Free Options Probability Cone Visualizer

What this options probability cone measures

A single annualized implied-volatility input drives an illustrative lognormal price distribution at each future date. Positive prices are preserved, unlike a symmetric dollar band that can cross below zero. The percentages describe the assumed distribution, not evidence about the future accuracy of an option quote.

How to read the output

1. Enter a supported stock or ETF ticker and a whole horizon from 7 to 365 calendar days, then generate the cone.
2. Inspect the actual contract, strike, expiry and IV selected from the returned sample. A nearest available contract is not necessarily at the money or close to the requested maturity.
3. Compare intervals at individual dates in the table. The requested final horizon is included even when it is not a standard table interval.

Model and assumptions

S(t) = S(0) × exp(−0.5 × σ² × t + σ × √t × Z)

Here t is calendar days divided by 365, σ is the selected annualized IV, and Z is standard normal. The zero arithmetic-drift assumption keeps the mean equal to the starting quote. The median is S(0) × exp(−0.5 × σ² × t). Using Z = ±1 and ±2 gives central model probabilities of approximately 68.27% and 95.45%. Log-price dispersion grows with the square root of time; dollar bands are asymmetric.

Limits that matter

IV depends on the option and the pricing assumptions used to infer it. This page holds one IV fixed and does not interpolate maturities or estimate drift, dividends, skew, jumps or event risk. Its zero-drift assumption is neither an estimated real-world return nor a complete risk-neutral pricing model. The first 250 contracts sorted by strike can omit nearer strikes and expiries. Inputs above 1000% IV are outside this tool’s supported domain.

A terminal interval is not a barrier or a bound on losses. A price may cross a band and return before the target date, or finish outside the outer band. The chart does not establish a trading edge, identify cheap options, or calculate the probability of profit for any strategy.

Sources

• NIST: lognormal distribution and quantiles
• OIC: option price behavior and implied volatility
• Massive: option-chain snapshot fields