AVNT up by 148.26% within 24 hours, driven by significant trading volume and increased buying momentum
As of September 17, 2025,
This sharp rally in AVNT highlights a rapid shift in market sentiment, with heightened buying activity observed over the last day. The increase followed a lengthy consolidation phase, culminating in a clear breakout that drove prices significantly above major resistance points. The momentum appears to be fueled by a blend of strong on-chain inflows and favorable on-chain metrics, though there was no particular news event identified as the spark. Notably, institutional participation was evident, as trading volumes diverged considerably from typical patterns, indicating potentially coordinated purchasing.
On the technical analysis front, AVNT has entered uncharted price levels after establishing a bullish reversal pattern near its 50-day moving average. The RSI, which had lingered in oversold conditions for several days, has now moved back into the overbought range, pointing to vigorous short-term strength. The asset also broke above a crucial rising trendline that previously capped its price action. The ongoing bullish MACD crossover, with both lines trending higher and the histogram widening positively, further supports the momentum. Analysts suggest the upward trajectory could persist if the $1.0137 price level remains a reliable support.
Backtesting Theory
The current technical signals—especially the RSI divergence, MACD crossover, and notable changes in volume—provide the foundation for a backtesting approach. A trading strategy built around these factors would involve taking long positions when the price surpasses a specified trendline and the RSI exits oversold conditions, setting a stop-loss just beneath a pivotal support level. This theory posits that a breakout from a consolidation phase, paired with rising volume, often precedes a lasting rally. Additional historical analysis would be necessary to verify this method across multiple AVNT market cycles and determine how on-chain inflow data could improve timing for entries and exits.
