INIT -35.87% in 24 Hours Amid Sharp Short-Term Volatility

On AUG 30 2025, INIT dropped by 35.87% within 24 hours to reach $0.352, following a dramatic 222.22% surge over the preceding 7 days. Over a 30-day period, the token fell by 1731.84%, marking one of the most volatile short-term movements in recent memory. Despite the sharp near-term correction, INIT has still gained 7020% over the past year, underscoring long-term bullish momentum against a backdrop of significant short-term turbulence.

The recent price movement was triggered by a sudden shift in market sentiment, with traders reacting to a combination of on-chain liquidity changes and broader macroeconomic factors. The token’s sharp 24-hour decline came after a brief but intense rally that saw it nearly triple in value over seven days. However, this rapid ascent failed to sustain momentum, leading to a swift reversal and a return toward prior levels of support. The 30-day drawdown further highlights the token’s sensitivity to both speculative positioning and macroeconomic headwinds, with capital flows shifting rapidly in response to market conditions.

Technical indicators painted a mixed picture as the recent swing unfolded. While the token’s long-term trend remains intact, as evidenced by the 7020% annual increase, short-term oscillators signaled overbought conditions prior to the correction. Moving averages showed signs of divergence in the days leading up to the 24-hour drop, with the 50-period line crossing below the 200-period line as a bearish signal. This structural shift in the short-term trend has led to increased caution among traders and analysts alike, many of whom are monitoring key support levels for signs of stabilization.

Backtest Hypothesis

The potential for a structured trading approach to capture some of the volatility in INIT’s short-term price action has led to the development of a backtesting strategy aimed at modeling the token’s price swings. The strategy is designed to isolate sharp movements like the 222.22% weekly increase and subsequent 35.87% decline, using predefined thresholds to trigger entry and exit points. By identifying and acting on overbought and oversold conditions, the backtest seeks to simulate a systematic approach to riding the token’s volatility while managing risk exposure.