FF +37.9% in 24 Hours Amid Sharp Price Fluctuations

On OCT 7 2025, FF rose by 37.9% within 24 hours to reach $0.00015422, FF dropped by 925.04% within 7 days, dropped by 1921.86% within 1 month, and dropped by 4435.11% within 1 year.

The asset has experienced a dramatic short-term rebound, reversing a multi-week downward trend. The 24-hour gain marks the largest intraday increase since the beginning of the year. Despite this, the longer-term performance remains severely bearish, with cumulative losses over the past 7 days and 1 month exceeding 900% and 1900%, respectively. This sharp reversal has drawn attention from traders and observers who are analyzing the potential for a broader market turnaround or a temporary bounce amid continued downward pressure.

Technical indicators suggest that the asset may still be in a bearish phase despite the recent upswing. A breakdown below key resistance levels in previous weeks has not been followed by a clear reestablishment of bullish momentum. Instead, the price appears to be testing the lower bounds of a multi-month downtrend. Traders are closely watching for confirmation of a sustained reversal or for signs of further volatility and retesting of critical support levels.

Backtest Hypothesis

A proposed backtesting strategy has been outlined to evaluate potential trade signals emerging from this recent volatility. The strategy employs a combination of moving average crossovers and RSI divergence to identify possible entry and exit points. The core assumption is that a significant intraday gain like the one observed on OCT 7 may precede a short-term reversal in the broader downtrend, especially if it coincides with overbought RSI conditions and a bullish moving average crossover.

The hypothesis involves entering a long position on a close above a 50-period moving average, with a stop loss placed below the 200-period line. A take-profit target is set at the nearest identifiable resistance level. This approach is backtested over a six-month historical period to assess its robustness and to identify performance metrics such as win rate, average gain per trade, and maximum drawdown.