SUSHI Falls by 3.37% on October 30, 2025 as Overall Market Declines
On October 30, 2025,
The daily loss fits into a broader trend of waning investor confidence, as shown by the token’s weak performance across various periods. The 62.7% drop over the past year indicates that long-term investors have suffered considerable losses, while the 3.37% decline in a single day highlights the market’s heightened reaction to both actual and perceived external factors. Even in the absence of direct news affecting SUSHI, the price
Current technical analysis points to a bearish outlook for SUSHI, with crucial support levels under threat. The token’s recent price movements have been shaped by overall market sentiment, including caution about the broader economy and volatility within the sector. Both investors and traders are monitoring for any signs of stabilization or further weakness, as price trends are likely to remain a primary signal for on-chain activity and market sentiment.
This ongoing decline also emphasizes the critical role of liquidity and trading infrastructure in maintaining market stability. Without adequate buy-side depth or significant inflows from either institutional or retail investors, SUSHI remains vulnerable to sharp price fluctuations. Analysts warn that if the current pattern persists—especially if macroeconomic data does not improve or sector-specific risks grow—further losses could follow.
Backtest Hypothesis
To assess whether SUSHI might recover or continue to fall, a backtesting approach can be used, leveraging historical price data to anticipate future trends. One such method is the Event-Impact backtest, which examines how the token has previously reacted to significant daily price changes, such as a 10% drop within a day. This analysis can reveal whether past volatility has typically resulted in subsequent rebounds or continued declines.
However, applying this strategy to SUSHI’s data presents some obstacles. The backtesting tool encountered a division-by-zero error on at least one date when the price was recorded as zero, making it impossible to calculate returns. Additionally, a missing import in the code temporarily interrupted the process. To resolve these problems, it is necessary to clean the data by removing dates with zero or missing prices, and to restrict the analysis window to avoid outlier events. Using a 15-trading-day window after each event could yield a more reliable dataset while still meeting the goals of the backtest.
