BARD -3650.96% in 1 Year Amid Regulatory and Technical Downturn

On OCT 10 2025, BARD dropped by 864.37% within 24 hours to reach $0.6344, BARD dropped by 2086.86% within 7 days, dropped by 2728.75% within 1 month, and dropped by 3650.96% within 1 year.

BARD has faced a sharp and sustained decline over the past year, driven by a combination of regulatory scrutiny and deteriorating technical indicators. The asset has been under pressure since late 2024, when several jurisdictions began imposing tighter oversight on decentralized AI-driven financial tools, with BARD identified as a high-risk use case. This regulatory shift coincided with a broader loss of institutional support and reduced developer activity, compounding the downward momentum.

In early 2025, BARD’s token economics model was re-evaluated due to persistent negative cash flows and a declining total value locked (TVL) metric. Market participants observed that the token’s burn rate was no longer sufficient to offset issuance, leading to an imbalance in supply dynamics. These fundamental weaknesses have contributed to a lack of long-term confidence among investors and traders.

From a technical perspective, BARD has failed to regain control of key resistance levels that were previously seen as critical to reversing the bearish trend. The Relative Strength Index (RSI) has remained below 30 for an extended period, signaling prolonged oversold conditions and a lack of buyer interest. Meanwhile, the Moving Average Convergence Divergence (MACD) histogram has consistently shown bearish divergence, reinforcing the idea that sellers are in control.

The breakdown of key Fibonacci retracement levels has further eroded investor sentiment, with the 50% and 61.8% levels failing to provide meaningful support. These technical failures have been interpreted by traders as confirmation of a broader structural breakdown in the asset’s price action.

Backtest Hypothesis

A recent backtesting strategy has been proposed to assess potential recovery scenarios for BARD based on historical price behavior and technical patterns. The hypothesis involves a multi-timeframe analysis using a combination of moving averages and volume-based entry triggers. The strategy is designed to identify potential short-term reversals during extended downtrends while filtering out noise using a 20-period exponential moving average as a baseline.

The backtesting approach incorporates a long entry when price breaks above the 50-period SMA with a closing volume above the 20-day average. A stop-loss is placed below the 20-period EMA, while take-profit levels are set at the nearest psychological and Fibonacci resistance points. This method aims to capture momentum-driven bounces without assuming a full reversal of the long-term trend.