About OpenGradient (OPG):
At $0.147472, OpenGradient (OPG) carries a market cap of $28.02M and logs about $32.78M in 24h turnover. Liquidity reads as high liquidity turnover (volume/market cap 116.99%) High turnover often reduces the impact of single orders on price..
Where it trades:
A significant share of spot activity is hosted on Pancakeswap Infinity CLMM (BSC), LBank and Bybit, where OpenGradient (OPG) is most commonly traded. Liquidity conditions are often strongest where order-book depth is deepest. A broader venue footprint can help smooth execution during normal conditions.
Market assessment:
Bull score 51/100 suggests moderate momentum with a developing directional bias with selective participation across venues..
Returns snapshot: -8.18% / -4.98% / -38.38% across 24h, 7d and 30d windows. Returns remain pressured across major windows. The 24h tape is lively, though still within moderate bounds. Risk is assessed as Low, which implies more stable conditions with tighter volatility behavior Liquidity changes can alter the risk profile quickly..
OpenGradient (OPG) is positioned in the Early phase,
typically associated with early-cycle structure with initial trend development Rotation across sectors can be choppy in early phases..
Supply ceiling details are not provided in the current snapshot.
Conclusion: Netting the signals, conditions read as a transitional structure with no dominant directional bias. Update date: 2026-06-19.
Newest YearBull Rank value for opengradient: #1664.
Rank timeline (last 365 days)
Rank movement (nearest daily data).
Reading rule: smaller rank numbers are better.
7d window (2026-06-12): #2305 → #1664 (up by 641).
30d window (2026-05-20): #2253 → #1664 (up by 589).
YearBull Rank is a relative placement score used on YearBull to compare a coin against peers within the same dataset. Lower rank numbers correspond to stronger relative placement.
Cycle view: If the line is range-bound, treat changes as relative, not absolute.
Execution context: If rank holds gains, the footprint is likely supporting the move.
Risk view: If the last month is chaotic, widen the lookback before concluding.
Turnover context: If the curve jumps, check whether the cohort moved too (relative effects).
Practical note: direction and persistence matter more than the last tick.
Editorial note:
This analysis was prepared by the YearBull research team under the direction of
Alan Zelvin,
Founder and Lead Crypto Researcher.
The assessment follows YearBull’s internal research methodology and editorial standards.
Methodology ·
Editorial Policy
We use cookies and similar technologies to improve site functionality, analyze usage, and enhance user experience.
By continuing to use YearBull, you agree to our use of cookies as described in our
Privacy Policy.
Comments