Bitcoin on-chain and options data point to a decisive move in BTC price

Bitcoin volatility has fallen to historically low levels thanks to macroeconomic uncertainty and low market liquidity. However, on-chain and options market data point to incoming volatility in June.

The Bitcoin Volatility Index, which measures daily fluctuations in the price of Bitcoin (BTC), shows that the 30-day volatility in the price of Bitcoin was 1.52%, which is less than half of the annual averages in the history of Bitcoin, with values ​​usually above 4%. .

According to Glassnode, the expectation of volatility is a “logical conclusion” based on the fact that low levels of volatility were only observed for 19.3% of Bitcoin’s price history.

The latest weekly update from the on-chain analytics company shows that Glassnode’s monthly realized volatility metric for Bitcoin dropped below the lower bounds of the historical Bollinger Band, signaling an incoming uptick in volatility.

Statistics for long-term Bitcoin holders point to a price breakout

Bitcoin’s on-chain transfer volumes across cryptocurrency exchanges fell to historically low levels. The price is also trading close to near-term holder preference, suggesting a “balanced position of gains and losses for new investors” who bought coins during and after the 2021-2022 bull cycle, the report said. . Currently, 50% of new investors make a profit, the rest make a loss.

However, while short-term stockholders reached equilibrium levels, long-term stockholders saw movement in the recent correction, which analysts say is supporting volatility.

Glassnode categorizes coins older than 155 days in a single wallet under long-term supply.

The gray bars in the image below show the long-term holder (LTH) binary spending indicator, which tracks whether average LTH spending over the last seven days is enough to reduce their total holdings.

It shows past instances where LTH spending increased, which was usually followed by an increase in volatility.

Bitcoin’s recent correction saw a small pullback in the indicator, “suggesting a net divestment from LTHs in 4 days out of 7, which is a level comparable to exit liquidity events YTD.”

The analysts expect a period of volatility to reach an equilibrium level, with the market moving primarily due to the accumulation or distribution of supply from long-term holders.

Options markets confirm traders’ expectation of volatility

The data in the options market points to a similar theory of impending volatility.

The options market’s final expiration for May turned out to be a dull event, despite a large expiration of $2.3 billion in notional value. However, long-term compression of volatility could indicate major inward movement in terms of price.

Bitfinex’s latest Alpha report shows that the DVOL index, which represents the market’s expectation of 30-day future implied Bitcoin volatility, fell to 45 from a position of 50 just before expiration, representing a yearly low.

Implied volatility in options refers to the market’s expectation of the future volatility of the underlying asset, as reflected in options prices.

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Bitfinex analysts said low expectations of volatility could arise due to “upcoming events expected to move the market” or “increased uncertainty or risk aversion among market participants”.

Currently, options traders are showing risk aversion and have increased their bearish positions from May to June.

The put-to-call ratio for Bitcoin options rose from 0.38 to 0.50. A higher weight of put options shows that traders are increasingly bearish on Bitcoin.

Analysts at Bitfinex are currently expecting “potential market turbulence and near-term price swings” in June, especially towards the end of the month.

The potential price levels that could act as a magnet according to the positioning of the options market are the maximum pain levels for the May and June expirations at $27,000 and $24,000 respectively.

Maximum pain, also known as maximum pain or option pain, is a concept used in options trading and refers to the price at which the buyers incur maximum losses.

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