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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

Prerequisites

Required:

• Python 3.8+ with numpy, scipy, requests

Optional:

• FMP API key (for real-time stock prices and historical volatility)
  • Set via FMP_API_KEY environment variable or --api-key argument
  • Without API key: Use manual inputs for stock price and volatility

Installation:

pip install numpy scipy requests

Quick Start Examples:

# 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

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

1. Protective Put - Own stock, buy put (insurance, limited downside)
2. Collar - Own stock, sell call + buy put (limited upside/downside)

Directional Strategies

1. Bull Call Spread - Buy lower strike call, sell higher strike call (limited risk/reward bullish)
2. Bull Put Spread - Sell higher strike put, buy lower strike put (credit spread, bullish)
3. Bear Call Spread - Sell lower strike call, buy higher strike call (credit spread, bearish)
4. Bear Put Spread - Buy higher strike put, sell lower strike put (limited risk/reward bearish)

Volatility Strategies

1. Long Straddle - Buy ATM call + ATM put (profit from big move either direction)
2. Long Strangle - Buy OTM call + OTM put (cheaper than straddle, bigger move needed)
3. Short Straddle - Sell ATM call + ATM put (profit from no movement, unlimited risk)
4. Short Strangle - Sell OTM call + OTM put (profit from no movement, wider range)

Range-Bound Strategies

1. Iron Condor - Bull put spread + bear call spread (profit from range-bound movement)
2. Iron Butterfly - Sell ATM straddle, buy OTM strangle (profit from tight range)

Advanced Strategies

1. Calendar Spread - Sell near-term option, buy longer-term option (profit from time decay)
2. Diagonal Spread - Calendar spread with different strikes (directional + time decay)
3. 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:

# 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:

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

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

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)

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

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

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%

Position Greeks:

For a strategy with multiple legs, sum Greeks across all legs:

# 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)

Greeks Interpretation:

Step 5: Simulate Strategy P/L

Objective: Calculate profit/loss at various stock prices at expiration.

Method:

Generate stock price range (e.g., ±30% from current price):

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

For each price point, calculate P/L:

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

Key Metrics:

• Max Profit : Highest possible P/L
• Max Loss : Worst possible P/L
• Breakeven Point(s) : Stock price(s) where P/L = 0
• Profit Probability : Percentage of price range that's profitable (simplified)

Example Output:

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)

Step 6: Generate P/L Diagram (ASCII Art)

Visual representation of P/L across stock prices:

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)

Example Output:

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

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

Step 7: Strategy-Specific Analysis

Provide tailored guidance based on strategy type:

Covered Call:

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

Protective Put:

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

Iron Condor:

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

Step 8: Earnings Strategy Analysis

Integration with Earnings Calendar:

When user asks about earnings strategies, fetch earnings date:

from earnings_calendar import get_next_earnings_date

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

Pre-Earnings Strategies:

Long Straddle/Strangle:

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)

Short Iron Condor:

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)

Step 9: Risk Management Guidance

Position Sizing:

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

Portfolio Greeks Management:

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

Adjustments and Exits:

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)

Output Format

Strategy Analysis Report Template:

# 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.*

File Naming Convention:

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

Example: options_analysis_AAPL_BullCallSpread_2025-11-08.md

Key Principles

Theoretical Pricing Limitations

What Users Should Know:

1. Black-Scholes Assumptions:

   • European-style options (can't exercise early)
   • Constant volatility (IV changes in reality)
   • No transaction costs
   • Continuous trading

2. Real vs Theoretical:

   • Bid-ask spread: Actual cost higher than theoretical
   • American options: Can be exercised early (especially ITM puts)
   • Liquidity: Wide markets on illiquid options
   • Dividends: Ex-dividend dates affect pricing

3. Best Practices:

   • Use as educational tool and comparative analysis
   • Get real quotes from broker before trading
   • Understand theoretical price ≈ mid-market price
   • Account for commissions and slippage

Volatility Guidance

Historical vs Implied Volatility:

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 Percentile:

User provides current IV, we calculate percentile:

# 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"

Integration with Other Skills

Earnings Calendar:

• Fetch earnings dates automatically
• Suggest earnings-specific strategies
• Calculate days to earnings (DTE critical for IV)
• Warn about IV crush risk

Technical Analyst:

• Use support/resistance for strike selection
• Trend analysis for directional strategies
• Breakout potential for straddle/strangle timing

US Stock Analysis:

• Fundamental analysis for longer-term strategies (LEAPS)
• Dividend yield for covered call/put analysis
• Earnings quality for earnings plays

Bubble Detector:

• High bubble risk → focus on protective puts
• Low risk → bullish strategies
• Critical risk → avoid long premium (theta hurts)

Portfolio Manager:

• Track options positions alongside stock positions
• Aggregate Greeks across portfolio
• Options as hedging tool for stock positions

Important Notes

• All analysis in English
• Educational focus : Strategies explained clearly
• Theoretical pricing : Black-Scholes approximation
• User IV input : Optional, defaults to HV
• No real-time data required : FMP Free tier sufficient
• Dependencies : Python 3.8+, numpy, scipy, pandas

Common Use Cases

Use Case 1: Learn Strategy

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

Use Case 2: Analyze Specific Trade

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

Use Case 3: Earnings Strategy

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

Use Case 4: Portfolio Greeks Check

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

Troubleshooting

Problem: IV not available

• Solution: Use HV as proxy, note to user
• Ask user to provide IV from broker platform

Problem: Negative option price

• Solution: Check inputs (strike vs stock price)
• Deep ITM options may have numerical issues

Problem: Greeks seem wrong

• Solution: Verify inputs (T, sigma, r)
• Check if using annual vs daily values

Problem: Strategy too complex

• Solution: Break into legs, analyze separately
• Refer to references for strategy details

Resources

References:

• references/black_scholes_methodology.md - Black-Scholes formulas, Greeks, and interpretation
• references/strategies_guide.md - All 17+ strategies explained (future)
• references/greeks_explained.md - Greeks deep dive (future)
• references/volatility_guide.md - HV vs IV, when to trade (future)

Scripts:

• scripts/black_scholes.py - Pricing engine and Greeks
• scripts/strategy_analyzer.py - Strategy simulation
• scripts/earnings_strategy.py - Earnings-specific analysis

External Resources:

• Options Playbook: https://www.optionsplaybook.com/
• CBOE Education: https://www.cboe.com/education/
• Black-Scholes Calculator: Various online tools for verification

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Version : 1.0 Last Updated : 2025-11-08 Dependencies : Python 3.8+, numpy, scipy, pandas, requests API : FMP API (Free tier sufficient)

Weekly Installs

265

Repository

tradermonty/cla…g-skills

GitHub Stars

394

First Seen

Jan 26, 2026

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