价差（方向性 + 时间衰减）
18. 比例价差 - 不平衡价差（一条腿上的合约数量更多）

分析工作流

第 1 步：收集输入数据

用户必填：

• 股票代码
• 策略类型
• 行权价
• 到期日
• 仓位规模（合约数量）

用户选填：

• 隐含波动率（IV）——若未提供，则使用历史波动率（HV）
• 无风险利率——默认使用当前 3 个月期国库券利率（截至 2025 年约为 5.3%）

从 FMP API 获取：

• 当前股价
• 历史价格（用于计算 HV）
• 股息率
• 即将公布的财报日期（用于财报策略）

用户输入示例：

Ticker: AAPL
Strategy: Bull Call Spread
Long Strike: $180
Short Strike: $185
Expiration: 30 days
Contracts: 10
IV: 25% (or use HV if not provided)

第 2 步：计算历史波动率（未提供 IV 时）

目标： 根据历史价格走势估算波动率。

方法：

# Fetch 90 days of price data
prices = get_historical_prices("AAPL", days=90)

# Calculate daily returns
returns = np.log(prices / prices.shift(1))

# Annualized volatility
HV = returns.std() * np.sqrt(252)  # 252 trading days

输出：

• 历史波动率（年化百分比）
• 给用户的提示：“HV = 24.5%，建议使用当前市场 IV，以提高准确性”

用户可以覆盖该值：

• 提供来自券商平台（ThinkorSwim、TastyTrade 等）的 IV
• 脚本接受 --iv 28.0 参数

第 3 步：使用 Black-Scholes 为期权定价

Black-Scholes 模型：

对于欧式期权：

Call Price = S * N(d1) - K * e^(-r*T) * N(d2)
Put Price = K * e^(-r*T) * N(-d2) - S * N(-d1)

Where:
d1 = [ln(S/K) + (r + σ²/2) * T] / (σ * √T)
d2 = d1 - σ * √T

S = Current stock price
K = Strike price
r = Risk-free rate
T = Time to expiration (years)
σ = Volatility (IV or HV)
N() = Cumulative standard normal distribution

调整：

• 对看涨期权，从 S 中减去股息现值
• 美式期权：使用近似值，或注明“欧式定价可能会低估美式期权价格”

Python 实现：

from scipy.stats import norm
import numpy as np

def black_scholes_call(S, K, T, r, sigma, q=0):
    """
    S: Stock price
    K: Strike price
    T: Time to expiration (years)
    r: Risk-free rate
    sigma: Volatility
    q: Dividend yield
    """
    d1 = (np.log(S/K) + (r - q + 0.5*sigma**2)*T) / (sigma*np.sqrt(T))
    d2 = d1 - sigma*np.sqrt(T)

    call_price = S*np.exp(-q*T)*norm.cdf(d1) - K*np.exp(-r*T)*norm.cdf(d2)
    return call_price

def black_scholes_put(S, K, T, r, sigma, q=0):
    d1 = (np.log(S/K) + (r - q + 0.5*sigma**2)*T) / (sigma*np.sqrt(T))
    d2 = d1 - sigma*np.sqrt(T)

    put_price = K*np.exp(-r*T)*norm.cdf(-d2) - S*np.exp(-q*T)*norm.cdf(-d1)
    return put_price

每条期权腿的输出：

• 理论价格
• 提示：“市场价格可能因买卖价差以及美式与欧式定价的差异而有所不同”

第 4 步：计算希腊值

希腊值用于衡量期权价格对各种因素的敏感度：

Delta（Δ）： 股价每变动 $1，期权价格的变化量

def delta_call(S, K, T, r, sigma, q=0):
    d1 = (np.log(S/K) + (r - q + 0.5*sigma**2)*T) / (sigma*np.sqrt(T))
    return np.exp(-q*T) * norm.cdf(d1)

def delta_put(S, K, T, r, sigma, q=0):
    d1 = (np.log(S/K) + (r - q + 0.5*sigma**2)*T) / (sigma*np.sqrt(T))
    return np.exp(-q*T) * (norm.cdf(d1) - 1)

Gamma（Γ）： 股价每变动 $1，Delta 的变化量

def gamma(S, K, T, r, sigma, q=0):
    d1 = (np.log(S/K) + (r - q + 0.5*sigma**2)*T) / (sigma*np.sqrt(T))
    return np.exp(-q*T) * norm.pdf(d1) / (S * sigma * np.sqrt(T))

Theta（Θ）： 期权价格每天的变化量（时间损耗）

def theta_call(S, K, T, r, sigma, q=0):
    d1 = (np.log(S/K) + (r - q + 0.5*sigma**2)*T) / (sigma*np.sqrt(T))
    d2 = d1 - sigma*np.sqrt(T)

    theta = (-S*norm.pdf(d1)*sigma*np.exp(-q*T)/(2*np.sqrt(T))
             - r*K*np.exp(-r*T)*norm.cdf(d2)
             + q*S*norm.cdf(d1)*np.exp(-q*T))

    return theta / 365  # Per day

Vega（ν）： 波动率每变动 1%，期权价格的变化量

def vega(S, K, T, r, sigma, q=0):
    d1 = (np.log(S/K) + (r - q + 0.5*sigma**2)*T) / (sigma*np.sqrt(T))
    return S * np.exp(-q*T) * norm.pdf(d1) * np.sqrt(T) / 100  # Per 1%

Rho（ρ）： 利率每变动 1%，期权价格的变化量

def rho_call(S, K, T, r, sigma, q=0):
    d2 = (np.log(S/K) + (r - q + 0.5*sigma**2)*T) / (sigma*np.sqrt(T)) - sigma*np.sqrt(T)
    return K * T * np.exp(-r*T) * norm.cdf(d2) / 100  # Per 1%

仓位希腊值：

对于包含多条腿的策略，将所有腿的希腊值相加：

# Example: Bull Call Spread
# Long 1x $180 call
# Short 1x $185 call

delta_position = (1 * delta_long) + (-1 * delta_short)
gamma_position = (1 * gamma_long) + (-1 * gamma_short)
theta_position = (1 * theta_long) + (-1 * theta_short)
vega_position = (1 * vega_long) + (-1 * vega_short)

希腊值解读：

希腊值	含义	示例
Delta	方向性敞口	Δ = 0.50 → 股价上涨 $1 时盈利 $50
Gamma	Delta 加速变化	Γ = 0.05 → 股价上涨 $1 时，Delta 增加 0.05
Theta	每日时间损耗	Θ = -$5 → 时间流逝每天亏损 $5
Vega	波动率敏感度	ν = $10 → IV 上升 1% 时盈利 $10
Rho	利率敏感度	ρ = $2 → 利率上升 1% 时盈利 $2

第 5 步：模拟策略盈亏

目标： 计算到期时不同股价下的盈亏。

方法：

生成股价范围（例如，当前股价上下浮动 ±30%）：

current_price = 180
price_range = np.linspace(current_price * 0.7, current_price * 1.3, 100)

针对每个价格点计算盈亏：

def calculate_pnl(strategy, stock_price_at_expiration):
    pnl = 0

    for leg in strategy.legs:
        if leg.type == 'call':
            intrinsic_value = max(0, stock_price_at_expiration - leg.strike)
        else:  # put
            intrinsic_value = max(0, leg.strike - stock_price_at_expiration)

        if leg.position == 'long':
            pnl += (intrinsic_value - leg.premium_paid) * 100  # Per contract
        else:  # short
            pnl += (leg.premium_received - intrinsic_value) * 100

    return pnl * num_contracts

关键指标：

• 最大盈利：可能达到的最高盈亏
• 最大亏损：最差情况下的盈亏
• 盈亏平衡点：盈亏为 0 时对应的股价
• 盈利概率：盈利的价格区间占比（简化估算）

输出示例：

Bull Call Spread: $180/$185 on AAPL (30 DTE, 10 contracts)

Current Price: $180.00
Net Debit: $2.50 per spread ($2,500 total)

Max Profit: $2,500 (at $185+)
Max Loss: -$2,500 (at $180-)
Breakeven: $182.50
Risk/Reward: 1:1

Probability Profit: ~55% (if stock stays above $182.50)

第 6 步：生成盈亏图（ASCII 艺术图）

以可视化方式呈现不同股价下的盈亏：

def generate_pnl_diagram(price_range, pnl_values, current_price, width=60, height=15):
    """Generate ASCII P/L diagram"""

    # Normalize to chart dimensions
    max_pnl = max(pnl_values)
    min_pnl = min(pnl_values)

    lines = []
    lines.append(f"\nP/L Diagram: {strategy_name}")
    lines.append("-" * width)

    # Y-axis levels
    levels = np.linspace(max_pnl, min_pnl, height)

    for level in levels:
        if abs(level) < (max_pnl - min_pnl) * 0.05:
            label = f"    0 |"  # Zero line
        else:
            label = f"{level:6.0f} |"

        row = label
        for i in range(width - len(label)):
            idx = int(i / (width - len(label)) * len(price_range))
            pnl = pnl_values[idx]
            price = price_range[idx]

            # Determine character
            if abs(pnl - level) < (max_pnl - min_pnl) / height:
                if pnl > 0:
                    char = '█'  # Profit
                elif pnl < 0:
                    char = '░'  # Loss
                else:
                    char = '─'  # Breakeven
            elif abs(level) < (max_pnl - min_pnl) * 0.05:
                char = '─'  # Zero line
            elif abs(price - current_price) < (price_range[-1] - price_range[0]) * 0.02:
                char = '│'  # Current price line
            else:
                char = ' '

            row += char

        lines.append(row)

    lines.append(" " * 6 + "|" + "-" * (width - 6))
    lines.append(" " * 6 + f"${price_range[0]:.0f}" + " " * (width - 20) + f"${price_range[-1]:.0f}")
    lines.append(" " * (width // 2 - 5) + "Stock Price")

    return "\n".join(lines)

输出示例：

P/L Diagram: Bull Call Spread $180/$185
------------------------------------------------------------
 +2500 |                               ████████████████████
       |                         ██████
       |                   ██████
       |             ██████
     0 |       ──────
       | ░░░░░░
       |░░░░░░
 -2500 |░░░░░
      |____________________________________________________________
       $126                  $180                   $234
                          Stock Price

Legend: █ Profit  ░ Loss  ── Breakeven  │ Current Price

第 7 步：针对策略类型进行分析

根据策略类型提供定制建议：

备兑看涨期权：

Income Strategy: Generate premium while capping upside

Setup:
- Own 100 shares of AAPL @ $180
- Sell 1x $185 call (30 DTE) for $3.50

Max Profit: $850 (Stock at $185+ = $5 stock gain + $3.50 premium)
Max Loss: Unlimited downside (stock ownership)
Breakeven: $176.50 (Cost basis - premium received)

Greeks:
- Delta: -0.30 (reduces stock delta from 1.00 to 0.70)
- Theta: +$8/day (time decay benefit)

Assignment Risk: If AAPL > $185 at expiration, shares called away

When to Use:
- Neutral to slightly bullish
- Want income in sideways market
- Willing to sell stock at $185

Exit Plan:
- Buy back call if stock rallies strongly (preserve upside)
- Let expire if stock stays below $185
- Roll to next month if want to keep shares

保护性看跌期权：

Insurance Strategy: Limit downside while keeping upside

Setup:
- Own 100 shares of AAPL @ $180
- Buy 1x $175 put (30 DTE) for $2.00

Max Profit: Unlimited (stock can rise infinitely)
Max Loss: -$7 per share = ($5 stock loss + $2 premium)
Breakeven: $182 (Cost basis + premium paid)

Greeks:
- Delta: +0.80 (stock delta 1.00 - put delta 0.20)
- Theta: -$6/day (time decay cost)

Protection: Guaranteed to sell at $175, no matter how far stock falls

When to Use:
- Own stock, worried about short-term drop
- Earnings coming up, want protection
- Alternative to stop-loss (can't be stopped out)

Cost: "Insurance premium" - typically 1-3% of stock value

Exit Plan:
- Let expire worthless if stock rises (cost of insurance)
- Exercise put if stock falls below $175
- Sell put if stock drops but want to keep shares

铁鹰式期权：

Range-Bound Strategy: Profit from low volatility

Setup (example on AAPL @ $180):
- Sell $175 put for $1.50
- Buy $170 put for $0.50
- Sell $185 call for $1.50
- Buy $190 call for $0.50

Net Credit: $2.00 ($200 per iron condor)

Max Profit: $200 (if stock stays between $175-$185)
Max Loss: $300 (if stock moves outside $170-$190)
Breakevens: $173 and $187
Profit Range: $175 to $185 (58% probability)

Greeks:
- Delta: ~0 (market neutral)
- Theta: +$15/day (time decay benefit)
- Vega: -$25 (short volatility)

When to Use:
- Expect low volatility, range-bound movement
- After big move, think consolidation
- High IV environment (sell expensive options)

Risk: Unlimited if one side tested
- Use stop loss at 2x credit received (exit at -$400)

Adjustments:
- If tested on one side, roll that side out in time
- Close early at 50% max profit to reduce tail risk

第 8 步：财报策略分析

与财报日历集成：

用户询问财报策略时，获取财报日期：

from earnings_calendar import get_next_earnings_date

earnings_date = get_next_earnings_date("AAPL")
days_to_earnings = (earnings_date - today).days

财报发布前策略：

买入跨式/宽跨式：

Setup (AAPL @ $180, earnings in 7 days):
- Buy $180 call for $5.00
- Buy $180 put for $4.50
- Total Cost: $9.50

Thesis: Expect big move (>5%) but unsure of direction

Breakevens: $170.50 and $189.50
Profit if: Stock moves >$9.50 in either direction

Greeks:
- Delta: ~0 (neutral)
- Vega: +$50 (long volatility)
- Theta: -$25/day (time decay hurts)

IV Crush Risk: ⚠️ CRITICAL
- Pre-earnings IV: 40% (elevated)
- Post-earnings IV: 25% (typical)
- IV drop: -15 points = -$750 loss even if stock doesn't move!

Analysis:
- Implied Move: √(DTE/365) × IV × Stock Price
  = √(7/365) × 0.40 × 180 = ±$10.50
- Breakeven Move Needed: ±$9.50
- Probability Profit: ~30-40% (implied move > breakeven move)

Recommendation:
✅ Consider if you expect >10% move (larger than implied)
❌ Avoid if expect normal ~5% earnings move (IV crush will hurt)

Alternative: Buy further OTM strikes to reduce cost
- $175/$185 strangle cost $4.00 (need >$8 move, but cheaper)

卖出铁鹰式期权：

Setup (AAPL @ $180, earnings in 7 days):
- Sell $170/$175 put spread for $2.00
- Sell $185/$190 call spread for $2.00
- Net Credit: $4.00

Thesis: Expect stock to stay range-bound ($175-$185)

Profit Zone: $175 to $185
Max Profit: $400
Max Loss: $100

IV Crush Benefit: ✅
- Short high IV before earnings
- IV drops after earnings → profit on vega
- Even if stock moves slightly, IV drop helps

Greeks:
- Delta: ~0 (market neutral)
- Vega: -$40 (short volatility - good here!)
- Theta: +$20/day

Recommendation:
✅ Good if expect normal earnings reaction (<8% move)
✅ Benefit from IV crush regardless of direction
⚠️ Risk if stock gaps outside range (>10% move)

Exit Plan:
- Close next day if IV crushed (capture profit early)
- Use stop loss if one side tested (-2x credit)

第 9 步：风险管理指导

仓位规模：

Account Size: $50,000
Risk Tolerance: 2% per trade = $1,000 max risk

Iron Condor Example:
- Max loss per spread: $300
- Max contracts: $1,000 / $300 = 3 contracts
- Actual position: 3 iron condors

Bull Call Spread Example:
- Debit paid: $2.50 per spread
- Max contracts: $1,000 / $250 = 4 contracts
- Actual position: 4 spreads

投资组合希腊值管理：

Portfolio Guidelines:
- Delta: -10 to +10 (mostly neutral)
- Theta: Positive preferred (seller advantage)
- Vega: Monitor if >$500 (IV risk)

Current Portfolio:
- Delta: +5 (slightly bullish)
- Theta: +$150/day (collecting $150 daily)
- Vega: -$300 (short volatility)

Interpretation:
✅ Neutral delta (safe)
✅ Positive theta (time working for you)
⚠️ Short vega: If IV spikes, lose $300 per 1% IV increase
→ Reduce short premium positions if VIX rising

调整与退出：

Exit Rules by Strategy:

Covered Call:
- Profit: 50-75% of max profit
- Loss: Stock drops >5%, buy back call to preserve upside
- Time: 7-10 DTE, roll to avoid assignment

Spreads:
- Profit: 50% of max profit (close early, reduce tail risk)
- Loss: 2x debit paid (cut losses early)
- Time: 21 DTE, close or roll (avoid gamma risk)

Iron Condor:
- Profit: 50% of credit (close early common)
- Loss: One side tested, 2x credit lost
- Adjustment: Roll tested side out in time

Straddle/Strangle:
- Profit: Stock moved >breakeven, close immediately
- Loss: Theta eating position, stock not moving
- Time: Day after earnings (if earnings play)

输出格式

策略分析报告模板：

# Options Strategy Analysis: [Strategy Name]

**Symbol:** [TICKER]
**Strategy:** [Strategy Type]
**Expiration:** [Date] ([DTE] days)
**Contracts:** [Number]

---

## Strategy Setup

### Leg Details
| Leg | Type | Strike | Price | Position | Quantity |
|-----|------|--------|-------|----------|----------|
| 1 | Call | $180 | $5.00 | Long | 1 |
| 2 | Call | $185 | $2.50 | Short | 1 |

**Net Debit/Credit:** $2.50 debit ($250 total for 1 spread)

---

## Profit/Loss Analysis

**Max Profit:** $250 (at $185+)
**Max Loss:** -$250 (at $180-)
**Breakeven:** $182.50
**Risk/Reward Ratio:** 1:1

**Probability Analysis:**
- Probability of Profit: ~55% (stock above $182.50)
- Expected Value: $25 (simplified)

---

## P/L Diagram

[ASCII art diagram here]

---

## Greeks Analysis

### Position Greeks (1 spread)
- **Delta:** +0.20 (gains $20 if stock +$1)
- **Gamma:** +0.03 (delta increases by 0.03 if stock +$1)
- **Theta:** -$5/day (loses $5 per day from time decay)
- **Vega:** +$8 (gains $8 if IV increases 1%)

### Interpretation
- **Directional Bias:** Slightly bullish (positive delta)
- **Time Decay:** Working against you (negative theta)
- **Volatility:** Benefits from IV increase (positive vega)

---

## Risk Assessment

### Maximum Risk
**Scenario:** Stock falls below $180
**Max Loss:** -$250 (100% of premium paid)
**% of Account:** 0.5% (if $50k account)

### Assignment Risk
**Early Assignment:** Low (calls have time value)
**At Expiration:** Manage positions if in-the-money

---

## Trade Management

### Entry
✅ Enter if: [Conditions]
- Stock price $178-$182
- IV below 30%
- >21 DTE

### Profit Taking
- **Target 1:** 50% profit ($125) - Close half
- **Target 2:** 75% profit ($187.50) - Close all

### Stop Loss
- **Trigger:** Stock falls below $177 (-$150 loss)
- **Action:** Close position immediately

### Adjustments
- If stock rallies to $184, consider rolling short call higher
- If stock drops to $179, add second spread at $175/$180

---

## Suitability

### When to Use This Strategy
✅ Moderately bullish on AAPL
✅ Expect upside to $185-$190
✅ Want defined risk
✅ 21-45 DTE timeframe

### When to Avoid
❌ Very bullish (buy stock or long call instead)
❌ High IV environment (wait for IV to drop)
❌ Earnings in <7 days (IV crush risk)

---

## Alternatives Comparison

| Strategy | Max Profit | Max Loss | Complexity | When Better |
|----------|-----------|----------|------------|-------------|
| Bull Call Spread | $250 | -$250 | Medium | Moderately bullish |
| Long Call | Unlimited | -$500 | Low | Very bullish |
| Covered Call | $850 | Unlimited | Medium | Own stock already |
| Bull Put Spread | $300 | -$200 | Medium | Want credit spread |

**Recommendation:** Bull call spread is good balance of risk/reward for moderate bullish thesis.

---

*Disclaimer: This is theoretical analysis using Black-Scholes pricing. Actual market prices may differ. Trade at your own risk. Options are complex instruments with significant loss potential.*

文件命名约定：

options_analysis_[TICKER]_[STRATEGY]_[DATE].md

示例：options_analysis_AAPL_BullCallSpread_2025-11-08.md

关键原则

理论定价的局限性

用户应了解的内容：

1. Black-Scholes 假设：

   • 欧式期权（不能提前行权）
   • 波动率恒定（实际中的 IV 会变化）
   • 无交易成本
   • 连续交易

2. 实际价格与理论价格：

   • 买卖价差：实际成本高于理论价格
   • 美式期权：可以提前行权（尤其是价内看跌期权）
   • 流动性：流动性差的期权市场价差较大
   • 股息：除息日会影响定价

3. 最佳实践：

   • 将其用作教育工具和比较分析工具
   • 交易前从经纪商处获取实时报价
   • 理解理论价格 ≈ 市场中间价
   • 将佣金和滑点纳入考虑

波动率指南

历史波动率与隐含波动率：

Historical Volatility (HV): What happened
- Calculated from past price movements
- Objective, based on data
- Available for free (FMP API)

Implied Volatility (IV): What market expects
- Derived from option prices
- Subjective, based on supply/demand
- Requires live options data (user provides)

Comparison:
- IV > HV: Options expensive (consider selling)
- IV < HV: Options cheap (consider buying)
- IV = HV: Fairly priced

IV 百分位：

用户提供当前 IV，我们计算百分位：

# Fetch 1-year HV data
historical_hvs = calculate_hv_series(prices_1yr, window=30)

# Calculate IV percentile
iv_percentile = percentileofscore(historical_hvs, current_iv)

if iv_percentile > 75:
    guidance = "High IV - consider selling premium (credit spreads, iron condors)"
elif iv_percentile < 25:
    guidance = "Low IV - consider buying options (long calls/puts, debit spreads)"
else:
    guidance = "Normal IV - any strategy appropriate"

与其他技能集成

财报日历：

• 自动获取财报日期
• 推荐针对财报的策略
• 计算距离财报的天数（DTE 对 IV 至关重要）
• 提醒 IV 崩跌风险

技术分析师：

• 使用支撑位／阻力位选择行权价
• 通过趋势分析制定方向性策略
• 根据突破潜力确定跨式／宽跨式策略的时机

美国股票分析：

• 通过基本面分析制定较长期策略（LEAPS）
• 使用股息收益率分析备兑看涨／看跌策略
• 通过财报质量分析财报交易策略

泡沫检测器：

• 泡沫风险高 → 重点考虑保护性看跌期权
• 风险低 → 看涨策略
• 风险极高 → 避免买入期权费（时间价值损耗会造成影响）

投资组合管理器：

• 将期权持仓与股票持仓一并跟踪
• 汇总整个投资组合的 Greeks
• 将期权用作股票持仓的对冲工具

重要说明

• 所有分析均使用英语
• 注重教育：清晰解释各类策略
• 理论定价：Black-Scholes 近似值
• 用户输入 IV：可选，默认为 HV
• 无需实时数据：FMP 免费版足够
• 依赖项：Python 3.9+、numpy、scipy、requests

常见用例

用例 1：学习策略

User: "Explain a covered call"

Workflow:
1. Read the strategy descriptions in this document and the pricing methodology
   in references/black_scholes_methodology.md
2. Explain concept, risk/reward, when to use
3. Simulate example on AAPL
4. Show P/L diagram
5. Compare to alternatives

用例 2：分析特定交易

User: "Analyze $180/$185 bull call spread on AAPL, 30 days"

Workflow:
1. Fetch AAPL price from FMP
2. Calculate HV or ask user for IV
3. Price both options (Black-Scholes)
4. Calculate Greeks
5. Simulate P/L
6. Generate analysis report

用例 3：财报策略

User: "Should I trade options before NVDA earnings?"

Workflow:
1. Fetch NVDA earnings date (Earnings Calendar)
2. Calculate days to earnings
3. Estimate IV percentile (if user provides IV)
4. Suggest straddle/strangle vs iron condor
5. Warn about IV crush
6. Simulate both strategies

用例 4：检查投资组合 Greeks

User: "What are my total portfolio Greeks?"

Workflow:
1. User provides current positions
2. Calculate Greeks for each position
3. Sum Greeks across portfolio
4. Assess overall exposure
5. Suggest adjustments if needed

故障排除

问题：无法获取 IV

• 解决方案：使用 HV 作为替代值，并告知用户
• 请用户提供经纪商平台上的 IV

问题：期权价格为负

• 解决方案：检查输入（行权价与股票价格）
• 深度价内期权可能存在数值问题

问题：Greeks 似乎不正确

• 解决方案：核实输入值（T、sigma、r）
• 检查使用的是年化值还是日值

问题：策略过于复杂

• 解决方案：拆分为各个期权腿，分别分析
• 参考文档了解策略详情

资源

参考资料：

• references/black_scholes_methodology.md - Black-Scholes 公式、Greeks 及其解读

脚本：

• scripts/black_scholes.py - 定价引擎和 Greeks

外部资源：

• Options Playbook：https://www.optionsplaybook.com/
• CBOE 教育资源：https://www.cboe.com/education/
• Black-Scholes 计算器：可用于核验的各种在线工具

---

版本：1.0 最后更新：2025-11-08 依赖项：Python 3.9+、numpy、scipy、requests API：FMP API（免费版足够）