Options Strategy Advisor

# Options Strategy Advisor

## 概述

此技能利用理论定价模型提供全面的期权策略分析和教育。它帮助交易者在无需订阅实时市场数据的情况下理解、分析和模拟期权策略。

**核心能力：** - **Black-Scholes 定价**：理论期权价格和希腊字母计算 - **策略模拟**：主要期权策略的盈亏分析 - **财报策略**：与财报日历集成的财报前波动率策略 - **风险管理**：仓位规模、希腊字母敞口、最大亏损/利润分析 - **教育重点**：策略和风险指标的详细解释

**数据来源：** - FMP API：股价、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：用于定价和希腊字母的 Black-Scholes 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（“什么是备兑看涨期权？”，“铁鹰式策略是如何运作的？”） - 用户想要模拟策略盈亏（“我的看涨期权牛市价差的最大利润是多少？”） - 用户需要希腊字母分析（“我的 Delta 敞口是多少？”） - 用户询问财报策略（“我应该在财报前买入跨式期权吗？”） - 用户想要比较策略（“备兑看涨期权 vs 保护性看跌期权？”） - 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（“目前的 IV 高吗？”）

示例请求： - “分析 AAPL 的备兑看涨期权” - “MSFT 行权价 $100/$105 的看涨期权牛市价差的盈亏是多少？” - “我应该在 NVDA 财报前交易跨式期权吗？” - “计算我的铁鹰式头寸的希腊字母” - “比较下行保护的保护性看跌期权和备兑看涨期权”

## 支持的策略

### 收益型策略 1. **备兑看涨期权** - 持有股票，卖出看涨期权（产生收益，限制上行） 2. **现金担保看跌期权** - 卖出有现金担保的看跌期权（收取权利金，愿意买入股票） 3. **穷人的备兑看涨期权** - 买入 LEAPS 看涨期权 + 卖出近月看涨期权（资本效率高）

### 保护型策略 4. **保护性看跌期权** - 持有股票，买入看跌期权（保险，限制下行） 5. **领口策略** - 持有股票，卖出看涨期权 + 买入看跌期权（限制上行/下行）

### 方向性策略 6. **看涨价差** - 买入低行权价看涨期权，卖出高行权价看涨期权（风险/收益有限的看涨） 7. **看跌价差** - 卖出高行权价看跌期权，买入低行权价看跌期权（权利金价差，看涨） 8. **熊看涨价差** - 卖出低行权价看涨期权，买入高行权价看涨期权（权利金价差，看跌） 9. **熊看跌价差** - 买入高行权价看跌期权，卖出低行权价看跌期权（风险/收益有限的看跌）

### 波动率策略 10. **买入跨式** - 买入平值看涨期权 + 平值看跌期权（从任一方向的剧烈波动中获利） 11. **买入宽跨式** - 买入虚值看涨期权 + 虚值看跌期权（比跨式便宜，需要更大的波动） 12. **卖出跨式** - 卖出平值看涨期权 + 平值看跌期权（从无波动中获利，无限风险） 13. **卖出宽跨式** - 卖出虚值看涨期权 + 虚值看跌期权（从无波动中获利，范围更宽）

### 震荡市策略 14. **铁鹰式** - 看跌价差 + 熊看涨价差（从震荡中获利） 15. **铁蝶式** - 卖出平值跨式，买入虚值宽跨式（从窄幅震荡中获利）

### 高级策略 16. **日历价差** - 卖出近期期权，买入远期期权（从时间衰减中获利） 17. **对角价差** - 不同行权价的日历价差（方向性 + 时间衰减） 18. **比率价差** - 不平衡价差（一条腿上有更多合约）

## 分析工作流

### 第一步：收集输入数据

**用户需提供：** - 股票代码 - 策略类型 - 行权价 - 到期日期 - 仓位规模（合约数量）

**用户可选：** - 隐含波动率 (IV) - 如未提供，使用历史波动率 (HV) - 无风险利率 - 默认为当前 3 个月期国库券利率（截至 2025 年约 5.3%）

**从 FMP API 获取：** - 当前股价 - 历史价格（用于 HV 计算） - 股息率 - 即将到来的财报日期（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) ```

### 第二步：计算历史波动率（如果未提供 IV）

**目标：** 根据历史价格变动估算波动率。

**方法：** ```python # 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 以获得更准确的结果”

**用户可以覆盖：** - 从券商平台（ThinkorSwim, TastyTrade 等）提供 IV - 脚本接受 `--iv 28.0` 参数

### 第三步：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：** - 对于看涨期权，从 S 中减去股息的现值 - 美式期权：使用近似方法或注明“欧式定价，可能会低估美式期权”

**Python 实现：** ```python 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：“市场价格可能因买卖价差和美式与欧式定价的差异而有所不同”

### 第四步：计算希腊字母

**希腊字母**衡量期权价格对各种因素的敏感度：

**Delta (Δ)：** 股价每变动 $1 时期权价格的变化 ```python 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 时 Delta 的变化 ```python 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)：** 每天期权价格的变化（时间衰减） ```python 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% 时期权价格的变化 ```python 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% 时期权价格的变化 ```python 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% ```

**仓位希腊字母：**

对于包含多条腿的策略，将所有腿的希腊字母求和： ```python # 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 | |-------|---------|---------| | **Delta** | 方向性敞口 | Δ = 0.50 → 股票上涨 $1 获利 $50 | | **Gamma** | Delta 加速度 | Γ = 0.05 → 股票上涨 $1 Delta 增加 0.05 | | **Theta** | 每日时间衰减 | Θ = -$5 → 随时间流逝每天亏损 $5 | | **Vega** | 波动率敏感度 | ν = $10 → IV 上涨 1% 获利 $10 | | **Rho** | 利率敏感度 | ρ = $2 → 利率上涨 1% 获利 $2 |

### 第五步：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，当前价格的 ±30%）： ```python 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： ```python 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**：可能的最大盈亏 - **最大亏损**：可能的最差盈亏 - **盈亏平衡点**：盈亏 = 0 的股价 - **盈利概率**：价格范围内盈利的百分比（简化计算）

**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) ```

### 第六步：生成盈亏图（ASCII 艺术字）

**跨股价的盈亏可视化表示：**

```python 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 ```

### 第七步：特定策略分析

根据策略类型提供定制指导：

**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：** ``` 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 ```

### 第八步：财报策略分析

**与财报日历集成：**

当用户询问财报策略时，获取财报日期： ```python 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：**

**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：** ``` 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) ```

### 第九步：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 ```

**投资组合希腊字母管理：**

``` 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 ```

**调整和退出：**

``` 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：**

```markdown # 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

**用户须知：** 1. **Black-Scholes 假设：** - 欧式期权（不能提前行权） - 恒定波动率（实际上 IV 会变化） - 无交易成本 - 连续交易

2. **实际与理论：** - 买卖价差：实际成本高于理论成本 - 美式期权：可以提前行权（特别是实值看跌期权） - 流动性：非流动性期权的买卖价差较宽 - 股息：除息日影响定价

3. **最佳实践：** - 用作教育工具和比较分析 - 交易前从券商获取实时报价 - 理解理论价格 ≈ 中间市场价格 - 考虑佣金和滑点

### 波动率指导

**历史与隐含波动率：**

``` 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： ```python # 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" ```

## 与其他技能的集成

**财报日历：** - 自动获取财报日期 - 建议针对财报的策略 - 计算距离财报的天数（DTE 对 IV 至关重要） - 警告 IV 崩盘风险

**技术分析师：** - 使用支撑/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/宽跨式时机的突破潜力

**美股分析：** - 用于较长期策略（LEAPS）的基本面分析 - 用于备兑看涨/看跌期权分析的股息率 - 用于财报博弈的财报质量

**泡沫检测器：** - 高泡沫风险 → 重点关注保护性看跌期权 - 低风险 → 看涨策略 - 极高风险 → 避免长权利金（Theta 伤害）

**投资组合经理：** - 与股票仓位一起跟踪期权仓位 - 汇总整个投资组合的希腊字母 - 期权作为股票仓位的对冲工具

## 重要说明

- **所有分析均使用英语** - **教育重点**：清晰解释策略 - **理论定价**：Black-Scholes 近似 - **用户 IV 输入**：可选，默认为 HV - **无需实时数据**：FMP 免费版即可 - **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：投资组合希腊字母检查** ``` 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 作为代理，并提示用户 - 要求用户从券商平台提供 IV

**问题：期权价格为负** - 解决方案：检查输入（行权价与股价） - 深度实值期权可能存在数值问题

**问题：希腊值看起来不正确** - 解决方案：验证输入（T, sigma, r） - 检查是否使用了年化与日化数值

**问题：策略过于复杂** - 解决方案：拆分为单腿，单独分析 - 参考资料了解策略详情

## 资源

**参考资料：** - `references/strategies_guide.md` - 所有 17+ 种策略的说明 - `references/greeks_explained.md` - 希腊值深度解析 - `references/volatility_guide.md` - HV vs IV，何时交易

**脚本：** - `scripts/black_scholes.py` - 定价引擎与希腊值 - `scripts/strategy_analyzer.py` - 策略模拟 - `scripts/earnings_strategy.py` - 财报季专项分析

**外部资源：** - Options Playbook: https://www.optionsplaybook.com/ - CBOE Education: https://www.cboe.com/education/ - Black-Scholes Calculator: 用于验证的各种在线工具

---

**版本**：1.0 **最后更新**：2025-11-08 **依赖项**：Python 3.8+, numpy, scipy, pandas, requests **API**：FMP API（免费版即可）