On September 20, 2025, PUMP experienced a 192.58% increase in value over a single day, hitting $0.007306. Within the following seven days, it saw a sharp decline of 1534.54%, but on a monthly and yearly basis, the token was up by 6223.9%.
This rapid 24-hour price jump represented a significant, albeit temporary, change in the ongoing volatility. Despite the steep drop of 1534.54% over the next week, PUMP showed notable strength with a 6223.9% rise over one month. The same impressive growth rate was observed when comparing to the previous year, highlighting the token’s long-term upward momentum.
The notable increase in the last 24 hours could indicate a short-term change in investor sentiment or liquidity influx, although the substantial fall in the following week points to persistent market instability. Experts note that such dramatic swings are typical for assets that are heavily influenced by speculative trading.
Looking at the technical analysis, the strong one-month growth demonstrates an ongoing bullish trend, possibly driven by broader economic conditions or changing trader strategies. The 6223.9% yearly surge further confirms this long-term positive direction, implying that the market has gradually stabilized after initial volatility. This pattern of bouncing back and continuous growth suggests PUMP is moving toward a more sustained upward trend after early turbulence.
Backtesting Strategy
One suggested method for examining PUMP’s price movements involves applying technical tools such as moving averages and the relative strength index (RSI) to spot possible trend reversals or continuation signals. The idea is that recent price fluctuations, when combined with historical data, could help predict future trends. Key signals—like a golden cross or RSI divergence—are considered potential triggers for buying or selling decisions.
The backtesting process would analyze how these indicators performed during the dramatic 24-hour and weekly price changes, assessing their effectiveness in anticipating the larger monthly and yearly gains. By following this systematic approach, the hypothesis tests whether technical signals corresponded with actual price trends and could have informed profitable trades during recent volatility.