Bitcoin Depot Inc. (BTM) Probability Analysis
Probability analysis extracts the risk-neutral probability distribution implied by option prices. It shows the market-implied likelihood of the underlying reaching various price levels by expiration.
Bitcoin Depot Inc. (BTM) operates in the Financial Services sector, specifically the Financial - Credit Services industry, with a market capitalization near $1.9M, listed on NASDAQ, employing roughly 126 people, carrying a beta of 3.13 to the broader market. Bitcoin Depot Inc. Led by W. Alexander Holmes, public since 2022-04-19.
Snapshot as of May 29, 2026.
- Spot Price
- $0.51
- ATM IV
- 498.0%
- IV Rank
- 100.0%
- IV Percentile
- 100.0%
- HV 20-Day
- 499.8%
- IV Skew 25Δ
- -2.808
As of May 29, 2026, Bitcoin Depot Inc. (BTM) at $0.51 has an ATM IV of 498.0%, implying a 30-day one-standard-deviation range of approximately ±$0.73. IV rank is 100.0% (elevated, distribution priced wider than typical). IV percentile is 100.0%. The 25-delta skew is -2.808: downside tail priced richer than upside, biasing probability mass below spot. Under lognormal assumptions roughly 68% of outcomes fall within ±1σ and 95% within ±2σ; risk-neutral probability analysis refines this by extracting the market-implied distribution directly from options prices, capturing the fat tails that real markets exhibit.
How BTM probability analysis Data Feeds Strategy Selection
Strategy selection on Bitcoin Depot Inc. options does not derive from any single metric in isolation. The probability analysis view above sits inside a broader read: ATM IV currently sits at 498.0% and dealer gamma exposure is positive, so dealer hedging is mechanically mean-reverting. Combine the probability analysis data here with the volatility-skew surface, dealer-gamma exposure, max-pain level, and upcoming-events calendar to build a positioning thesis. Risk-defined structures (credit spreads, debit spreads, iron condors) are usually safer than naked positions while the regime is uncertain; the data on this page anchors the inputs but does not by itself constitute a trade thesis.
How to read the BTM probability distribution
The probability cone above is the option-market-implied distribution of where Bitcoin Depot Inc. spot could end up at expiration. It's derived from the implied-volatility surface via a risk-neutral pricing transformation, not from historical realized returns. With ATM IV at 498.0% and spot at $0.51, the 1σ band is approximately ±171.8% over a 30-day horizon. Recent realized HV-20 of 499.8% runs 1.8 vol points above current implied, an inverted regime where premium buyers are underpaying.
BTM risk-neutral vs real-world probabilities
The probabilities derived from option prices reflect the market's risk-adjusted view, not the realized statistical distribution. Risk-neutral probabilities include the equity risk premium and skew preferences priced into options, so they tend to overstate tail probability and understate upside drift relative to actually-realized outcomes. BTM's put-skewed 25-delta surface (-2.808) means downside risk-neutral probabilities are higher than upside - the empirical bias is well-documented. For probability-of-touch calculations and assignment-risk modeling, risk-neutral is the right benchmark. For position-sizing your own conviction, blend with realized-volatility-based statistics from the HV columns.
Trading the BTM distribution
Probability-driven strategies aim to capture mispricings between the implied distribution and your own probability assessment. Premium-selling structures (credit spreads, iron condors, cash-secured puts) profit when the implied distribution overprices tail probability relative to realized; premium-buying (debit spreads, long calls/puts, long straddles) profits in the reverse. With BTM IV rank at 100.0%, the chain is pricing fatter tails than recent realized history; sellers earn the gap on average. Always pair probability-driven strategy selection with a stop loss or wing-defined risk - the implied distribution is a snapshot, and regime shifts can invalidate it intraday.
Learn how risk-neutral density is reported and how to read the data →