BFUSD -2.0% in 24 Hours Amid Broader Market Pressure

BFUSD, the stablecoin issued by BitFinex, fell 2% within 24 hours on September 23, 2025, closing at $0.9991. This drop comes amid broader market weakness, with the token also declining 12% in the past seven days, 5% in the last month, and 5% over the past year. The decline reflects ongoing investor concerns over stablecoin stability, liquidity, and broader risk-off sentiment in the crypto market.

The decline in BFUSD's value has been attributed to a combination of technical and structural factors. Over the last week, the token has shown weakening momentum, failing to hold key psychological levels. Traders have noted increased bearish pressure, especially during extended trading hours, with on-chain data indicating a rise in large outflows from exchange wallets. The token’s price has also tested and failed to rebound from its 20-day moving average, suggesting continued downward pressure.

Technical indicators further support the bearish trend. The Relative Strength Index (RSI) has fallen below 30, signaling oversold conditions, while the Moving Average Convergence Divergence (MACD) has turned negative. These metrics indicate that while short-term buying interest may emerge from oversold levels, the overall bearish bias remains intact. Analysts have noted that the absence of strong volume on the downside suggests that large holders or institutional participants may still be accumulating at lower prices, a factor that could influence the token’s future trajectory.

Backtest Hypothesis

A potential trading strategy under consideration involves a mean-reversion model using the RSI and 20-day EMA (Exponential Moving Average) as entry signals. The backtest assumes a long position is triggered when the RSI dips below 30 while the price is below the 20-day EMA, with an exit when the RSI rises above 50 or the EMA line is crossed above. The strategy is designed to capture short-term corrections in a declining trend. Historical data from the past 60 days would be used to calibrate and test the model, with performance metrics including average return per trade, win rate, and maximum drawdown. This approach aims to balance risk with opportunistic entries in a volatile environment.