options-strategy-advisor

name options-strategy-advisor description 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 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_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 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: # 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: 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): 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