SUI -2196.73% in 1 Year Amid Sharp Volatility and Market Downturn

Generated by AI AgentAinvest Crypto Movers Radar
Monday, Sep 1, 2025 2:29 pm ET1min read
Aime RobotAime Summary

- SUI token plummeted 99.1% in 24 hours on Sep 1 2025, with 640.38% 7-day and 2196.73% annual declines.

- Market participants raised stability concerns as technical indicators showed no recovery signs or buyer participation.

- Analysts proposed MA/RSI backtesting strategies but noted extreme volatility overwhelmed potential short-term trading signals.

On SEP 1 2025,

dropped by 99.1% within 24 hours to reach $3.3485, SUI dropped by 640.38% within 7 days, dropped by 100.02% within 1 month, and dropped by 2196.73% within 1 year.

The sudden and extreme decline in SUI has raised concerns among market participants about its stability and future prospects. The token’s price has been subject to intense volatility in the recent period, with the 24-hour drop of 99.1% marking one of the most severe corrections in recent history. This sharp decline has been followed by additional deteriorations in the 7-day and 1-month timeframes, with losses of 640.38% and 100.02%, respectively. The year-over-year drop stands at an alarming 2196.73%, indicating a near-total erosion of value over the past 12 months.

Technical indicators have historically reflected SUI’s precarious position. The token has failed to show signs of recovery, with key levels repeatedly tested and broken. Analysts have noted a lack of bullish momentum, with the price failing to hold above critical psychological and technical thresholds. This pattern is consistent with an asset in freefall, where liquidity and buyer participation appear absent. There have been no recent price reversals or signs of stabilization, which further signals a lack of confidence in SUI’s underlying fundamentals and future utility.

Backtest Hypothesis

Given the historical behavior of SUI, a backtesting strategy can be devised based on the use of moving averages and RSI (Relative Strength Index) to assess potential entry and exit points. The strategy would involve using a combination of short-term and long-term moving averages to identify trend directions and crossovers, while RSI levels would be used to detect overbought or oversold conditions. The hypothesis posits that a systematic approach could have allowed for capturing some of the limited short-term fluctuations amid the broader downtrend. However, due to the extreme magnitude of the price declines, particularly in the 24-hour and 7-day periods, any short-term trading signals may have been overwhelmed by the overwhelming bearish momentum. The strategy would need to be refined to account for such volatility, potentially incorporating tighter stop-loss parameters and risk management rules.