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The volatile crypto market highlights the importance of dependable mechanisms to indicate likely price bottoms as well as market turning points. In this respect, Glassnode has recently unveiled a unique Capitulation Metric in line with the Cost Basis Distribution to provide traders with a data-led tool to navigate Bitcoin ($BTC) and overall market cycles. The platform shared the details of this metric in an exclusive report. Have $BTC or $ETH reached a local bottom yet? What about top meme coins? Our capitulation metric, based on Cost Basis Distribution (CBD), helps pinpoint these moments offering a data-driven view into potential market turning points. 🧵👇 pic.twitter.com/aR1dp8yrJs — glassnode (@glassnode) March 4, 2025 Glassnode Unveils Capitulation Metric, Offering Better Overview of Losses and Investor Sentiment As per Glassnode, the conventional metrics indicating realized loss deal with losses equally. However, deep drawdowns reportedly influence investors to a significant extent. Based on this, Capitulation Metric is responsible for identifying such a difference by implementing quadratic weighting, emphasizing bigger losses. The respective refined approach delivers a relatively precise representation of the selling pressure and the wider investor sentiment. A prominent feature of Glassnode’s Capitulation Metric take into account the detection of local bottoms in line with the capitulation signals. In addition to this, the metric also detects where troubled sellers quit the market. Moreover, it also leverages on-chain data to assist in making sagacious trading decisions. Crypto market bottoms often take place during the extreme distressful periods. These times are marked by peaks of forced sellings and capitulation by exhausted investors. The Capitulation Metric tracks these changes in the wider supply distribution by utilizing CBD. Strengthening Relatively Informed Decisions in Extreme Market Conditions According to Glassnode, the Capitulation Metric takes into account 3 main features. One is “Weighted Sell Volumes,” which implements a quadratic function to examine prices for the identification of severe losses. Additionally, another prominent feature is “Smoothing for Clarity.” it includes a 7-day EMA to filter out temporary noise to get robust trend analysis. Moreover, the 3rd well-known feature is “Non-Linear Economic Pain.” It weighs losses in line with psychological influence instead of nominal value. Overall, Capitulation Metric offers wider insights into likely buying opportunities, letting investors make relatively informed decisions in extreme conditions.

huge resatnce 1.90 to 2 dolr but unfortunately saling ar so much i hope price well down again 1.75 so everyone don't sale hold it if breaks 2 and stay above the next target is above 5

Lazarus Group launders $138M from the $1.4B Bybit hack. Can regulations stop future attacks?The recent Bybit hack has rattled the cryptocurrency industry. Hackers stole an astounding $1.4 billion and are rapidly laundering their stolen funds. Security researchers, law enforcement agencies, and industry leaders are responding urgently to contain the damage. The FBI and blockchain analysts are tracking and attempting to block these illegal transactions. However, hackers keep exploiting security flaws on decentralized platforms. This breach has reignited concerns about weaknesses in crypto security. Some community members are now urging stronger protective measures and improved risk management strategies to prevent future attacks. Lazarus Group Lanunders Nearly 70% of Stolen Crypto The North Korean Lazarus Group hackers masterminded one of the largest crypto hacks in history and stole $1.4B. Now, Lazarus group launders these stolen funds. EmberCN, a crypto researcher, has analyzed this group’s movements and has presented his findings. According to him, in their latest move, the Lazarus Group has laundered 62,200 ETH worth $138 million. This brings the amount of laundered funds to a staggering 343,000 ETH. With this movement, the hackers covered nearly 68.7% of the 499,000 ETH stolen in the infamous attack. The crypto researcher EmberCN projects that based on the current rate, the total funds will be laundered within three days. Such development is happening despite the FBI’s efforts to curb these hackers’ progress. FBI Calls for Industry Action FBI crypto investigation led to the release of a public announcement and addressed the hacking operation as TaderTraitor. This announcement highlighted how Lazarus Group hackers behind TraderTraitor are distributing the stolen funds in thousands of wallets. Moreover, it also mentions how the hackers have turned the assets into Bitcoin and other assets. These assets are expected to be turned into fiat currency after Lazarus Group launders them. Image 1 – Provided by FBI, published on crypto.news, March 3, 2025 In this announcement, the FBI also asked private companies and RPC node operators for assistance in this case. They provided addresses that were linked to the hackers and their activity. Moreover, the blockchain analytic firm Elliptic has also helped with the tracking process and has marked 11,000 wallets. As such, the FBI asked exchanges and other asset service providers to block any connection to these wallets and addresses. What Went Wrong at Bybit? According to Chainalysis, despite the FBI crypto investigation, the hackers have used various services without KYC requirements to hide their trail. They have used instant swap platforms, cross-chain services, and decentralized exchanges to launder the stolen assets. This incident has also caused discussions among crypto enthusiasts and the community about the security of crypto platforms. The release of a forensic review by Bybit also pinpointed the cause of the safe wallet system, further igniting the discourse. The founder of the decentralized data storage network AR.IO, Phil Mataras, highlighted the importance of tamper-proof storage in mitigating hack impacts. Louis Bellet, a lead architect in a decentralized network, also mentioned how the industry needs stronger security systems. The CEO of Panther Protocol stated that the hack was caused by poor management rather than a flaw in centralized exchanges. He added that it was shocking that Bybit was not using air-thigh security systems. Moreover, he said it is inexcusable for the safe wallet to allow for such security vulnerability. How Digital Assets Can Be Made Safer In the future, Regulators may impose stricter rules on exchanges and decentralized platforms to prevent hacker exploitation. AI-driven security systems and stronger KYC/AML enforcement could become standard to counter cyber threats. Advancements in quantum computing and zero-knowledge proofs might transform security measures, making asset laundering harder for hackers. Authorities and developers will likely collaborate to close loopholes and enhance protection against cyberattacks. The future of digital asset security depends on proactive innovation and global cooperation. The post Bybit Hack: Lazarus Group Launders $138 Million as Crypto Security Fears Grow appeared first on Coinfomania.

Price forecasting is a crucial aspect of cryptocurrency trading, and various models have been developed to predict the future price of digital assets like $AB. In this article, we will explore some common price forecasting models for $AB, analyzing their strengths, weaknesses, and applications. *1. Autoregressive Integrated Moving Average (ARIMA) Model* The ARIMA model is a popular statistical approach for forecasting time series data, including cryptocurrency prices. It combines three key components: autoregression (AR), differencing (I), and moving average (MA). - Strengths: ARIMA models are effective in capturing trends and patterns in historical data. - Weaknesses: They can be sensitive to parameter selection and may not perform well with non-stationary data. - Application: ARIMA models can be used to forecast short-term price movements in $AB. *2. Exponential Smoothing (ES) Model* Exponential smoothing models are a family of methods that weight historical data to forecast future values. They are commonly used in finance and economics. - Strengths: ES models are simple to implement and can handle trends and seasonality. - Weaknesses: They can be sensitive to parameter selection and may not perform well with non-linear data. - Application: ES models can be used to forecast short-term to medium-term price movements in $AB. *3. Long Short-Term Memory (LSTM) Networks* LSTM networks are a type of recurrent neural network (RNN) designed to handle sequential data. They are widely used in cryptocurrency price forecasting. - Strengths: LSTM networks can learn complex patterns in data and handle non-linear relationships. - Weaknesses: They can be computationally intensive and require large amounts of training data. - Application: LSTM networks can be used to forecast medium-term to long-term price movements in $AB. *4. Vector Autoregression (VAR) Model* VAR models are a multivariate extension of AR models, used to forecast multiple time series simultaneously. - Strengths: VAR models can capture relationships between multiple variables and handle exogenous factors. - Weaknesses: They can be sensitive to parameter selection and may not perform well with non-stationary data. - Application: VAR models can be used to forecast the impact of external factors on $AB prices. *5. Prophet Model* Prophet is an open-source software for forecasting time series data. It is based on a generalized additive model and can handle multiple seasonality and non-linear trends. - Strengths: Prophet is easy to use and can handle complex data patterns. - Weaknesses: It can be less accurate than other models for very long-term forecasts. - Application: Prophet can be used to forecast short-term to medium-term price movements in $AB. *Conclusion* Price forecasting for $AB is a complex task that requires careful consideration of various models and techniques. Each model has its strengths and weaknesses, and the choice of model depends on the specific forecasting goal and data characteristics. By combining multiple models and techniques, traders and investors can develop a more comprehensive understanding of $AB price movements and make more informed decisions. *References* - Box, G. E., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: Forecasting and control. John Wiley & Sons. - Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: Principles and practice. OTexts. - Taylor, S. J., & Lysy, G. (2017). Financial forecasting with Prophet. Journal of Business Forecasting, 36(2), 12-18.

Here are some common price forecasting models that can be used to predict the future price of $AB : 1. Autoregressive Integrated Moving Average (ARIMA) Model - *Definition:* ARIMA is a statistical model that forecasts future values based on past patterns. - *How it works:* ARIMA uses a combination of autoregressive (AR) terms, integrated (I) terms, and moving average (MA) terms to forecast future values. 2. Exponential Smoothing (ES) Model - *Definition:* ES is a family of methods for forecasting future values based on weighted averages of past observations. - *How it works:* ES uses a weighted average of past observations, with more recent observations given more weight. 3. Moving Average (MA) Model - *Definition:* MA is a statistical model that forecasts future values based on the average of past values. - *How it works:* MA uses a simple average of past values to forecast future values. 4. Linear Regression (LR) Model - *Definition:* LR is a statistical model that forecasts future values based on the relationship between a dependent variable and one or more independent variables. - *How it works:* LR uses a linear equation to forecast future values based on the relationship between the dependent variable and independent variables. 5. Prophet Model - *Definition:* Prophet is an open-source software for forecasting time series data. - *How it works:* Prophet uses a combination of linear regression and seasonality to forecast future values. 6. LSTM (Long Short-Term Memory) Model - *Definition:* LSTM is a type of recurrent neural network (RNN) that is well-suited for forecasting time series data. - *How it works:* LSTM uses a network of interconnected nodes to learn patterns in time series data and forecast future values. 7. Vector Autoregression (VAR) Model - *Definition:* VAR is a statistical model that forecasts future values based on the relationships between multiple time series. - *How it works:* VAR uses a system of linear equations to forecast future values based on the relationships between multiple time series. These models can be used individually or in combination to forecast the future price of $AB. However, it's essential to note that no single model can predict the future with certainty, and it's always a good idea to use multiple models and consider multiple scenarios when making investment decisions.