LQTY Up 334.19% in 24 Hours Amid Strong Short- and Medium-Term Gains

On SEP 3 2025, LQTY surged by 334.19% within 24 hours to reach $0.811. The token also posted gains of 360.82% over the past 7 days and 592.89% over the past 30 days. While the 1-year performance remains significantly negative at -5545.71%, the recent upward momentum has captured the attention of traders and investors.

The surge in LQTY’s price is attributed to a combination of on-chain activity and speculative trading interest. A growing number of unique wallet addresses have been interacting with LQTY in recent days, signaling increased participation in the token’s ecosystem. On-chain data also reflects a decline in the number of wallets holding large amounts of LQTY, suggesting distribution and reduced concentration among large holders. Analysts note that these movements align with typical patterns observed prior to price rallies in similar assets.

Technical indicators on LQTY have shown significant bullish divergence. The Relative Strength Index (RSI) has moved above the 60 threshold, while the Moving Average Convergence Divergence (MACD) has crossed into positive territory, confirming upward momentum. The 50-day and 200-day moving averages are converging, which historically has been associated with breakout patterns. These indicators are frequently referenced by algorithmic traders to identify potential entry and exit points.

Backtest Hypothesis

A proposed backtesting strategy for LQTY involves using a combination of RSI and MACD crossovers to generate buy and sell signals. The approach is based on the idea that divergence in these indicators can reliably predict short-term price movements. In the strategy, a long position is triggered when RSI crosses above 60 and the MACD line crosses above the signal line. A short position is initiated when the opposite occurs, with stop-loss and take-profit levels defined based on the volatility and average true range of the asset.

This strategy is intended to capture the volatility seen in LQTY over the past month. By simulating past trades based on the same indicators, it is possible to assess the viability of this approach. The backtest would use historical data from the past year to measure the win rate, risk-reward ratio, and maximum drawdown of the strategy. If the results align with expected patterns—such as increased accuracy during periods of high on-chain activity—the strategy could be further refined for live trading conditions.