Options Strategy Advisor

Options trading strategy analysis and simulation tool. Provides theoretical pricing using Black-Scholes model, Greeks calculation, strategy P/L simulation, and risk management guidance. Use when user requests options strategy analysis, covered calls, protective puts, spreads, iron condors, earnings plays, or options risk management. Includes volatility analysis, position sizing, and earnings-based strategy recommendations. Educational focus with practical trade simulation.

# Options Strategy Advisor

## Overview

This skill provides comprehensive options strategy analysis and education using theoretical pricing models. It helps traders understand, analyze, and simulate options strategies without requiring real-time market data subscriptions.

**Core Capabilities:**
- **Black-Scholes Pricing**: Theoretical option prices and Greeks calculation
- **Strategy Simulation**: P/L analysis for major options strategies
- **Earnings Strategies**: Pre-earnings volatility plays integrated with Earnings Calendar
- **Risk Management**: Position sizing, Greeks exposure, max loss/profit analysis
- **Educational Focus**: Detailed explanations of strategies and risk metrics

**Data Sources:**
- FMP API: Stock prices, historical volatility, dividends, earnings dates
- User Input: Implied volatility (IV), risk-free rate
- Theoretical Models: Black-Scholes for pricing and Greeks

## When to Use This Skill

Use this skill when:
- User asks about options strategies ("What's a covered call?", "How does an iron condor work?")
- User wants to simulate strategy P/L ("What's my max profit on a bull call spread?")
- User needs Greeks analysis ("What's my delta exposure?")
- User asks about earnings strategies ("Should I buy a straddle before earnings?")
- User wants to compare strategies ("Covered call vs protective put?")
- User needs position sizing guidance ("How many contracts should I trade?")
- User asks about volatility ("Is IV high right now?")

Example requests:
- "Analyze a covered call on AAPL"
- "What's the P/L on a $100/$105 bull call spread on MSFT?"
- "Should I trade a straddle before NVDA earnings?"
- "Calculate Greeks for my iron condor position"
- "Compare protective put vs covered call for downside protection"

## Supported Strategies

### Income Strategies
1. **Covered Call** - Own stock, sell call (generate income, cap upside)
2. **Cash-Secured Put** - Sell put with cash backing (collect premium, willing to buy stock)
3. **Poor Man's Covered Call** - LEAPS call + short near-term call (capital efficient)

### Protection Strategies
4. **Protective Put** - Own stock, buy put (insurance, limited downside)
5. **Collar** - Own stock, sell call + buy put (limited upside/downside)

### Directional Strategies
6. **Bull Call Spread** - Buy lower strike call, sell higher strike call (limited risk/reward bullish)
7. **Bull Put Spread** - Sell higher strike put, buy lower strike put (credit spread, bullish)
8. **Bear Call Spread** - Sell lower strike call, buy higher strike call (credit spread, bearish)
9. **Bear Put Spread** - Buy higher strike put, sell lower strike put (limited risk/reward bearish)

### Volatility Strategies
10. **Long Straddle** - Buy ATM call + ATM put (profit from big move either direction)
11. **Long Strangle** - Buy OTM call + OTM put (cheaper than straddle, bigger move needed)
12. **Short Straddle** - Sell ATM call + ATM put (profit from no movement, unlimited risk)
13. **Short Strangle** - Sell OTM call + OTM put (profit from no movement, wider range)

### Range-Bound Strategies
14. **Iron Condor** - Bull put spread + bear call spread (profit from range-bound movement)
15. **Iron Butterfly** - Sell ATM straddle, buy OTM strangle (profit from tight range)

### Advanced Strategies
16. **Calendar Spread** - Sell near-term option, buy longer-term option (profit from time decay)
17. **Diagonal Spread** - Calendar spread with different strikes (directional + time decay)
18. **Ratio Spread** - Unbalanced spread (more contracts on one leg)

## Analysis Workflow

### Step 1: Gather Input Data

**Required from User:**
- Ticker symbol
- Strategy type
- Strike prices
- Expiration date(s)
- Position size (number of contracts)

**Optional from User:**
- Implied Volatility (IV) - if not provided, use Historical Volatility (HV)
- Risk-free rate - default to current 3-month T-bill rate (~5.3% as of 2025)

**Fetched from FMP API:**
- Current stock price
- Historical prices (for HV calculation)
- Dividend yield
- Upcoming earnings date (for earnings strategies)

**Example User Input:**
```
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)
```

### Step 2: Calculate Historical Volatility (if IV not provided)

**Objective:** Estimate volatility from historical price movements.

**Method:**
```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
```

**Output:**
- Historical Volatility (annualized percentage)
- Note to user: "HV = 24.5%, consider using current market IV for more accuracy"

**User Can Override:**
- Provide IV from broker platform (ThinkorSwim, TastyTrade, etc.)
- Script accepts `--iv 28.0` parameter

### Step 3: Price Options Using Black-Scholes

**Black-Scholes Model:**

For European-style options:
```
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
```

**Adjustments:**
- Subtract present value of dividends from S for calls
- American options: Use approximation or note "European pricing, may undervalue American options"

**Python Implementation:**
```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
```

**Output for Each Option Leg:**
- Theoretical price
- Note: "Market price may differ due to bid-ask spread and American vs European pricing"

### Step 4: Calculate Greeks

**The Greeks** measure option price sensitivity to various factors:

**Delta (Δ):** Change in option price per $1 change in stock price
```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):** Change in delta per $1 change in stock price
```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):** Change in option price per day (time decay)
```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):** Change in option price per 1% change in volatility
```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):** Change in option price per 1% change in interest rate
```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