get_monte_carlo
Run a Monte Carlo simulation to generate probabilistic price ranges and quantify downside risk for a stock over a 30-day horizon.
Instructions
Run a Monte Carlo price-path simulation for a stock over a 30-day horizon.
Use this tool when:
You need a probabilistic price range rather than a single point estimate.
You want to quantify downside risk (e.g., probability of a 10 % drawdown).
You are sizing a position using a volatility-adjusted scenario.
The simulation uses a GBM (Geometric Brownian Motion) model calibrated with the stock's realized volatility and current IV. 10,000 paths are run by default.
Parameters
symbol : str Exchange ticker in uppercase, e.g. "MSFT", "NVDA", "SPY".
Returns
dict with keys: symbol : str — normalized ticker current_price : float — spot price at simulation start mean_price : float — expected price at horizon range_90 : dict — {"lower": float, "upper": float} 90 % CI range_68 : dict — {"lower": float, "upper": float} 68 % CI prob_above_spot: float — probability (0–1) price is above current spot prob_10pct_drop: float — probability (0–1) of ≥10 % decline distribution : dict — histogram data: {"bins": list, "frequencies": list, "kde_x": list, "kde_y": list}
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| symbol | Yes | Exchange ticker in uppercase, e.g. 'MSFT', 'NVDA', 'SPY'. Do NOT pass company names — use official tickers only. |