Install
openclaw skills install @clementgu/alphagbm-pnl-simulatorP&L simulation engine for any single-leg or multi-leg option position. Generates profit/loss diagrams at expiry, P&L over time, what-if scenarios (price, IV, time), breakeven analysis, and probability distributions. Use when: testing a trade idea, visualizing risk/reward, running what-if scenarios, checking breakeven points, stress-testing a position. Triggers on: "simulate PnL for AAPL bull call spread", "what if NVDA drops 10%", "P&L diagram", "test my iron condor", "breakeven analysis", "stress test my position", "what happens at expiry".
openclaw skills install @clementgu/alphagbm-pnl-simulatorALPHAGBM_API_KEY (format agbm_xxxx...).https://alphagbm.zeabur.app. Override with env ALPHAGBM_BASE_URL.Simulates profit and loss for any option position across multiple dimensions -- underlying price, implied volatility, and time to expiration. Produces P&L diagrams, breakeven analysis, and probability-weighted outcome distributions.
| Strategy | Ideal Trend | Max Profit | Max Loss |
|---|---|---|---|
| Sell Put | Neutral / Bullish | Premium received | Strike - Premium |
| Sell Call | Neutral / Bearish | Premium received | Unlimited (uncovered) |
| Buy Call | Bullish | Unlimited | Premium paid |
| Buy Put | Bearish | Strike - Premium | Premium paid |
| Capability | Description |
|---|---|
| P&L at Expiry | Classic payoff diagram -- profit/loss vs. underlying price at expiration |
| P&L Over Time | How the position's value evolves from now to expiry (time-series curves) |
| What-If: Price | Vary underlying price by fixed amount or percentage -- see impact on P&L |
| What-If: IV | Vary implied volatility -- see how IV crush or spike affects the position |
| What-If: Time | Fast-forward to a specific date -- see theta decay impact |
| Probability Distribution | Monte Carlo simulation of outcomes with probability of profit |
| Breakeven Analysis | Exact breakeven points with time-varying breakevens before expiry |
POST /api/options/tools/simulate
Content-Type: application/json
{
"symbol": "AAPL",
"spot": 150.0,
"legs": [
{"action": "buy", "option_type": "call", "strike": 145, "expiry_days": 30, "iv": 0.26},
{"action": "sell", "option_type": "call", "strike": 150, "expiry_days": 30, "iv": 0.25}
]
}
Parameters:
"buy" or "sell""call" or "put"{
"ticker": "AAPL",
"price": 218.45,
"position": {
"strategy": "Bull Call Spread",
"legs": [
{"action": "buy", "type": "call", "strike": 215, "expiry": "2026-04-18", "price": 7.20, "qty": 1},
{"action": "sell", "type": "call", "strike": 225, "expiry": "2026-04-18", "price": 3.40, "qty": 1}
],
"net_debit": 380
},
"pnl_at_expiry": {
"price_axis": [195, 200, 205, 210, 215, 218.8, 220, 225, 230, 235],
"pnl_axis": [-380, -380, -380, -380, -380, 0, 120, 620, 620, 620]
},
"pnl_over_time": {
"dates": ["2026-03-29", "2026-04-04", "2026-04-11", "2026-04-18"],
"curves": {
"at_210": [-180, -220, -290, -380],
"at_218": [50, 30, 10, -20],
"at_225": [320, 400, 510, 620]
}
},
"breakevens": [218.80],
"max_profit": 620,
"max_loss": 380,
"risk_reward_ratio": 1.63,
"probability_of_profit": 0.56,
"expected_value": 42.50,
"scenarios": {
"price_down_10pct": {"pnl": -380, "pnl_pct": -100},
"price_up_10pct": {"pnl": 620, "pnl_pct": 163},
"iv_crush_50pct": {"pnl": -85, "note": "IV drop hurts long spread slightly"},
"iv_spike_50pct": {"pnl": 120, "note": "IV rise helps long spread slightly"}
}
}
| User Says | What Happens |
|---|---|
| "Simulate PnL for AAPL bull call spread" | Full P&L diagram at expiry + over time |
| "What if NVDA drops 10%?" | Price scenario analysis for current position |
| "P&L diagram" | Expiry payoff chart for any defined position |
| "Test my iron condor" | Full simulation with breakevens, max P&L, probability of profit |
| "Breakeven analysis for my spread" | Exact breakeven points + time-varying breakevens |
| "Stress test: what if IV doubles?" | IV shock scenario with P&L impact |
| "Monte Carlo for my straddle" | 10,000-path simulation with outcome distribution |
Demo tickers available without API key: AAPL, NVDA, SPY, TSLA, META. Simulations use realistic pricing models calibrated to mock-data/ snapshots.
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