Precio de Octavia AIVIA

Cango’s (NYSE: CANG) cash deal includes an initial payment of $210.64 million upon closing, with the remaining $141.3 million contingent on Cango fulfilling tax obligations and reducing credit risk exposure linked to sold entities. The transaction, approved by Cango’s board and a special committee, responds to a March 14 proposal from Enduring Wealth Capital Limited (EWCL) to acquire control of the company and divest its PRC business. Closing conditions require shareholder approval and completion of an internal restructuring to separate Cango’s China operations—including automotive trading—from its international bitcoin mining and automotive businesses. If finalized, Cango will petition the China Securities Regulatory Commission (CSRC) to terminate its “China Concept Stock” status, subject to a reversal clause if the status remains unchanged within three months or if EWCL’s proposed secondary acquisition of 10 million Class B shares from co-founders fails. On paper, Cango’s financial health remains strong, with a $415 million market cap, a current ratio of 1.88, and gross profit margins of 55%. Its stock has surged 195% over the past year, trading at a P/E ratio of 11.89. The company also renegotiated terms with Golden Techgen Limited for its bitcoin mining machine acquisition, initially settled via share issuance, to avoid defaults post-divestiture. Recent developments include a 12% monthly increase in bitcoin production to 530.1 coins in March 2025, a deadline extension for closing its mining assets acquisition, and inclusion in the Bitwise Bitcoin Standard Corporations ETF. A $30 million share buyback program further shows efforts to boost shareholder value. The deal highlights Cango’s strategic pivot from its legacy automotive operations to capitalize on cryptocurrency demand. 免责声明：本文章仅代表作者个人观点，不代表本平台的立场和观点。本文章仅供信息分享，不构成对任何人的任何投资建议。用户与作者之间的任何争议，与本平台无关。如网页中刊载的文章或图片涉及侵权，请提供相关的权利证明和身份证明发送邮件到support@aicoin.com，本平台相关工作人员将会进行核查。

A whirlwind of speculation hit the crypto and political spheres as reports emerged that Elon Musk had stepped down from his role in the Trump administration. However, these rumors were swiftly debunked—by Musk himself. Taking to his X account , Musk retweeted a post clarifying that he has not resigned and will only depart “once his job is finished.” He further stated that he is “just getting started” and uncovering significant government inefficiencies. Musk's influence on the crypto market is undeniable, and even false reports about his decisions can create volatility. Following the initial wave of misinformation, Dogecoin ( DOGE ) and other Musk-associated cryptocurrencies saw temporary fluctuations. However, as the truth emerged, markets stabilized. This incident highlights how quickly misinformation can spread and influence crypto sentiment. It also reinforces the importance of verifying news, especially when it involves figures like Musk, whose words and actions can shift markets in an instant. Dogecoin has been on a downward trend so far, but that's because the entire crypto market is currently bearish. Since mid-March however, Dogecoin saw stability around the price of $0.15 and $0.17. DOGE/USDT 1-day chart - TradingView via Bitget From a technical perspective, the fast and slow MAs seem to have crossed, indicating a temporary pause in the Dogecoin price down, and a potential upward movement. As Musk continues his role in the administration, his involvement in uncovering government inefficiencies could have far-reaching implications—not just politically, but also for the broader tech and financial sectors, including cryptocurrency . With his commitment to innovation and disruption, the crypto community will be closely watching what he does next. For now, one thing is clear: Musk is here to stay, and his impact on both politics and digital assets is far from over. A whirlwind of speculation hit the crypto and political spheres as reports emerged that Elon Musk had stepped down from his role in the Trump administration. However, these rumors were swiftly debunked—by Musk himself. Taking to his X account , Musk retweeted a post clarifying that he has not resigned and will only depart “once his job is finished.” He further stated that he is “just getting started” and uncovering significant government inefficiencies. Musk's influence on the crypto market is undeniable, and even false reports about his decisions can create volatility. Following the initial wave of misinformation, Dogecoin ( DOGE ) and other Musk-associated cryptocurrencies saw temporary fluctuations. However, as the truth emerged, markets stabilized. This incident highlights how quickly misinformation can spread and influence crypto sentiment. It also reinforces the importance of verifying news, especially when it involves figures like Musk, whose words and actions can shift markets in an instant. Dogecoin has been on a downward trend so far, but that's because the entire crypto market is currently bearish. Since mid-March however, Dogecoin saw stability around the price of $0.15 and $0.17. DOGE/USDT 1-day chart - TradingView via Bitget From a technical perspective, the fast and slow MAs seem to have crossed, indicating a temporary pause in the Dogecoin price down, and a potential upward movement. As Musk continues his role in the administration, his involvement in uncovering government inefficiencies could have far-reaching implications—not just politically, but also for the broader tech and financial sectors, including cryptocurrency . With his commitment to innovation and disruption, the crypto community will be closely watching what he does next. For now, one thing is clear: Musk is here to stay, and his impact on both politics and digital assets is far from over.

AI has reshaped a lot of industries since it appeared and it’s continuing to do so. The financial market is one of them, which particularly saw a big change with the introduction of AI-powered trading bots. These bots leverage machine learning, deep learning, and predictive analytics to identify trading opportunities and execute trades at blazing speed (one could say it’s even ludicrous speed). Unlike traditional algorithmic trading, AI-based systems continuously learn from new data and adapt to changing market conditions, making them powerful tools for traders. However, using AI for market prediction faces challenges and limitations. Predicting price movements with certainty remains difficult due to the inherent complexity of financial markets, external economic influences, and sudden, unpredictable events (which, considering human nature, is quite often). Let’s just say, the technology just isn’t quite there yet, or rather, people haven’t figured out all the kinks and nuances. As one might have gathered by now, predicting financial markets is far from straightforward, probably even more so today with the crypto industry in the mix. Multiple hurdles limit the effectiveness of AI-powered trading systems, starting with inherent complexity. Financial markets are complicated by nature and are influenced by a combination of several elements, that is, macroeconomic factors, geopolitical events, investor psychology, market sentiment, high-frequency trading, and institutional manipulation. A key issue is the lack of structured rules; markets lack fixed patterns and are often swayed by unforeseeable events. Artificial intelligence struggles to account for unexpected shifts, like regulatory crackdowns or economic crises, making accurate predictions challenging. The next set of challenges are data limitations and bias. AI models require vast amounts of high-quality data for precise predictions. Sounds simple enough, but the problem is that financial data often contains biases, missing information, or manipulated data that can mislead models. To give you an example, an AI model trained only on bull market data might perform poorly during a sudden market downturn because it has never encountered such conditions before. Similarly, historical data may not always reflect current market realities due to evolving economic policies and investor behaviors. Then, there are overfitting and model risks. At first glance, this doesn’t sound like an issue, but overfitting is a common problem in AI trading. It refers to a situation when an AI model performs exceptionally well on historical data but fails in live trading. Overfitting occurs when models memorize past trends rather than recognizing generalizable patterns. On top of that, large institutional traders actively adapt their strategies to counteract AI-driven retail trading, further diminishing the reliability of predictive models. Despite the challenges above, AI trading bots can still be useful as they use various techniques to generate market predictions. To name a few: Core AI components like supervised learning, reinforcement learning, and neural networks allow AI to learn from labeled past trading data for future predictions. Through a combination of these, AI learns from labeled past trading data and applies it to future predictions, all the while it continuously improves upon strategies via feedback from simulated trading. In addition, deep learning techniques recognize price patterns, helping AI detect trends. In summary, these models analyze historical price movements, trading volume, and volatility to forecast potential price actions. The name perhaps sounds complicated, but it basically involves AI bots scanning news articles, financial reports, and social media to assess market sentiment. Then, by analyzing text data, NLP models gauge investor outlook (bullish or bearish). For instance, an out-of-the-blue increase in positive sentiment about Bitcoin on social media might indicate an impending price surge. On the other hand, panic-driven discussions may signal a market downturn. NLP understands the context of these conversations, analyzing word relationships between words in a sentence across paragraphs to get the meaning. This is more technical in nature and is a bit more complicated as AI-powered trading bots rely on a bunch of technical indicators. These include moving averages (MA, EMA), relative strength index (RSI), moving average convergence divergence (MACD), Bollinger Bands, and liquidity analysis. If you’re not familiar with the terms, you’ve likely read a bunch of gibberish now. Put simply, these signals help AI determine potential entry and exit points for trades by: Last but not least, AI bots use and analyze alternative data sources to speculate. This could be blockchain data with on-chain transactions, whale movements, and DeFi activity for crypto markets. Also, it employs options market data where open interest and trading volumes help predict investor sentiment. Moreover, AI even uses Google, specifically Google Trends and web traffic data. It can look for spikes in searches for specific cryptocurrencies or stocks that may indicate upcoming market movements. It’s worth remembering that AI indeed is a powerful tool, but it’s not foolproof since it has its limitations. Impressive and at times unbelievable, it isn’t magical or a crystal ball where you can see your future. Who knows, that might be true in the next few years, but it certainly isn’t true today, as many people overestimate AI’s ability to predict price movements with absolute certainty (which is wrong on many levels). To help you avoid making these mistakes putting all your hopes in AI, it’s best to remember several things, such as: Keep in mind that AI can offer you an edge, but can’t guarantee you a profit. Though AI currently struggles with predictive certainty, there are likely several advancements coming in the future. Some are speculation, some more grounded, but sooner or later, at least a few improvements are bound to happen. We may get more advanced deep learning models, which would make AI models better at adapting to unexpected market conditions, thus improving predictive accuracy. Potentially, with the rise of decentralized finance (DeFi), AI trading bots could integrate directly into smart contracts, enabling autonomous trading without intermediaries. In addition, with the increasing regulatory issues regarding AI and ethical concerns over its impact on retail traders (or in general), we may also get new laws governing AI trading. Whatever happens in the years to come, it’s a fact that AI-powered trading bots have transformed financial markets by making trading faster, more efficient, and data-driven. The technology isn’t know-it-all, and it works best alongside human expertise, fundamental analysis, and strong risk management. As AI evolves, traders should stay informed, adhere to strategies, and above all, set realistic expectations about AI’s capabilities. The future of AI in trading is promising, but it remains a tool that requires careful application with oversight. Disclaimer: The information presented in this article is for informational and educational purposes only. The article does not constitute financial advice or advice of any kind. Coin Edition is not responsible for any losses incurred as a result of the utilization of content, products, or services mentioned. Readers are advised to exercise caution before taking any action related to the company.

Arthur Hayes: Bitcoin should hold $76.5K until tax day to signal stability Arthur Hayes, co-founder of the BitMEX crypto exchange, noted via X that the market reacted negatively to April 2 "Liberation Day" tariffs. He said that if Bitcoin $BTC stays above $76,500 until U.S. tax

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