T-REX 2X Long APH Daily Target ETF

APHU— · USD
22.61USD0.00 (-1.31%)

T-REX 2X Long APH Daily Target ETF (APHU) Implied Volatility Current

APHU implied volatility is 79%. IV Rank is —%, placing current premiums in the middle of their 52-week range.

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Tracking APHU implied volatility helps you identify when options premiums on T-REX 2X Long APH Daily Target ETF are historically cheap or expensive, and where the best trades are hiding. T-REX 2X Long APH Daily Target ETF implied volatility reflects the market's expectation of future price movement: when APHU IV rises, option premiums increase, creating opportunities for sellers, and when it drops, buyers can find cheaper contracts. Use our scanner to monitor T-REX 2X Long APH Daily Target ETF's implied volatility current levels in real time and filter for high-probability trades.

Implied volatility is derived from option prices using models like Black-Scholes and represents the annualized expected move of an underlying stock. For APHU, tracking metrics like APHU IV rank helps traders understand whether current implied volatility is historically high or low. IV rank compares today's reading against the past year's range — a high rank on APHU signals rich premiums and potential mean-reversion, while a low rank may favor long options strategies.

Premium sellers, directional traders, and spread builders all need the same starting point: a clear read on where APHU implied volatility sits today versus where it has been. Our scanner ranks T-REX 2X Long APH Daily Target ETF implied volatility against its historical range, surfaces extremes in APHU IV rank, and pairs every reading with the trades that exploit it. Stop guessing whether T-REX 2X Long APH Daily Target ETF IV is rich or cheap — measure it, then act on it.

Implied Volatility

IV Rank
IV Rank
Implied Volatility (30d)78.64%

IV Rank

Historical Volatility (30d)80.20%

IV - HV-1.56%

As of September 18, 2026

Trade options with IV on your side

Track APHU IV rank across every expiration, spot where volatility is elevated, and identify high-probability setups before the window closes.

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