Bitcoin’s price has entered a critical phase after dropping below $84,000, following one of its strongest weekly performances that saw the cryptocurrency reach $88,000. The latest market movement, triggered by February’s core inflation data, has left investors questioning the strength of key support levels.
As Bitcoin continues testing crucial support zones, on-chain data from Glassnode provides essential insights into potential price floors that could determine BTC’s next major move.
Understanding Bitcoin’s Current Support Structure
Glassnode’s cost basis distribution analysis reveals several critical support levels that could influence Bitcoin’s price trajectory:
- $84,100: 40,000 BTC accumulated
- $82,090: 50,000 BTC accumulated
- $80,920: 20,000 BTC accumulated
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Deeper Support Zones and Risk Analysis
The analysis identifies additional support levels that could become crucial if current levels fail:
- $74,000: Strong support with 49,000 BTC accumulated
- $71,000: Major accumulation zone with 41,000 BTC
Notably, the previously strong $78,000 support level has weakened following recent sell-offs, potentially creating vulnerability in the short term.
Market Impact and Trading Implications
The current 4% decline brings several key considerations for traders:
- Short-term volatility expected around $84,000
- Increased trading volume at support levels
- Potential for quick rebounds from accumulation zones
Frequently Asked Questions
What caused Bitcoin’s recent price drop?
The decline was primarily triggered by February’s core inflation data release and subsequent market reactions.
Where is the strongest support level for Bitcoin?
According to Glassnode data, the $82,090 level shows the strongest support with 50,000 BTC accumulated.
Could Bitcoin fall to $71,000?
While possible, multiple strong support levels would need to break first, with $74,000 serving as a major buffer zone.
As the market continues to digest these developments, traders should maintain close attention to these key support levels and corresponding volume patterns for potential entry and exit points.