Bitcoin Improvement Proposal 110 (BIP-110) has become one of the most discussed proposals in the Bitcoin ecosystem in 2026. The proposal aims to limit the amount of arbitrary data that can be embedded in Bitcoin transactions, primarily targeting inscription-based protocols such as Ordinals, BRC-20, and Runes. Supporters believe the proposal will help reduce blockchain bloat, lower node operating costs, and preserve Bitcoin’s role as a peer-to-peer payment network. Critics, however, argue that it could restrict legitimate use cases, impact Layer-2 development, and introduce protocol-level censorship.

With the miner signaling window approaching, the discussion around BIP-110 has expanded beyond technical implementation to broader questions about Bitcoin’s governance, decentralization, and future development. This article examines why the proposal was introduced, the changes it proposes, the arguments from both sides, and what the outcome could mean for the Bitcoin network.

What Is BIP-110?

BIP-110 is a proposed temporary soft fork that introduces stricter limits on how arbitrary data can be stored within Bitcoin transactions. The proposal was first introduced in October 2025 under the placeholder draft name BIP-444 by pseudonymous Bitcoin developer Dathon Ohm.

Its primary objective is to reduce non-financial data stored on the Bitcoin blockchain by restricting the transaction structures commonly used by Ordinals, BRC-20 tokens, and Runes. The proposal also aims to reduce blockchain growth, lower node hardware requirements, and improve accessibility for individuals running full Bitcoin nodes.

Although the proposal’s technical specification has been marked as complete, it still requires ecosystem support before any activation can occur.

Why Was BIP-110 Proposed?

The proposal was introduced in response to the rapid growth of inscription-based protocols that use Bitcoin block space to store images, tokens, and other forms of arbitrary data. Supporters argue that these applications have significantly increased blockchain storage requirements while driving higher transaction fees for standard Bitcoin users.

According to the proposal, four major issues have emerged:

Proponents also argue that recent policy changes in Bitcoin Core made it easier for data-heavy transactions to enter the network, accelerating blockchain growth and increasing pressure on node operators.

What Changes Would BIP-110 Introduce?

Rather than banning inscription protocols directly, BIP-110 modifies transaction validation rules to make storing large amounts of arbitrary data significantly more difficult.

These restrictions would significantly impact protocols that rely on embedding large amounts of data on-chain.

Potential Impact Across the Ecosystem

Why Has the Proposal Become So Controversial?

BIP-110 has divided the Bitcoin community over a fundamental question: Should Bitcoin prioritize its role as a monetary network, or remain completely permissionless regardless of how block space is used?

Supporters argue that inscription-based protocols are consuming valuable block space, increasing node costs, and making it more expensive for users to participate in the network.

Luke Dashjr, one of Bitcoin’s long-time developers and a supporter of the proposal, has described BIP-110 as “a temporary measure designed to keep validation accessible and protect node operators from unnecessary storage costs.”

Jason Hughes, Vice President of Development and Engineering at OCEAN, echoed a similar view, saying:

“We need to maintain the purity of the blockchain’s base layer to keep it decentralized. BIP-110 restores historical policy caps that should never have been bypassed.”

Independent Bitcoin researcher Robert Allen also believes action is necessary, stating:

“BIP-110 is imperfect, but it is highly preferable to leaving the issue of blockchain spam completely unaddressed.”

Veteran Bitcoin investor Fred Krueger took a broader perspective on the debate, saying:

“Eventually we will figure out some way to deal with spam, quantum, and other issues… Bitcoin will make it through.”

Despite these arguments, opposition to BIP-110 remains significant.

Adam Back, CEO of Blockstream, dismissed the proposal as an unnecessary attempt to regulate how users interact with the network, describing it as a “quest to police other people,” which he believes conflicts with Bitcoin’s permissionless design.

Bitcoin security expert Jameson Lopp has also criticized the proposal, arguing that its architectural priorities are misplaced and warning against introducing consensus changes that could affect broader ecosystem development.

Developer Peter Todd questioned the proposal’s effectiveness, arguing that determined users could bypass many of the proposed restrictions, limiting its practical impact.

Community criticism extends beyond developers. Crypto analyst Javier Hermosa compared the proposal’s supporters to overly restrictive policy advocates, while Ki Young Ju, CEO of CryptoQuant, remarked:

“BIP-110 is like amending the constitution to ban littering in the park.”

Similarly, Samson Mow questioned the proposal’s chances of success, stating:

“It doesn’t have consensus… especially amongst technical development experts… and there are a lot of ordinary people using Bitcoin that don’t agree with it either.”

Supporters vs Critics

What Happens Next?

The next stage for BIP-110 is the miner signaling period scheduled to begin in August 2026. According to the available proposal details, miner support currently remains extremely limited, with signaling reported at approximately 0.31%.

If sufficient support is not achieved during the activation window, the proposal is unlikely to move forward in its current form. However, the broader discussion around inscription protocols, node costs, and Bitcoin’s long-term scalability is expected to continue regardless of BIP-110’s outcome.

Conclusion

BIP-110 has evolved beyond a technical proposal into a broader discussion about Bitcoin’s future. While supporters view it as a way to reduce blockchain bloat and improve node accessibility, critics believe it could limit innovation and alter Bitcoin’s permissionless nature. Regardless of its outcome, the proposal is likely to influence future discussions on Bitcoin governance and protocol development.

Why Is BIP-110 Creating So Much Controversy, and What Could It Mean for Bitcoin’s Future? was originally published in Coinmonks on Medium, where people are continuing the conversation by highlighting and responding to this story.

Crypto markets enter a high-impact week as traders monitor inflation reports, Fed policy signals, corporate earnings, and the CLARITY Act hearing for potential market catalysts.

The cryptocurrency market is entering a busy week as investors prepare for several major U.S. economic events, corporate earnings reports, and regulatory developments that could impact risk sentiment across financial markets.

Bitcoin and other cryptocurrencies continue to react closely to macroeconomic signals, especially inflation trends, Federal Reserve rate expectations, and global market uncertainty. This week’s schedule includes important economic indicators along with a major crypto regulation hearing that traders will be watching closely.

Markets React to Strait of Hormuz Closure

Markets begin the week with investors assessing the impact of developments surrounding the Strait of Hormuz closure.

The region plays a major role in global energy markets, and any disruption could affect oil prices, inflation expectations, and overall investor sentiment.

Higher energy costs could create additional inflation concerns and influence expectations around Federal Reserve monetary policy. Since crypto markets often move alongside broader risk assets, increased uncertainty could lead to higher volatility in Bitcoin and altcoins.

June CPI Inflation Data — Tuesday, July 14

One of the most important economic events arrives on Tuesday, July 14, with the release of June Consumer Price Index (CPI) inflation data.

The CPI report will provide fresh insight into whether inflation continues to cool or remains elevated. A lower-than-expected inflation reading could increase expectations for future Federal Reserve rate cuts, potentially supporting risk assets like Bitcoin and Ethereum.

However, stronger inflation numbers may reduce hopes for monetary easing and create short-term pressure across financial markets, including cryptocurrencies.

June PPI Inflation Data — Wednesday, July 15

On Wednesday, July 15, investors will focus on June Producer Price Index (PPI) inflation data.

The PPI report tracks changes in prices received by producers and offers another view of inflation trends. Rising producer prices could signal ongoing inflation pressure, while weaker data may improve confidence that inflation is moving toward the Federal Reserve’s target.

Crypto traders will monitor the report as inflation trends continue to influence liquidity conditions and investor risk appetite.

June Retail Sales Data — Thursday, July 16

Thursday, July 16, will bring June Retail Sales data, providing insight into U.S. consumer spending strength.

Consumer activity remains a key indicator of economic health. Strong retail sales could show resilience in the economy but may also reduce expectations for immediate rate cuts.

Meanwhile, weaker consumer spending data could raise concerns about economic slowdown while increasing expectations for a more accommodative monetary policy environment, which may benefit risk assets.

July Philly Fed Manufacturing Index — Thursday, July 16

Also on Thursday, July 16, markets will receive the July Philadelphia Fed Manufacturing Index.

The report will provide a snapshot of manufacturing activity and business conditions in the U.S.

A stronger manufacturing reading could support confidence in economic growth, while weaker numbers may increase concerns about slowing economic momentum.

July Michigan Inflation Expectations — Friday, July 17

On Friday, July 17, investors will watch July Michigan Inflation Expectations data.

Inflation expectations are closely monitored by the Federal Reserve because rising expectations can influence future price trends and monetary policy decisions.

A rise in inflation expectations could create pressure on markets, while stable or declining expectations may support investor confidence.

July Michigan Consumer Sentiment Data — Friday, July 17

Also scheduled for Friday, July 17, is the July Michigan Consumer Sentiment report.

The data will provide insight into consumer confidence and how households view current and future economic conditions.

Improving sentiment could support broader market optimism, while declining confidence may increase concerns about economic weakness.

Around 10% of S&P 500 Companies Report Earnings

Alongside economic releases, approximately 10% of S&P 500 companies are expected to report earnings this week.

Corporate earnings results could influence overall market direction and investor confidence. Strong earnings may support risk assets, while weaker-than-expected results could increase market volatility.

Since Bitcoin and crypto assets have shown stronger connections with traditional markets in recent years, equity market movements could also impact digital asset sentiment.

CLARITY Act Hearing — July 17

The final major event arrives on July 17, when the House Financial Services Committee will hold a key hearing in New York focused on the CLARITY Act.

Lawmakers are expected to discuss an updated version of the legislation, which combines proposals from multiple Senate committees. However, several important provisions remain under negotiation, creating uncertainty around the timeline for a potential Senate floor vote.

The outcome of the hearing could influence expectations around U.S. crypto regulation. Progress toward clearer rules may improve institutional confidence in digital assets, while delays could extend uncertainty for the industry.

What Crypto Investors Should Watch

This week brings multiple market-moving factors, from inflation reports and economic data to corporate earnings and crypto regulation discussions.

For Bitcoin and the broader cryptocurrency market, the combination of inflation trends, Federal Reserve expectations, and regulatory developments will likely determine short-term market sentiment.

Traders will be closely watching whether economic data supports a more favorable environment for risk assets or creates additional pressure across crypto markets.

Key U.S. Economic Events This Week That Could Impact Crypto Market Sentiment was originally published in Coinmonks on Medium, where people are continuing the conversation by highlighting and responding to this story.

H1 2026 was one of the toughest six-month periods for crypto in recent memory. The total crypto market cap fell from $3.32 trillion in January to $2.28 trillion by June, while Bitcoin dropped 31.5% and Ethereum lost roughly 32%–40%. Yet even in this broad market decline, a few crypto categories still generated outsized gains and created the strongest wealth-building opportunities.

The key question is not which sector had the biggest market cap, but which category delivered the highest concentration of high-return opportunities. Based on the data, Real World Assets (RWA) emerged as the strongest overall category in H1 2026, while AI tokens and select meme coins produced the most explosive individual token returns.

H1 2026 crypto market overview

The first half of 2026 was defined by a sharp contraction across the crypto market. Bitcoin dominance climbed from 57%–58% in January to 63% by June, showing that capital rotated away from many altcoins and back into Bitcoin during the downturn.

Which crypto category performed best?

RWA was the clear category winner because it combined positive market-cap growth, the largest capital inflows, and strong institutional demand. AI and meme coins still produced massive individual token gains, but as categories they did not outperform RWA overall.

Why RWA stood out in H1 2026

RWA benefited from a different type of demand than most crypto narratives. While meme coins and AI tokens relied heavily on retail speculation, RWA attracted institutional capital through tokenized treasuries, equities, and real-world yield products.

The sector saw +$9.4 billion in capital inflows, +66% TVL growth, and trading volume growth of +115%. Its market size expanded from roughly $52 billion to $60–63.6 billion, making it one of the few crypto categories to grow during a broader market downturn.

This matters for the millionaire-making narrative because RWA created wealth through sustained capital appreciation and institutional adoption, not just short-lived speculation. Investors who positioned early in RWA-related projects benefited from both rising valuations and a growing narrative around tokenized real-world assets.

AI tokens still created explosive gains

AI tokens were one of the most exciting narratives of H1 2026, even though the category’s overall market cap declined from $29.5 billion to $25 billion. The sector attracted $340 million in capital inflows and saw +45% trading-volume growth, driven by AI-agent perpetuals and meme-style speculation around AI projects.

The reason AI still matters in this article is simple: individual AI-related tokens delivered some of the highest returns in the market. Even if the category as a whole was down, select AI tokens created outsized wealth for early investors.

Top-performing tokens in H1 2026

MUMU was the standout performer, surging +123,407.72% from January to June. That type of return is exactly why meme coins remain part of the millionaire-making conversation, even when the broader meme category was down overall.

Meme coins: high risk, high reward

Meme coins had a mixed H1 2026. The category’s market cap fell from roughly $47 billion to $24.48–30.6 billion, and capital inflows turned negative as money rotated back into blue-chip assets. Trading volume also dropped 22% after the hype peak of 2024 and 2025.

However, meme coins still produced the single largest individual token gain through MUMU. This shows the difference between category performance and individual token performance. The meme sector was weak overall, but a few speculative tokens created life-changing returns for early holders.

Capital flows reveal where smart money went

Capital flow data confirms that RWA attracted the strongest conviction from investors. Layer-1 and Layer-2 ecosystems also received positive inflows, but they did not match the scale of RWA’s institutional demand.

By contrast, DeFi suffered the largest outflow at -$45 billion, with TVL dropping 39.1% from $115 billion to $70 billion. Gaming also struggled, with -$180 million in outflows and a 50.77% decline in market cap.

Did these categories really create the most millionaires?

No public dataset can verify the exact number of millionaires created by each crypto category. But the available data strongly suggests that RWA created the most sustainable wealth opportunities, while AI and meme coins created the most explosive short-term gains.

RWA stands out because it combined positive category growth, the largest capital inflows, and institutional adoption. AI tokens stand out because they produced several triple-digit returns, even in a declining category. Meme coins stand out because they produced the single most extreme gain through MUMU.

Final verdict

RWA was the strongest crypto category in H1 2026 when measured by category growth, capital inflows, and institutional demand. It offered the clearest path to sustainable wealth creation during a difficult market period.

However, if the question is about which category produced the most explosive millionaire-making returns, then AI tokens and meme coins deserve the spotlight. AI delivered strong speculative momentum, while meme coins produced the extraordinary MUMU rally.

The safest conclusion is this: RWA won on overall category strength, while AI and meme coins produced the highest-risk, highest-reward opportunities in H1 2026.

Which Crypto Category Created the Most Millionaires in H1 2026? was originally published in Coinmonks on Medium, where people are continuing the conversation by highlighting and responding to this story.

Predicting the exact bottom of a Bitcoin bear market has long been one of the greatest challenges for investors. Every major downturn is accompanied by widespread fear, conflicting expert opinions, and increasing market uncertainty. During the 2022 cryptocurrency crash, for example, several prominent analysts predicted that Bitcoin would fall below $10,000, while others believed the market had already reached its lowest point. In reality, Bitcoin bottomed near $15,742 before beginning its recovery.

This recurring pattern raises an important question: Can artificial intelligence (AI) identify Bitcoin bear market bottoms more accurately than experienced human investors?

Unlike humans, AI models are not influenced by fear, greed, or media narratives. Instead, they analyze vast amounts of historical price data, blockchain activity, trading volume, and market sentiment to identify patterns that may indicate when Bitcoin is approaching a market bottom. However, cryptocurrency markets are also shaped by unpredictable events such as exchange failures, regulatory decisions, pandemics, and geopolitical uncertainty — factors that even sophisticated AI models struggle to anticipate.

This article explores Bitcoin’s major bear markets over the past fifteen years, compares human predictions with AI-driven forecasting techniques, and evaluates whether machine learning can genuinely improve investors’ ability to identify market bottoms.

Bitcoin Bear Markets: A History of Extreme Volatility

Since its creation in 2009, Bitcoin has experienced multiple severe bear markets, each triggered by different economic or industry-specific events. While the causes varied, every cycle tested investor confidence and challenged analysts attempting to predict the market bottom.

Although Bitcoin’s volatility has gradually declined as the market matured, accurately identifying the bottom has remained remarkably difficult. Every bear market has been accompanied by pessimistic forecasts, many of which significantly underestimated Bitcoin’s long-term resilience.

Why Human Investors Struggle to Identify Market Bottoms

Human decision-making is rarely objective during financial crises. Behavioral finance research shows that investors often react emotionally during periods of uncertainty, allowing fear and panic to influence their decisions.

During Bitcoin bear markets, several psychological biases become particularly evident:

• Loss Aversion: Investors fear additional losses and sell near the bottom.
• Recency Bias: Recent price declines are assumed to continue indefinitely.
• Confirmation Bias: Investors seek opinions that reinforce their bearish outlook.
• Herd Behaviour: Market participants follow the crowd instead of analyzing data independently.

These biases were clearly visible during the 2022 cryptocurrency crash. As Bitcoin fell below $30,000, DoubleLine Capital CEO Jeffrey Gundlach suggested that prices could decline to $10,000, reflecting growing concerns about tightening monetary policy and liquidity risks. Similarly, Bloomberg Intelligence strategist Mike McGlone warned that structural weakness could push Bitcoin toward the same level.

More recent forecasts also illustrate the uncertainty surrounding market bottoms. Analyst Doctor Profit projected a cyclical bottom between $40,000 and $50,000, while on-chain analyst Leshka estimated a structural floor between $40,700 and $47,500, demonstrating that even experienced market participants often disagree significantly.

These examples highlight a fundamental limitation of human forecasting: investment decisions are influenced not only by market data but also by emotions, personal experience, and rapidly changing news cycles.

How Artificial Intelligence Approaches Market Bottom Prediction

Artificial intelligence takes a fundamentally different approach. Rather than relying on intuition or subjective interpretation, machine learning models analyze thousands of historical observations simultaneously to detect recurring market patterns.

Modern Bitcoin forecasting systems typically combine several categories of information:

• Historical Bitcoin prices (Open, High, Low, Close)
• Trading volume
• Technical indicators
• On-chain blockchain metrics
• Market sentiment
• Macroeconomic variables

Among the most widely used AI techniques are Long Short-Term Memory (LSTM) networks, XGBoost, ARIMA, Prophet, and hybrid deep-learning architectures.

Unlike traditional statistical models, deep learning algorithms are capable of identifying complex nonlinear relationships between multiple variables. For example, AI can simultaneously evaluate declining exchange reserves, improving network activity, increasing hash rate, and historically low valuation metrics to estimate whether Bitcoin may be entering an accumulation phase.

Your research also identifies several important blockchain indicators frequently incorporated into AI-based forecasting systems:

• Market Value to Realized Value (MVRV)
• Net Unrealized Profit/Loss (NUPL)
• Spent Output Profit Ratio (SOPR)
• Puell Multiple
• Exchange Reserves
• Bitcoin Hash Rate

These indicators provide information beyond simple price movements, enabling AI models to assess investor profitability, miner behavior, network security, and long-term market valuation.

Academic research further supports the growing role of AI in cryptocurrency forecasting. The two studies included in your research compare machine learning approaches such as LSTM, ARIMA, XGBoost, Prophet, and sentiment analysis, concluding that deep learning models generally outperform traditional statistical methods for short-term Bitcoin price prediction. However, these studies also acknowledge an important limitation: predicting the exact bottom of a bear market remains considerably more challenging than forecasting short-term price movements.

AI vs. Human Investors: Who Predicts Bitcoin Bottoms Better?

Although artificial intelligence has significantly improved financial forecasting, claiming that AI can consistently predict Bitcoin bear market bottoms better than humans would be misleading. Instead, the evidence suggests that both approaches possess unique strengths and limitations.

Human investors excel at interpreting qualitative information such as regulatory announcements, geopolitical developments, institutional adoption, and unexpected economic events. For example, experienced investors can assess the implications of Bitcoin ETF approvals or changes in central bank policy long before these factors are fully reflected in historical datasets. However, humans are also highly susceptible to emotional decision-making. Fear, greed, confirmation bias, and herd behavior often lead investors to panic sell near market bottoms or become overly optimistic near market peaks.

Artificial intelligence, in contrast, operates without emotional bias. Machine learning algorithms continuously process thousands of data points, identifying statistical relationships that would be difficult for humans to detect manually. By combining historical prices, blockchain metrics, trading volume, sentiment indicators, and macroeconomic variables, AI can recognize conditions that historically preceded Bitcoin recoveries.

However, AI has one significant weakness: it depends on historical data. When unprecedented events occur, such as the collapse of Mt. Gox, the COVID-19 pandemic, or the failure of FTX, AI models may struggle because these events have few historical precedents. Human judgment remains valuable in interpreting such extraordinary circumstances, where contextual understanding is often more important than pattern recognition.

What Do On-Chain Metrics Reveal?

One of AI’s greatest advantages is its ability to integrate multiple blockchain indicators simultaneously instead of relying solely on price action.

The on-chain metrics collected for this study including MVRV, NUPL, SOPR, Puell Multiple, Exchange Reserves, and Hash Rate have historically provided valuable insights into Bitcoin market cycles.

Several recurring patterns emerge across previous bear markets:

MVRV Ratio: Historically, values below their long-term average have coincided with periods where Bitcoin was significantly undervalued. AI models frequently use this metric to identify potential accumulation zones rather than precise market bottoms.

NUPL (Net Unrealized Profit/Loss): When market sentiment shifts toward capitulation, NUPL typically enters historically depressed levels, reflecting widespread investor losses and pessimism.

SOPR (Spent Output Profit Ratio): During bear markets, SOPR often falls below one, indicating that investors are selling coins at a loss. Sustained recovery above this threshold has historically signaled improving market conditions.

Puell Multiple: This indicator evaluates miner profitability. Extremely low values have frequently appeared near previous Bitcoin cycle bottoms, suggesting periods of miner capitulation.

Exchange Reserves: Declining Bitcoin balances on exchanges generally indicate that investors are moving coins into long-term storage rather than preparing to sell, reducing immediate selling pressure.

Hash Rate: Despite severe price declines, Bitcoin’s hash rate has generally continued to recover over time, reflecting long-term confidence among miners and strengthening network security.

Individually, these indicators cannot identify the exact bottom. However, AI models gain a significant advantage by evaluating them together, recognizing combinations of signals that have historically preceded market recoveries.

Lessons for Investors

The evidence suggests several important lessons.

• Predicting the exact bottom remains extremely difficult.
• Human investors frequently make emotional decisions.
• AI provides objective, data-driven insights but cannot predict unprecedented events.
• Combining AI with disciplined investment strategies such as Dollar-Cost Averaging (DCA) is often more effective than relying solely on intuition.

Conclusion

Bitcoin’s history demonstrates that neither humans nor AI can consistently predict the exact bottom of every bear market. Human investors possess contextual understanding and adaptability but are susceptible to emotional biases. Artificial Intelligence excels at processing enormous datasets and identifying historical market patterns, yet it remains constrained by the quality of historical information and struggles with black swan events.

Therefore, AI should not be viewed as a replacement for human judgment but rather as a powerful decision-support tool. Investors who combine AI-driven analytics with sound risk management and long-term discipline are better positioned to navigate Bitcoin’s volatile market cycles.

Can AI Predict Bitcoin Bear Market Bottoms Better Than Humans? A Data-Driven Analysis was originally published in Coinmonks on Medium, where people are continuing the conversation by highlighting and responding to this story.

Bitcoin’s Realized Profit/Loss (P/L) Ratio has declined to -0.35, its lowest level in 43 months, according to on-chain data from CryptoQuant. The metric measures whether Bitcoin holders are realizing profits or losses based on the price at which their coins last moved on the blockchain.

Historically, similar readings have appeared during periods of market weakness and have often been observed near previous cycle lows. While the indicator has attracted attention from long-term investors, it does not confirm that the market has reached its bottom.

What the Realized P/L Ratio Indicates

The Realized P/L Ratio compares the value of realized profits with realized losses across the Bitcoin network. When the ratio falls below zero, it indicates that more losses are being realized than profits.

A reading of -0.35 suggests that selling at a loss has increased. Such periods are generally associated with reduced market confidence and increased selling pressure. In previous market cycles, similar conditions were followed by periods of accumulation, although the timing and outcome varied.

Because the indicator reflects on-chain activity rather than short-term price movements, it is commonly used alongside other market and macroeconomic data.

Strategy’s Preferred Stock Draws Market Attention

Some market participants have linked the recent decline in sentiment to developments involving Strategy, the largest corporate holder of Bitcoin.

Attention increased after the company’s perpetual preferred stock offering, Stretch (STRC), traded below its $100 par value and fell to under $75. The decline led to concerns among some investors about the sustainability of the dividend structure associated with the offering.

Although these developments affected market discussions, there is no confirmed evidence that they were the primary reason for Bitcoin’s recent price movement.

Adam Livingston Highlights Bitcoin’s Realized Price

Crypto analyst Adam Livingston said the current market conditions resemble previous periods when Bitcoin traded close to its realized price.

The realized price represents the average price at which every Bitcoin last moved on-chain. It is often viewed as the average cost basis of all Bitcoin holders.

According to Livingston, Bitcoin is currently trading about 16% above its realized price, meaning the average holder remains only modestly in profit.

Based on historical market data, Livingston noted the following average returns after Bitcoin traded around this level:

• 41% at 6 months
• +81% at 12 months
• +121% at 18 months
• +323% at 24 months.

He also noted that, in previous market cycles, the 18-month and 24-month periods following similar conditions ended with positive returns. However, historical performance should not be considered a guarantee of future results.

Bitcoin ETF Inflows Resume

Institutional investment activity has also shown signs of improvement.

U.S. spot Bitcoin exchange-traded funds (ETFs) recently recorded approximately $221.7 million in net inflows, ending a 10-session period of net outflows during which nearly $2.7 billion left the funds.

The improvement followed weaker-than-expected U.S. economic data, which reduced concerns about future interest rate decisions by the Federal Reserve. During the same period, Bitcoin recovered from around $61,000 to approximately $62,500.

Despite the recent inflows, June remained the weakest month for U.S. spot Bitcoin ETFs since their launch, with total monthly net outflows of about $4.5 billion.

Historical Data Points to July Performance

Some analysts have also referred to Bitcoin’s historical monthly performance.

Crypto analyst Cyclop, citing data from CoinGlass, said Bitcoin has recorded gains of more than 20% during July in previous bear-market years.

While seasonal patterns are often used as a reference, analysts note that market conditions differ across cycles and historical trends do not ensure similar performance in the future.

Analysts Compare the Current Correction With Previous Cycles

Crypto analyst Ardi compared the current correction with previous Bitcoin bear markets.

According to Ardi, earlier market cycles typically spent around one year forming a bottom before a sustained recovery began. Based on the current correction lasting roughly nine months, he suggested that Bitcoin may be entering the period that has historically been associated with higher probabilities of a market bottom.

He also noted that the duration of market cycles varies, meaning any bottom could occur earlier or later than previous averages.

Conclusion

Bitcoin’s Realized P/L Ratio has reached its lowest level in 43 months, placing one of the market’s widely followed on-chain indicators back into focus. At the same time, Bitcoin continues to trade close to its realized price, institutional ETF inflows have resumed after a period of withdrawals, and several analysts have compared current market conditions with previous bear-market cycles.

Although these indicators provide historical context, they do not confirm future market direction. Investors typically consider on-chain data together with macroeconomic conditions, liquidity, and market sentiment before making investment decisions.

Bitcoin Realized P/L Ratio Hits Lowest Level in 43 Months: What It Means for BTC was originally published in Coinmonks on Medium, where people are continuing the conversation by highlighting and responding to this story.