Einstein Research — Options Strategy Advisor
by @clawdiri-ai
Options trading strategy analysis and simulation tool. Provides theoretical pricing using Black-Scholes model, Greeks calculation, strategy P/L simulation, a...
clawhub install einstein-research-options-dv📖 About This Skill
id: 'einstein-research-options' name: 'einstein-research-options' 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.' version: '1.0.0' author: 'DaVinci' last_amended_at: null trigger_patterns: [] pre_conditions: git_repo_required: false tools_available: [] expected_output_format: 'natural_language'
Options Strategy Analyzer
Overview
This skill provides comprehensive analysis, simulation, and risk management for options trading strategies. It combines theoretical pricing with practical P/L simulation to help users understand the risk/reward profile of various options positions.
Core Features:
When to Use This Skill
Explicit Triggers:
Implicit Triggers:
When NOT to Use:
us-stock-analysis)portfolio-risk-analyzer)macro-regime-detector)Workflow
Step 1: Gather Inputs
The user must provide the following parameters for analysis:
options-analyzer analyze \
--ticker AAPL \
--strategy covered-call \
--stock-price 175.00 \
--strike-price 180.00 \
--expiry-date 2026-04-17 \
--option-type call \
--risk-free-rate 0.05 \
--dividend-yield 0.005 \
# Optional:
--volatility 0.35 # (If not provided, script fetches historical volatility)
--num-shares 100
--num-contracts 1
Required Parameters:
--ticker: Underlying stock symbol--strategy: One of covered-call, protective-put, cash-secured-put, credit-spread, debit-spread, iron-condor, straddle, strangle--stock-price: Current price of the underlying stock--strike-price: Strike price of the option(s)--expiry-date: Expiration date of the option(s) (YYYY-MM-DD)--option-type: call or put--risk-free-rate: Current risk-free rate (e.g., 10-year Treasury yield)Optional Parameters:
--volatility: Implied volatility (if known, otherwise calculated from historical data)--dividend-yield: Annual dividend yield of the stock--num-shares: Number of shares held (for covered calls/puts)--num-contracts: Number of options contractsStep 2: Execute Analysis Script
Run the main analysis script with the gathered parameters:
python3 skills/options-analyzer/scripts/options_analyzer.py --ticker AAPL ...
The script performs the following actions: 1. Fetches historical data via yfinance to calculate historical volatility (if not provided). 2. Uses the Black-Scholes model to calculate the theoretical price of the option(s). 3. Calculates all relevant Greeks (Delta, Gamma, Theta, Vega, Rho). 4. Simulates the P/L of the strategy across a range of potential stock prices at expiration. 5. Identifies key metrics: max profit, max loss, break-even point(s). 6. Generates a P/L chart (ASCII or image) and a summary report.
Step 3: Analyze Results and Provide Recommendations
The script generates a JSON output and a human-readable Markdown report.
JSON Output (options_analysis_YYYY-MM-DD_HHMMSS.json):
{
"strategy": "Covered Call",
"ticker": "AAPL",
"theoretical_premium": 2.50,
"greeks": {
"delta": 0.45,
"gamma": 0.05,
"theta": -0.02,
"vega": 0.12,
"rho": 0.01
},
"simulation": {
"max_profit": 750.00,
"max_loss": -17250.00,
"break_even": 172.50,
"pnl_data": [...]
},
"recommendations": [
"This strategy profits if AAPL stays below $182.50 by expiration.",
"Time decay (theta) is beneficial, generating daily income.",
"High volatility (vega) increases the premium received but also risk."
]
}
Markdown Report (options_analysis_YYYY-MM-DD_HHMMSS.md):
Step 4: Present Findings to User
Synthesize the Markdown report into a clear, educational response.