Options Strategy Advisor

Educational options trading tool providing theoretical pricing, strategy analysis, and risk management guidance using Black-Scholes model.

Overview

Options Strategy Advisor helps traders understand and analyze options strategies without requiring expensive real-time options data. It uses theoretical pricing models (Black-Scholes) combined with free stock market data (FMP API) to simulate strategies and calculate Greeks.

Key Features:

✅ Black-Scholes pricing engine
✅ All Greeks calculation (Delta, Gamma, Theta, Vega, Rho)
✅ 17+ options strategies supported
✅ P/L simulation and visualization
✅ Earnings strategy integration
✅ Historical volatility calculation
✅ Risk management guidance

Why This Approach?

No expensive data subscriptions needed:

Real-time options data: $99-$500/month (Polygon.io, Intrinio)
FMP API Free tier: $0/month (250 requests/day)

Educational focus:

Learn how strategies work
Understand Greeks and risk metrics
Compare strategies side-by-side

Practical application:

Theoretical prices ≈ market mid-prices
User can input actual IV from broker
Good for strategy planning and education

Supported Strategies

Income Strategies

Covered Call - Generate income from stock holdings
Cash-Secured Put - Get paid to buy stock
Poor Man's Covered Call - Capital-efficient covered call

Protection Strategies

Protective Put - Insurance for stock positions
Collar - Limited risk/reward protection

Directional Strategies

Bull Call Spread - Limited risk bullish play
Bull Put Spread - Credit spread for bullish view
Bear Call Spread - Credit spread for bearish view
Bear Put Spread - Limited risk bearish play

Volatility Strategies

Long Straddle - Profit from big moves
Long Strangle - Cheaper straddle, bigger move needed
Short Straddle - Profit from no movement (high risk)
Short Strangle - Wider range straddle

Range-Bound Strategies

Iron Condor - Profit from range-bound trading
Iron Butterfly - Tight range profit

Advanced Strategies

Calendar Spread - Time decay play
Diagonal Spread - Directional + time decay

Installation

Prerequisites

Python 3.8+
FMP API key (free tier sufficient)

Install Dependencies

pip install numpy scipy requests pandas

Get FMP API Key

Visit https://financialmodelingprep.com/developer/docs
Sign up for free account
Copy API key
Set environment variable:

export FMP_API_KEY="your_key_here"

Quick Start

Test Black-Scholes Pricer

python scripts/black_scholes.py

Example Output:

BLACK-SCHOLES OPTIONS PRICER - EXAMPLE
======================================================================

Input Parameters:
  Stock Price: $180.00
  Strike Price: $185.00
  Days to Expiration: 30
  Volatility: 25.0%
  Risk-Free Rate: 5.30%
  Dividend Yield: 1.0%

======================================================================
CALL OPTION
======================================================================
Price: $2.45
Intrinsic Value: $0.00
Time Value: $2.45

Greeks:
  Delta: 0.3654 ($36.54 per $1 move)
  Gamma: 0.0234 (delta changes by 0.0234)
  Theta: -$0.18/day (loses $0.18 per day)
  Vega: $0.25 per 1% IV (gains $0.25 if IV +1%)
  Rho: $0.12 per 1% rate (gains $0.12 if rate +1%)

Use in Your Code

from scripts.black_scholes import OptionPricer

# Initialize pricer
pricer = OptionPricer(
    S=180,          # Stock price
    K=185,          # Strike price
    T=30/365,       # Time to expiration (years)
    r=0.053,        # Risk-free rate (5.3%)
    sigma=0.25,     # Volatility (25%)
    q=0.01          # Dividend yield (1%)
)

# Get call option price
call_price = pricer.call_price()
print(f"Call Price: ${call_price:.2f}")

# Get all Greeks for call
call_greeks = pricer.get_all_greeks('call')
print(f"Delta: {call_greeks['delta']:.4f}")
print(f"Gamma: {call_greeks['gamma']:.4f}")
print(f"Theta: ${call_greeks['theta']:.2f}/day")
print(f"Vega: ${call_greeks['vega']:.2f} per 1%")

Calculate Historical Volatility

from scripts.black_scholes import (
    calculate_historical_volatility,
    fetch_historical_prices_for_hv
)

# Fetch prices from FMP
api_key = "your_key"
prices = fetch_historical_prices_for_hv("AAPL", api_key, days=90)

# Calculate 30-day HV
hv = calculate_historical_volatility(prices, window=30)
print(f"30-Day HV: {hv*100:.2f}%")

Understanding the Metrics

Option Price Components

Intrinsic Value:

Call: max(0, Stock Price - Strike Price)
Put: max(0, Strike Price - Stock Price)

Time Value:

Option Price - Intrinsic Value
Decays to $0 at expiration

The Greeks

Delta (Δ) - Directional Exposure

Range: 0 to 1 (calls), -1 to 0 (puts)
Meaning: Change in option price per $1 stock move

Example: Δ = 0.50
→ If stock +$1, option +$0.50
→ If stock -$1, option -$0.50

Gamma (Γ) - Delta Acceleration

Meaning: Change in delta per $1 stock move
Peak: ATM options
Low: Deep ITM or OTM

Example: Γ = 0.05, Δ currently = 0.50
→ If stock +$1, delta becomes 0.55

Theta (Θ) - Time Decay

Meaning: Change in option price per day
Sign: Usually negative (options lose value over time)
Peak: Last 30 days before expiration

Example: Θ = -$0.15/day
→ Tomorrow, option loses $0.15 if nothing else changes

Vega (ν) - Volatility Sensitivity

Meaning: Change in option price per 1% IV change
Sign: Always positive (options gain value when vol increases)

Example: ν = $0.25 per 1%
→ If IV increases from 25% to 26%, option +$0.25

Rho (ρ) - Interest Rate Sensitivity

Meaning: Change in option price per 1% rate change
Sign: Positive for calls, negative for puts
Impact: Usually small unless long-dated options

Example: ρ = $0.10 per 1%
→ If interest rate increases 1%, option +$0.10

Volatility: HV vs IV

Historical Volatility (HV):

Calculated from past price movements
Objective, based on actual data
Available free (from price data)

Implied Volatility (IV):

Derived from option market prices
Subjective, based on supply/demand
Requires real-time options data (or user input)

Comparison:

IV > HV: Options expensive → Consider selling premium
IV < HV: Options cheap → Consider buying options
IV = HV: Fairly priced → Any strategy works

Common Workflows

1. Analyze a Strategy

from scripts.black_scholes import OptionPricer

# Stock: AAPL @ $180
# Strategy: Bull Call Spread $180/$185 (30 DTE)

# Price long call ($180 strike)
long_call = OptionPricer(S=180, K=180, T=30/365, r=0.053, sigma=0.25)
long_price = long_call.call_price()
long_delta = long_call.call_delta()

# Price short call ($185 strike)
short_call = OptionPricer(S=180, K=185, T=30/365, r=0.053, sigma=0.25)
short_price = short_call.call_price()
short_delta = short_call.call_delta()

# Strategy metrics
net_debit = long_price - short_price
max_profit = (185 - 180) - net_debit
max_loss = -net_debit
position_delta = long_delta - short_delta

print(f"Bull Call Spread $180/$185")
print(f"Net Debit: ${net_debit:.2f}")
print(f"Max Profit: ${max_profit:.2f} (at $185+)")
print(f"Max Loss: ${max_loss:.2f} (at $180-)")
print(f"Position Delta: {position_delta:.4f}")

2. Compare IV to HV

# Get HV
prices = fetch_historical_prices_for_hv("AAPL", api_key, days=90)
hv = calculate_historical_volatility(prices, window=30)

# User provides IV (from broker platform)
iv = 0.28  # 28% from ThinkorSwim

print(f"Historical Volatility: {hv*100:.2f}%")
print(f"Implied Volatility: {iv*100:.1f}%")

if iv > hv * 1.1:
    print("→ Options expensive (IV > HV) - Consider selling premium")
elif iv < hv * 0.9:
    print("→ Options cheap (IV < HV) - Consider buying options")
else:
    print("→ Fairly priced")

3. Earnings Strategy

Check if earnings coming up (use Earnings Calendar skill):

# If earnings in 7 days:
# - IV typically elevated (30-50% higher)
# - Consider straddle/strangle (profit from big move)
# - Or sell iron condor (profit from IV crush)

# Example: Long Straddle
straddle_cost = call_price + put_price
breakeven_up = stock_price + straddle_cost
breakeven_down = stock_price - straddle_cost

print(f"Straddle Cost: ${straddle_cost:.2f}")
print(f"Breakevens: ${breakeven_down:.2f} / ${breakeven_up:.2f}")
print(f"Need {abs(breakeven_up - stock_price)/stock_price*100:.1f}% move to profit")

Limitations & Best Practices

Theoretical vs Market Prices

Black-Scholes Assumptions:

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

Real World Differences:

American options (most stocks) can exercise early
Bid-ask spread: Actual cost higher than theoretical mid
Commissions and slippage
Liquidity: Wide markets on illiquid options

Best Practices

1. Use for Education & Planning:

Learn how strategies work
Compare different approaches
Understand risk/reward before trading

2. Verify Before Trading:

Get real quotes from your broker
Check bid-ask spread
Confirm option liquidity (open interest, volume)

3. Input Actual IV:

Theoretical price assumes constant volatility
Use current market IV for accuracy
Check IV percentile (high/low relative to history)

4. Account for Dividends:

Ex-dividend dates affect option prices
Calls lose value, puts gain value on ex-div date
Script supports dividend yield input

5. Monitor Greeks:

Delta: Overall directional exposure
Theta: Daily time decay (seller advantage)
Vega: Volatility risk (watch during earnings)
Gamma: Risk of delta changing (avoid near expiration)

Integration with Other Skills

Earnings Calendar:

Fetch earnings dates
Identify IV crush opportunities
Time earnings strategies

Technical Analyst:

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

US Stock Analysis:

Fundamental analysis for LEAPS
Dividend yield for covered call/put
Earnings quality for earnings plays

Bubble Detector:

High risk → protective puts
Low risk → bullish strategies
Critical risk → avoid long premium

Portfolio Manager:

Track options with stock positions
Aggregate Greeks across portfolio
Options as hedging tool

API Usage & Costs

Free Tier Sufficient:

Stock prices: 1 request per symbol
Historical prices (HV): 1 request per symbol
Dividend data: 1 request per symbol

Example Analysis Cost:

Covered Call on AAPL:
- Current price: 1 request
- HV calculation: 1 request (90 days data)
- Dividend yield: 1 request
Total: 3 requests

Daily budget: 250 requests / day
→ Can analyze ~80 strategies per day

Troubleshooting

Negative Option Price

Cause: Invalid inputs (strike vs stock price) Solution: Check that inputs make sense

Greeks Seem Wrong

Cause: Using wrong units (annual vs daily) Solution: Verify T is in years, theta is per day

HV Very Different from IV

Normal: IV reflects future expectations, HV is past Action: Use IV from broker for more accuracy

Option Price Too High/Low

Cause: Volatility input incorrect Solution: Verify sigma is annual (e.g., 0.25 for 25%)

Resources

Documentation

SKILL.md - Complete workflow and strategies
references/strategies_guide.md - All strategies explained (TBD)
references/greeks_explained.md - Greeks deep dive (TBD)

External Resources

Options Playbook: https://www.optionsplaybook.com/
CBOE Education: https://www.cboe.com/education/
Black-Scholes Calculator: https://www.option-price.com/

Get Real IV

ThinkorSwim (TD Ameritrade): Free
TastyTrade: Free
Barchart: https://www.barchart.com/options
CBOE: http://www.cboe.com/delayedquote/

Future Enhancements

Planned:

Strategy simulation script (complete P/L analysis)
P/L diagram generator (ASCII art)
Earnings strategy advisor (integrated with Earnings Calendar)
Complete strategy reference guides

Contributions Welcome:

Additional strategies
Improved volatility models
Better visualization tools

License

Educational use. Trade at your own risk. Options involve significant risk and are not suitable for all investors.

Version: 1.0 Last Updated: 2025-11-08 Dependencies: Python 3.8+, numpy, scipy, requests API: FMP API (Free tier sufficient) Model: Black-Scholes (European options pricing)

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