AXL -147.06% in 24 Hours Amid Sharp Price Correction

Generated by AI AgentAinvest Crypto Movers Radar
Saturday, Sep 6, 2025 12:44 pm ET1min read
Aime RobotAime Summary

- AXL plunged 147.06% in 24 hours on Sep 6, 2025, with 6132.76% annual decline, signaling extreme bearish pressure.

- Technical and on-chain data show liquidity withdrawal, inactive addresses, and failed support levels, confirming weak buyer demand.

- A backtesting strategy combines RSI, MACD, and on-chain metrics to predict AXL's volatility and assess post-correction recovery potential.

- Market observers monitor if AXL can stabilize below current levels or face further sell-offs amid deteriorating technical conditions.

On SEP 6 2025,

dropped by 147.06% within 24 hours to reach $0.00000269, AXL rose by 0% within 7 days, dropped by 428.57% within 1 month, and dropped by 6132.76% within 1 year.

Technical indicators suggest AXL is under intense bearish pressure, with price failing to hold above key support levels. Multiple on-chain metrics show a withdrawal of liquidity and a reduction in active addresses, signaling a lack of buyer interest. The 24-hour drop exceeds historical volatility thresholds and suggests a potential continuation pattern toward lower levels. The 7-day unchanged price reflects an absence of short-term momentum, while the monthly and yearly declines highlight a broader trend of declining demand and speculative interest. Market participants are closely watching whether AXL can establish a floor below its current level or if further sell-offs are likely in the near term.

Backtest Hypothesis

A proposed backtesting strategy for AXL is based on key technical indicators used in assessing price momentum and trend sustainability. The strategy employs a combination of relative strength index (RSI), moving average convergence divergence (MACD), and on-chain liquidity metrics to identify potential entry and exit points. Historical data from prior price movements in AXL are used to evaluate the effectiveness of the model in predicting short-term volatility and trend reversals. The hypothesis is that incorporating both price-based and on-chain signals will improve the accuracy of the model in detecting sell-offs and potential recovery phases. This method aims to validate whether the sharp price correction on SEP 6 could have been anticipated based on pre-existing technical and on-chain conditions. The model also tests the resilience of AXL following such corrections, assessing whether it can re-enter a stable price range or remains subject to further downward pressure.

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