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My fav thing to do is look at the posts (usually panic or euphoria posts) around news and fundamentals where there’s zero edge around but people try to sound smart around it and write down a 1000 word essay of why something is gonna happen but then the opposite happens

My fav thing to do is look at the posts (usually panic or euphoria posts) around news and fundamentals where there’s zero edge around but people try to sound smart around it and write down a 1000 word essay of why something is gonna happen but then the opposite happens

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Digital asset manager Grayscale, overseeing nearly $16 billion ($15.95B) in assets, released insights identifying 20 crypto assets to watch for the second quarter of 2025, reportedly based on various internal metrics. The insight highlights established, large-cap tokens such as Bitcoin and Ethereum alongside others like SUI. It also recognizes emerging projects within growing sectors like Decentralized Finance (DeFi), Artificial Intelligence (AI), and Decentralized Physical Infrastructure Networks (DePIN). Notably, the Grayscale list highlights Bitcoin as the dominant asset, with a market cap of $1.66 trillion and a low volatility of 52.8%. Ethereum follows with a market cap of $238 billion and a higher volatility of 77.4%. It continues to lead in decentralized applications (dApps) and smart contracts. Another token mentioned by Grayscale in this top category included Optimism (OP). Joining the ranks of the top assets are Solana, Chainlink, and SUI. Solana, with a market cap of $65.3 billion, stands out for its scalability and low transaction costs despite having a higher volatility of 112.2%. Similarly, SUI has a market cap of $7.2 billion and a volatility of 115%. Chainlink, a key player in the DeFi and tokenization sectors, boasts a market cap of $8.9 billion and a volatility of 105.8%. As a decentralized oracle network, Chainlink is critical in ensuring secure and reliable data transfers for smart contracts. Grayscale’s insight also points to the growing importance of AI and DeFi projects. Bittensor (TAO) and Story Protocol (IP) are leading innovations in AI. Story Protocol is showcasing an extreme volatility of 417.3%. Related: Crypto Market Divided Ahead of April 2 “Liberation Day”: BTC Waits, Alts Run In the DeFi space, projects like Hyperliquid (HYPE), Uniswap (UNI), and Aave (AAVE) continue to hold strong market positions, highlighting the demand for decentralized financial services that empower users to transact and earn independently of traditional financial institutions. The Grayscale report also notes the increasing relevance of stablecoins and Ethereum scaling solutions. Ethena (ENA), with a market cap of $1.9 billion, is focused on stablecoins and has a volatility of 148.8%. On the Ethereum L2 side, Optimism (OP) is highlighted again as an emerging leader among scaling solutions. It aims to improve transaction efficiency and reduce costs for dApps built on the main Ethereum network. DePIN (Decentralized Physical Infrastructure Networks) is another area of growth, with projects like Helium (HNT) and Geodnet (GEO) leading the way. These projects combine physical infrastructure with decentralized networks. Related: Ethereum April 2025 Forecast: Analyzing the Bull vs Bear Case After Dip Separately, other AI-focused projects like Grass (GRASS) and Virtuals Protocol (VIRTUAL) are also on Grayscale’s Q2 watchlist. These projects are gaining attention for building foundational ecosystems that integrate AI into the blockchain. The asset manager stresses the fundamental importance of conducting thorough independent research (DYOR) before making any investment decisions based on their watchlist or other analyses. While the identified tokens show promise according to Grayscale’s selected metrics, the inherent volatility common across the entire cryptocurrency market always requires investor caution and careful risk management. 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.

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.