About Doggo Inu (DOGGO):
At $0.000000000071493, Doggo Inu (DOGGO) carries a market cap of $35.76K and logs about $32 in 24h turnover. Liquidity remains soft turnover conditions (volume/market cap 0.09%) Lower turnover can make short-term moves feel jumpier in fast markets..
Market assessment:
Bull score 69/100 suggests moderate momentum with mixed short-term signals with a neutral-to-mixed regime profile..
Recent change: 24h -0.21%, 7d 5.03%, 30d 12.69%. Windowed returns align with an emerging uptrend.
The 24h profile suggests a lower-volatility session. Risk is assessed as Low, which implies a lower-stress regime with fewer sharp swings Risk metrics are snapshot-based and can evolve with the market..
Doggo Inu (DOGGO) is positioned in the Early phase,
typically associated with early-cycle structure with initial trend development Early-cycle labels often reflect a market still building conviction..
Snapshot tokenomics details are limited for this asset.
Conclusion: Netting the signals, conditions read as a transitional structure with no dominant directional bias. Update date: 2026-04-11.
YearBull Rank overview
Most recent YearBull Rank reading for doggo-inu is #6463.
Rank timeline (last 365 days)
Rank change (reference points).
Reading rule: rank #120 sits higher than rank #200.
- 7d window: no reference point available.
- 30d window: no reference point available.
YearBull Rank is an internal ordering on YearBull that positions a coin relative to the rest of the tracked universe. Treat it as a directional context tool rather than a standalone verdict.
Cycle context: If the 30d is noisy, increase the lookback to avoid over-reading. cycle shifts often show up as slope changes, not spikes.
Where it trades: If the line is step-like, watch for discrete market changes. changes can follow how the coin is routed across markets.
Flow context: If the curve improves and holds, it is usually more structural. rank can move when liquidity redistributes across the cohort.
Volatility posture: If the curve is step-like, it may be reacting to discrete inputs. big jumps can be data-driven, but also rotation-driven.
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
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