The top potential bitcoin prices for this cycle has been a hot topic of discussion over the last few years. The rally to a new all-time high, exceeding $123,000 in July introduced a new vitality into the debate as major cryptocurrencies believe they have reached their highest points and others continue to expect higher prices. The call ranged from $150,000 to $500,000, but as the market matures, the potential for a major price crash for Bitcoin remains.
What the previous cycle performance says about the top Bitcoin prices
Crypto and market expert Mike Alfred went to the X (formerly Twitter) platform and shared where he thought Bitcoin prices were headed. Alfred shows how old cycle performance and subsequent Bear Markets use this to do this, and how much the price crashes from here.
First, there was the bull market in 2014, when we saw Bitcoin price crash from $1,000 to $200. This was an 80% crash from the top. Again, in 2018, Bitcoin prices crashed from highs to lows of $20,000 to $3,200, resulting in a price drop of 84%.
Following the same trend, similar prices fell deeply after Bitcoin prices exceeded $69,000 in 2021 and then crashed violently in 2022 due to factors like the FTX collapse. Ultimately, cryptocurrency bottomed out at around $16,000 before the rebound, dropping its price by around 80%.
Using this trend, Crypto analysts expect Bitcoin prices to crash on similar metrics, not before hitting a new high of over $300,000. In the post, Alfred will reach the current Bitcoin cycle top of $312,000 before a market crash occurs.
Once this level is achieved, we expect the next wave to reduce the crash at a low price of $75,000. This is a 76% decrease. Additionally, analysts are hoping that this is not happening in 2025, but a crash will occur in 2026.
Depending on the post, another X user, Becky, I opposed Bitcoin price cannot reach $300,000 It refers to realized volatility It indicates that this is not possible. However, Alfred exposes this by the fact that the volatility realized is not static and does not accurately predict the period of higher volatility before.
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