Agent skill

Options Strategy Advisor

by tradermonty in tradermonty/claude-trading-skills

Options trading strategy analysis and simulation tool. An agent skill from tradermonty/claude-trading-skills.

MITAuto-check passedBusiness, Finance & HR

Install Options Strategy Advisor

skills CLI
$ npx skills add tradermonty/claude-trading-skills --skill options-strategy-advisor -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install tradermonty/claude-trading-skills options-strategy-advisor --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/tradermonty/claude-trading-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/options-strategy-advisor .claude/skills/options-strategy-advisor && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
options-strategy-advisor
GitHub stars
3k
Used in
3 other repos
Token cost
~7.3k tokens
SKILL.md length
1,393 words
Files
8 (incl. scripts, references)
Skills in repo
74
Repo updated
First seen
Licence
MIT

At a glance

Options trading strategy analysis and simulation tool. An agent skill from tradermonty/claude-trading-skills.

  • Works in 9 steps: Gather Input Data → Calculate Historical Volatility (if IV… → Price Options Using Black-Scholes → …
  • User requests options strategy analysis
  • SKILL.md covers Overview, Prerequisites, When to Use This Skill and Supported Strategies, plus 8 more sections
  • Runs Python scripts from its folder; calls python3 and pip; needs FMP_API_KEY

What it does

Options Strategy Advisor is an agent skill from tradermonty/claude-trading-skills. 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.

Its SKILL.md is about 7.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 10 other files, including scripts and reference files (for example `README.md`, `references/black_scholes_methodology.md` and `scripts/black_scholes.py`).

It sits in Business, Finance & HR, covering Trading and backtesting. The repository describes itself as: Claude Code skills for equity investors and traders — market analysis, technical charting, economic calendars, screeners, and trading strategy development. The licence is MIT.

When your agent uses it

  • User requests options strategy analysis
  • Protective puts
  • Options risk management

Example prompts

  • “Use the options-strategy-advisor skill to option trading strategy analysis and simulation tool. An agent skill from tradermonty/claude-trading-skills”
  • “/options-strategy-advisor”

Requirements

  • Python 3
  • A credential in FMP_API_KEY

Workflow steps

9 steps, taken from the step headings in SKILL.md.

  1. Gather Input Data
  2. Calculate Historical Volatility (if IV not provided)
  3. Price Options Using Black-Scholes
  4. Calculate Greeks
  5. Simulate Strategy P/L
  6. Generate P/L Diagram (ASCII Art)
  7. Strategy-Specific Analysis
  8. Earnings Strategy Analysis
  9. Risk Management Guidance

What it can do on your machine

Read from SKILL.md and the folder at commit eab8d5c. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 4 files in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python3
    • pip

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • optionsplaybook.com
    • cboe.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • FMP_API_KEY

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Options Strategy Advisor loads about 7.3k tokens when it runs, and up to ~8.4k if it reads all its reference files. Until then it costs about 126 tokens; SKILL.md has 1,393 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~126
When it runs · the whole SKILL.md, loaded when a task matches
~7.3k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~8.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from tradermonty/claude-trading-skills at commit eab8d5c, republished under its MIT licence (© tradermonty). 1,393 words, ~7,252 tokens.

Download SKILL.mdSave it as .claude/skills/options-strategy-advisor/SKILL.md (or your agent's skills folder). This skill also uses 7 other files; get the full folder from GitHub.
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:

bash
pip 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
  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:

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 * 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:

GreekMeaningExample
DeltaDirectional exposureΔ = 0.50 → $50 profit if stock +$1
GammaDelta accelerationΓ = 0.05 → Delta increases by 0.05 if stock +$1
ThetaDaily time decayΘ = -$5 → Lose $5/day from time passing
VegaVolatility sensitivityν = $10 → Gain $10 if IV increases 1%
RhoInterest rate sensitivityρ = $2 → Gain $2 if rates increase 1%
Show full SKILL.md (549 more words)Show less
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):

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

For each price point, calculate P/L:

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

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:

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)

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:

python
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:

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:

  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:

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 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:


Version: 1.0 Last Updated: 2025-11-08 Dependencies: Python 3.9+, numpy, scipy, requests API: FMP API (Free tier sufficient)

© tradermonty, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 7 other files (scripts, references) in skills/options-strategy-advisor of tradermonty/claude-trading-skills.

  • SKILL.md
  • README.md
  • references/black_scholes_methodology.md
  • requirements.txt
  • scripts/black_scholes.py
  • scripts/tests/conftest.py
  • scripts/tests/test_black_scholes.py
  • scripts/tests/test_fmp_endpoints.py

Open the folder on GitHubat commit eab8d5c

Used in 3 other repositories

We found 3 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 3 other GitHub owners. This page covers the copy in tradermonty/claude-trading-skills, which our catalogue first saw on October 7, 2026.

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Questions about Options Strategy Advisor

What does Options Strategy Advisor do?

Options trading strategy analysis and simulation tool. An agent skill from tradermonty/claude-trading-skills. Options Strategy Advisor is an agent skill from tradermonty/claude-trading-skills. Options trading strategy analysis and simulation tool.

When should I use Options Strategy Advisor?

Options Strategy Advisor fits situations like: user requests options strategy analysis; protective puts; options risk management.

How do I install Options Strategy Advisor in Claude Code?

Run `npx skills add tradermonty/claude-trading-skills --skill options-strategy-advisor -a claude-code`. Or copy the skill folder (skills/options-strategy-advisor in tradermonty/claude-trading-skills) into .claude/skills/options-strategy-advisor in your project. Claude Code loads it when a task matches its description.

How do I install Options Strategy Advisor in Codex?

Run `npx skills add tradermonty/claude-trading-skills --skill options-strategy-advisor -a codex`. Or copy the skill folder (skills/options-strategy-advisor in tradermonty/claude-trading-skills) into .agents/skills/options-strategy-advisor in your project. Codex loads it when a task matches its description.

Can I use Options Strategy Advisor in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add tradermonty/claude-trading-skills --skill options-strategy-advisor -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/options-strategy-advisor, .gemini/skills/options-strategy-advisor, .github/skills/options-strategy-advisor and .opencode/skills/options-strategy-advisor in your project.

What does Options Strategy Advisor need to run?

Going by SKILL.md and its folder, Options Strategy Advisor needs Python for the scripts in its folder, the command-line tools its instructions call (python3 and pip) and credentials named FMP_API_KEY. Our summary lists: Python 3; A credential in FMP_API_KEY.

Does Options Strategy Advisor access the network?

SKILL.md names 2 domains. As links in the text: optionsplaybook.com and cboe.com. This is read from the text; nothing was executed.

Is Options Strategy Advisor safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Options Strategy Advisor use?

Options Strategy Advisor is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Options Strategy Advisor use?

About 7.3k tokens (SKILL.md is roughly 29k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 1.1k tokens, read only when the agent opens those files.

What are the alternatives to Options Strategy Advisor?

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Who maintains Options Strategy Advisor?

tradermonty (a GitHub user) maintains it in tradermonty/claude-trading-skills, which has 2,977 GitHub stars. The repository holds 74 skills in this directory. The repository was last updated on October 9, 2026.

Source: tradermonty/claude-trading-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.