期权策略顾问

概述

本技能利用理论定价模型，提供全面的期权策略分析与教育。它帮助交易者理解、分析和模拟期权策略，无需订阅实时市场数据。

核心功能：

• 布莱克-斯科尔斯定价：理论期权价格与希腊值计算
• 策略模拟：主要期权策略的盈亏分析
• 财报季策略：结合财报日历的财报前波动率策略
• 风险管理：头寸规模、希腊值敞口、最大亏损/盈利分析
• 教育重点：策略与风险指标的详细解释

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、无风险利率
• 理论模型：用于定价和希腊值计算的布莱克-斯科尔斯模型

何时使用此技能

在以下情况使用此技能：

• 用户询问期权策略（"什么是备兑认购期权？"、"铁鹰策略如何运作？"）
• 用户想要模拟策略损益（"我的牛市认购价差最大利润是多少？"）
• 用户需要希腊值分析（"我的德尔塔风险敞口是多少？"）
• 用户询问财报季策略（"我应该在财报前买入跨式组合吗？"）
• 用户想要比较策略（"备兑认购期权 vs 保护性认沽期权？"）
• 用户需要仓位规模指导（"我应该交易多少张合约？"）
• 用户询问波动率（"当前隐含波动率高吗？"）

示例请求：

• "分析苹果股票的备兑认购期权"
• "微软100/105美元牛市认购价差的损益是多少？"
• "我应该在英伟达财报前交易跨式组合吗？"
• "计算我的铁鹰策略头寸的希腊值"
• "比较保护性认沽期权与备兑认购期权在下跌保护方面的优劣"

支持的策略

收益策略

1. 备兑认购期权- 持有股票，卖出看涨期权（产生收入，限制上涨空间）
2. 现金担保看跌期权- 以现金支持卖出看跌期权（收取权利金，愿意购买股票）
3. 穷人式备兑看涨期权- 长期看涨期权 + 卖出短期看涨期权（资本效率高）

保护策略

4. 保护性看跌期权- 持有股票，买入看跌期权（保险，限制下跌风险）
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/下跌空间）

方向性策略

6. 牛市看涨价差- 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/收益有限，看涨）
7. 牛市看跌价差- 卖出较高行权价的看跌期权，买入较低行权价的看跌期权（贷方价差，看涨）
8. 熊市看涨价差- 卖出较低行权价的看涨期权，买入较高行权价的看涨期权（贷方价差，看跌）
9. 熊市看跌价差- 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/收益有限，看跌）

波动率策略

10. 多头跨式期权- 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. 多头宽跨式期权- 买入虚值看涨期权 + 虚值看跌期权（比跨式期权成本更低，但需要更大的价格波动才能获利）
12. 空头跨式期权- 卖出平值看涨期权 + 平值看跌期权（从价格无波动中获利，风险无限）
13. 空头宽跨式期权- 卖出虚值看涨期权 + 虚值看跌期权（从价格无波动中获利，价格波动容忍范围更宽）

区间震荡策略

14. 铁鹰式期权组合- 牛市看跌期权价差 + 熊市看涨期权价差（从区间震荡行情中获利）
15. 铁蝶式期权组合- 卖出平值跨式期权，买入虚值宽跨式期权（从价格在窄幅区间内波动中获利）

高级策略

16. 日历价差- 卖出近期期权，买入远期期权（从时间价值衰减中获利）
17. 对角价差- 行权价不同的日历价差（兼具方向性押注与时间价值衰减获利）
18. 比率价差- 不平衡价差（一腿的合约数量更多）

分析工作流程

步骤 1：收集输入数据

用户需提供：

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

用户可选提供：

• 隐含波动率 - 如未提供，则使用历史波动率
• 无风险利率 - 默认使用当前3个月期国债利率（截至2025年约为5.3%）

从FMP API获取：

• 当前股价
• 历史价格（用于计算历史波动率）
• 股息收益率
• 即将到来的财报发布日期（针对财报策略）

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)

步骤 2：计算历史波动率（如未提供隐含波动率）

目标：根据历史价格变动估算波动率。

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%，建议使用当前市场隐含波动率以获得更高准确性"

用户可以覆盖：

• 从经纪商平台（如ThinkorSwim、TastyTrade等）提供隐含波动率
• 脚本接受--iv 28.0参数

步骤3：使用布莱克-斯科尔斯模型为期权定价

布莱克-斯科尔斯模型：

适用于欧式期权：

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：

• 对于看涨期权，从S中减去股息的现值
• 美式期权：使用近似值或注明"欧式定价，可能低估美式期权的价值"

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

每个期权腿的输出：

• 理论价格
• 注意："由于买卖价差以及美式与欧式定价的差异，市场价格可能有所不同"

步骤4：计算希腊值

希腊字母衡量期权价格对各种因素的敏感度：

德尔塔 (Δ)：股价每变动1美元所引起的期权价格变动

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)

伽马 (Γ)：股价每变动1美元所引起的德尔塔变动

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))

西塔 (Θ)：期权价格每日的变动（时间衰减）

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

维加 (ν)：波动率每变动1%所引起的期权价格变动

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%

柔 (ρ)：利率每变动1%所引起的期权价格变动

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%

头寸希腊字母：

对于包含多个组成部分的策略，将所有组成部分的希腊字母值相加：

# 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)

希腊字母解读：

希腊字母	含义	示例
德尔塔	方向性敞口	Δ = 0.50 → 股价上涨1美元，获利50美元
伽马	德尔塔加速度	Γ = 0.05 → 如果股价上涨1美元，德尔塔值增加0.05
西塔	每日时间衰减	Θ = -$5 → 随时间流逝每天损失5美元
维加	波动率敏感性	ν = $10 → 如果隐含波动率上升1%，则获利10美元
柔	利率敏感性	ρ = $2 → 如果利率上升1%，则获利2美元

步骤5：模拟策略盈亏

目标：计算到期时不同股价下的利润/损失。

方法：

生成股价范围（例如，从当前价格±30%）：

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

对于每个价格点，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：

• 最大利润：可能达到的最高盈亏值
• 最大损失：可能发生的最差盈亏值
• 盈亏平衡点盈亏平衡的股价
• 盈利概率：价格区间中盈利部分所占的百分比（简化版）

示例输出：

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)

步骤六：生成盈亏图（ASCII艺术）

股价变动对应的盈亏可视化呈现：

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)

示例输出：

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

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

步骤七：策略专项分析

根据策略类型提供定制化指导：

备兑认购：

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

保护性认沽：

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

铁鹰策略：

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

步骤八：财报策略分析

与财报日历整合：

当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

财报前策略：

多头跨式/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)

空头铁鹰策略：

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)

步骤九：风险管理指导

头寸规模：

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.*

文件命名规范：

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

示例：期权分析_AAPL_牛市看涨价差_2025-11-08.md

核心原则

理论定价局限性

用户须知：

1. 布莱克-斯科尔斯模型假设：

   • 欧式期权（无法提前行权）
   • 波动率恒定（现实中隐含波动率会变化）
   • 无交易成本
   • 连续交易

2. 现实与理论的差异：

   • 买卖价差：实际成本高于理论值
   • 美式期权：可提前行权（尤其是价内看跌期权）
   • 流动性：非流动性期权市场价差较大
   • 股息：除息日影响定价

3. 最佳实践：

   • 作为教育工具和对比分析使用
   • 交易前从经纪商获取实际报价
   • 理解理论价格≈中间市场价格
   • 考虑佣金和滑点

波动率指引

历史波动率 vs 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

隐含波动率百分位：

用户提供当前隐含波动率，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"

与其他技能的整合

财报日历：

• 自动获取财报日期
• 建议针对财报的特定策略
• 计算距离财报的天数（到期天数对隐含波动率至关重要）
• 警告隐含波动率骤降风险

技术分析师：

• 利用支撑/阻力位进行行权价选择
• 用于方向性策略的趋势分析
• 用于跨式/宽跨式策略时机选择的突破潜力分析

美股分析：

• 用于长期策略（长期普通股预期证券）的基本面分析
• 用于备兑看涨/看跌期权分析的股息收益率
• 盈利质量对收益表现的影响

泡沫检测器：

• 高风险泡沫 → 关注保护性看跌期权
• 低风险 → 看涨策略
• 关键风险 → 避免多头溢价（时间价值损耗）

投资组合经理：

• 跟踪期权头寸与股票头寸
• 汇总投资组合的希腊值
• 期权作为股票头寸的对冲工具

重要说明

• 所有分析以英文进行
• 教育重点：策略解释清晰
• 理论定价：布莱克-斯科尔斯近似法
• 用户隐含波动率输入：可选，默认历史波动率
• 无需实时数据：FMP免费层级足够
• 依赖项：Python 3.8+、numpy、scipy、pandas

常见用例

用例 1：学习策略

User: "Explain a covered call"

Workflow:
1. Load strategy reference (references/strategies_guide.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：分析具体交易

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：财报季策略

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：检查投资组合希腊值

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：隐含波动率不可用

• 解决方案：使用历史波动率作为代理，并通知用户
• 请用户从经纪商平台提供隐含波动率

问题：期权价格为负

• 解决方案：检查输入（行权价与股价）
• 深度价内期权可能存在数值问题

问题：希腊值似乎有误

• 解决方案：验证输入（T、sigma、r）
• 检查是否使用了年化值与日度值

问题：策略过于复杂

• 解决方案：拆分为单腿，单独分析
• 参考相关资料了解策略详情

资源

参考资料：

• 参考资料/策略指南.md- 详细解释了所有 17+ 种策略
• 参考资料/希腊字母详解.md- 希腊字母深入解析
• 参考资料/波动率指南.md- 历史波动率 vs 隐含波动率，交易时机判断

脚本：

• 脚本/布莱克-斯科尔斯.py- 定价引擎与希腊字母计算
• 脚本/策略分析器.py- 策略模拟
• 脚本/财报策略.py- 财报专用分析

外部资源：

• 期权策略手册：https://www.optionsplaybook.com/
• CBOE 教育中心：https://www.cboe.com/education/
• 布莱克-斯科尔斯计算器：用于验证的各种在线工具

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版本: 1.0最后更新: 2025-11-08依赖项: Python 3.8+, numpy, scipy, pandas, requestsAPI: FMP API（免费套餐已足够）

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