Pyth Network (PYTH) is tracked by TokenRadar as a Infrastructure, Decentralized Finance (DeFi), Oracle, Analytics asset. Pyth Network is a decentralised oracle network designed to provide real-time financial market data to decentralised applications (dApps) across multiple blockchains. Data snapshot date: May 14, 2026. This overview focuses on market structure, historical reference points, liquidity, and risk context rather than buy or sell recommendations.
| Metric | Value |
|
|
|
| Price | $0.0504 |
| Market Cap | $289.59M |
| 24h Change | -9.27% |
| Market Rank | #158 |
| 24h Volume | $17.42M |
| ATH Distance | -95.79% |
Market Position for PYTH
Pyth Network trades at $0.0504 with a market cap of $289.59M and 24h volume of $17.42M. The latest ranked market snapshot places PYTH at #158 by market capitalization. The token is -95.79% from its all-time high of $1.2, recorded on March 16, 2024. Its all-time low is $0.0368, recorded on March 28, 2026, while the 30-day move is +16.39%. Circulating supply is 5,749,982,050.06, total supply is 10,000,000,000, and max supply is 10,000,000,000. The volume-to-market-cap ratio is 6.02%, which helps separate active markets from thin markets. A higher ratio usually means recent trading activity is easier to observe, while a very low ratio can make price changes less reliable as a signal.
TokenRadar Risk Context
TokenRadar currently assigns Pyth Network a risk score of 7/10, a growth potential index of 52/100, narrative strength of 65/100, and a volatility index of 36/100. The computed risk level is high. Pyth Network is a high-risk, near ATH token.
Risk should be read together with liquidity. Pyth Network has 24h high and low levels of $0.0563 and $0.0499, so the short-term range is visible before considering broader 7-day and 30-day changes of -2.13% and +16.39%.
Historical Data Points
The historical reference set for PYTH includes an all-time high of $1.2 on March 16, 2024, an all-time low of $0.0368 on March 28, 2026, and a 1-year move of -74.62%. These figures do not predict the future, but they show whether the current price is near extremes or in a middle range.
Supply and Valuation
Circulating supply is 5,749,982,050.06, total supply is 10,000,000,000, and max supply is 10,000,000,000. Fully diluted valuation is $503.63M. Market cap and FDV can diverge when a large portion of supply is not circulating, so investors should compare circulating supply, total supply, and unlock or emission information before relying on valuation multiples.
What Could Change the Setup
Pyth Network would need stronger evidence across volume, liquidity, development activity, and category momentum for the setup to improve. Weak follow-through, falling volume, large unlocks, contract migrations, or negative security events would weaken the research case even if the spot price rises for a short period.
FAQ
What is Pyth Network?
Pyth Network is a decentralised oracle network designed to provide real-time financial market data to decentralised applications (dApps) across multiple blockchains.
Is PYTH a low-risk asset?
No crypto asset should be treated as low risk by default. TokenRadar currently shows a risk score of 7/10, and that score should be checked against volatility, liquidity, supply, and recent events.
What market data matters most for PYTH?
The most useful starting points are price, market cap, 24h volume, market rank, ATH distance, circulating supply, and the 30-day trend.
Does this overview recommend buying Pyth Network?
No. It is a structured research summary for comparing data points and risks.
Disclaimer: This article is for informational purposes only and does not constitute financial advice. Always do your own research (DYOR).
For PYTH research, the important control is consistency between the live market snapshot and the longer historical record. A single 24h move can be noisy, while market cap rank, 30-day performance, all-time high distance, supply structure, and volume-to-cap ratio create a more durable comparison set. This is why the article separates market data from decision rules and keeps the conclusion conditional.
Pyth Network also needs category-aware interpretation. A Infrastructure token with high liquidity can behave very differently from a low-volume asset with the same percentage move. The practical question is whether volume, spread quality, supply data, and catalyst evidence confirm the move or contradict it.
The safest way to use this overview article is as a checklist. Confirm the latest price, check the current rank, compare 24h volume with market cap, review the ATH and ATL dates, and look for any project-specific changes before treating the data as current enough for research.
For PYTH research, the important control is consistency between the live market snapshot and the longer historical record. A single 24h move can be noisy, while market cap rank, 30-day performance, all-time high distance, supply structure, and volume-to-cap ratio create a more durable comparison set. This is why the article separates market data from decision rules and keeps the conclusion conditional.
Pyth Network also needs category-aware interpretation. A Infrastructure token with high liquidity can behave very differently from a low-volume asset with the same percentage move. The practical question is whether volume, spread quality, supply data, and catalyst evidence confirm the move or contradict it.
The safest way to use this overview article is as a checklist. Confirm the latest price, check the current rank, compare 24h volume with market cap, review the ATH and ATL dates, and look for any project-specific changes before treating the data as current enough for research.
For PYTH research, the important control is consistency between the live market snapshot and the longer historical record. A single 24h move can be noisy, while market cap rank, 30-day performance, all-time high distance, supply structure, and volume-to-cap ratio create a more durable comparison set. This is why the article separates market data from decision rules and keeps the conclusion conditional.
Pyth Network also needs category-aware interpretation. A Infrastructure token with high liquidity can behave very differently from a low-volume asset with the same percentage move. The practical question is whether volume, spread quality, supply data, and catalyst evidence confirm the move or contradict it.
