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 3.9+ with
numpy,scipy,requests
Optional:
- FMP API key (for real-time stock prices and historical volatility)
- Set via
FMP_API_KEYenvironment variable or--api-keyargument - Without API key: Use manual inputs for stock price and volatility
- Set via
Installation:
bashpip install numpy scipy requests
Quick Start Examples:
bash# 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
- Covered Call - Own stock, sell call (generate income, cap upside)
- Cash-Secured Put - Sell put with cash backing (collect premium, willing to buy stock)
- Poor Man's Covered Call - LEAPS call + short near-term call (capital efficient)
Protection Strategies
- Protective Put - Own stock, buy put (insurance, limited downside)
- Collar - Own stock, sell call + buy put (limited upside/downside)
Directional Strategies
- Bull Call Spread - Buy lower strike call, sell higher strike call (limited risk/reward bullish)
- Bull Put Spread - Sell higher strike put, buy lower strike put (credit spread, bullish)
- Bear Call Spread - Sell lower strike call, buy higher strike call (credit spread, bearish)
- Bear Put Spread - Buy higher strike put, sell lower strike put (limited risk/reward bearish)
Volatility Strategies
- Long Straddle - Buy ATM call + ATM put (profit from big move either direction)
- Long Strangle - Buy OTM call + OTM put (cheaper than straddle, bigger move needed)
- Short Straddle - Sell ATM call + ATM put (profit from no movement, unlimited risk)
- Short Strangle - Sell OTM call + OTM put (profit from no movement, wider range)
Range-Bound Strategies
- Iron Condor - Bull put spread + bear call spread (profit from range-bound movement)
- Iron Butterfly - Sell ATM straddle, buy OTM strangle (profit from tight range)
Advanced Strategies
- Calendar Spread - Sell near-term option, buy longer-term option (profit from time decay)
- Diagonal Spread - Calendar spread with different strikes (directional + time decay)
- 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 28.0parameter
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:
pythonfrom 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
pythondef 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
pythondef 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)
pythondef 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
pythondef 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
pythondef 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:
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)
Greeks Interpretation:
| Greek | Meaning | Example |
|---|---|---|
| Delta | Directional exposure | Δ = 0.50 → $50 profit if stock +$1 |
| Gamma | Delta acceleration | Γ = 0.05 → Delta increases by 0.05 if stock +$1 |
| Theta | Daily time decay | Θ = -$5 → Lose $5/day from time passing |
| Vega | Volatility sensitivity | ν = $10 → Gain $10 if IV increases 1% |
| Rho | Interest rate sensitivity | ρ = $2 → Gain $2 if rates increase 1% |
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):
pythoncurrent_price = 180 price_range = np.linspace(current_price * 0.7, current_price * 1.3, 100)
For each price point, calculate P/L:
pythondef 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 $182.50)
Step 6: Generate P/L Diagram (ASCII Art)
Visual representation of P/L across stock prices:
pythondef generate_pnl_diagram(price_range, pnl_values, current_price, width=60, height=15): """Generate ASCII P/L diagram""" # Normalize to chart dimensions max_pnl = max(pnl_values) min_pnl = min(pnl_values) lines = [] lines.append(f"\nP/L Diagram: {strategy_name}") lines.append("-" * width) # Y-axis levels levels = np.linspace(max_pnl, min_pnl, height) for level in levels: if abs(level) < (max_pnl - min_pnl) * 0.05: label = f" 0 |" # Zero line else: label = f"{level:6.0f} |" row = label for i in range(width - len(label)): idx = int(i / (width - len(label)) * len(price_range)) pnl = pnl_values[idx] price = price_range[idx] # Determine character if abs(pnl - level) < (max_pnl - min_pnl) / height: if pnl > 0: char = '█' # Profit elif pnl < 0: char = '░' # Loss else: char = '─' # Breakeven elif abs(level) < (max_pnl - min_pnl) * 0.05: char = '─' # Zero line elif abs(price - current_price) < (price_range[-1] - price_range[0]) * 0.02: char = '│' # Current price line else: char = ' ' row += char lines.append(row) lines.append(" " * 6 + "|" + "-" * (width - 6)) lines.append(" " * 6 + f"${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:
pythonfrom 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:
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.*
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:
-
Black-Scholes Assumptions:
- European-style options (can't exercise early)
- Constant volatility (IV changes in reality)
- No transaction costs
- Continuous trading
-
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
-
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:
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"
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 3.9+, numpy, scipy, requests
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 interpretationreferences/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 Greeksscripts/strategy_analyzer.py- Strategy simulationscripts/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
Version: 1.0 Last Updated: 2025-11-08 Dependencies: Python 3.9+, numpy, scipy, requests API: FMP API (Free tier sufficient)
