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$AI Holding Support?I’ve taken a small position in NYSE:AI here as it seems to have put in a support level. In addition, the selling volume has declined quite a bit suggesting to me that sellers are pretty much done. If we have a pocket pivot in the next few days / weeks, I will bring it up to a full position size. I am using the support area as my stop guideline. If it fails, then it proves that was not a supported area. For my trading style I like the risk reward ratio here. Thanks for looking. Ideas, not investment / trading advice.
NYSE:AILong
by jaxdog
3
HOW WILL AI AFFECT FINANCIAL MARKETS?Artificial Intelligence (AI) is revolutionizing the financial markets, with its algorithms and automated systems allowing for faster and more accurate trading decisions. AI technology has already seen success in stock market trading, but it is now being used to analyze data from all areas of finance, including banking and investments. In this article, we will explore the advantages and challenges posed by AI-based trading systems, as well as potential opportunities for AI in the future of financial markets. Finally, we will provide guidance on how to prepare for the impact of AI on financial markets. 1. Understanding AI and its Impact on the Financial Market Artificial Intelligence (AI) is an advanced technology that has been used in a variety of industries to automate tasks and make decisions. In the financial markets, AI can be used to analyze large amounts of data quickly and accurately. It can recognize patterns, identify trends, and even predict outcomes in order to generate trading signals for investors. The potential implications of AI in the financial markets are vast. AI-based systems can be used to streamline trading processes, reduce risk, and increase profitability. However, there are also drawbacks associated with using AI in finance that must be considered. For example, AI systems may lack the human intuition needed to make sound decisions during volatile market conditions or when dealing with complex security types. AI-based systems have already demonstrated their ability to recognize certain trends and patterns in financial data. For instance, AI has been used successfully by traders to detect price movements before they occur and capitalize on them accordingly. Similarly, these systems can also identify correlations between different asset classes or sectors over time, allowing investors to diversify their portfolios more efficiently. Finally, there are a number of examples of successful applications of AI in finance already taking place around the world. Hedge funds have adopted machine learning algorithms for portfolio optimization; banks have leveraged natural language processing (NLP) technologies for customer service; and stock exchanges have implemented automated surveillance solutions for fraud detection. All of these examples demonstrate how powerful AI can be when it comes to making decisions within the financial markets. 2. Advantages of AI in Trading AI has the potential to revolutionize how trading is conducted in financial markets. By leveraging the power of AI, traders can gain an edge in the markets and improve their chances of success. Here are some of the main advantages of using AI in trading: 1. Quick and Accurate Analysis: AI-based systems are capable of quickly analyzing large amounts of data and providing accurate market insights. This helps traders make faster, more informed decisions about when to buy or sell a particular asset. It also reduces the risk associated with manual analysis, as there is less chance for human error to enter into decision making processes. 2. Identifying Profitable Opportunities: AI-based systems are able to identify profitable opportunities that may otherwise be overlooked by manual analysis. This allows traders to capitalize on positive trends and maximize returns from their investments. 3. Identifying Risks: AI-based systems can also help identify risks associated with certain trades or investments, allowing traders to mitigate these risks before acting on them. This helps reduce losses and improves overall profitability for investors and traders alike. 4. Automated Decision Making: AI-based systems can automate certain aspects of trading decisions, eliminating the need for manual input or assistance from a human trader/investor. This reduces errors associated with manual decision making processes, while increasing efficiency and accuracy when it comes time to execute trades or invest in assets. 5. Lower Overall Costs: Finally, using an AI-based system helps reduce overall costs associated with trading due to its ability to automate certain processes and eliminate errors associated with manual decision making processes. This can help improve profitability for investors/traders over time by reducing expenses related to trading activities such as commissions, fees, etcetera 3. Future Opportunities for AI in Financial Markets The potential of Artificial Intelligence (AI) in the financial markets is immense. It has the power to revolutionize how traders and investors make decisions, identify new opportunities, and reduce risk. AI-based systems are able to automate processes and improve accuracy in decision making - providing a competitive advantage to those who utilize it. Additionally, algorithmic trading can give an extra edge by increasing efficiency when predicting market trends and stock prices. Synthetic assets are another way that AI is being employed in the financial sector. These products can provide investors with exposure to investments not typically offered on traditional markets or products. Furthermore, AI helps organizations create effective risk management strategies by recognizing potential risks quickly and offering guidance on how to prevent them from occurring. AI has already been utilized by some of the world's largest banks as a way to gain insight into the complexities of financial markets; giving businesses access to innovative investment strategies and new growth prospects within their organization. As this technology develops further, now is the perfect time for corporate entities to prepare for its impact on their operations so they can take full advantage of its many advantages when they arise. In summary, AI offers a great opportunity for traders and investors alike in terms of achieving higher returns while minimizing losses through improved decision making processes, enhanced analysis effectiveness, and more precise predictions about stock prices and market trends. With its rapid evolution continuing apace, it’s essential for companies operating in the financial industry to start preparing now for what lies ahead so they can capitalize on all that this powerful technology has to offer them in future years! 4. Challenges Faced by AI in Financial Markets AI is a powerful tool for understanding and predicting financial markets, but it does come with certain challenges that must be addressed in order for it to become a viable tool. Below, we will explore the five main challenges facing AI when applied to financial markets. Developing Reliable Algorithms: Developing reliable algorithms is essential for successful AI trading systems. It is important to ensure that investors are not exposed to unnecessary risks due to inaccurate predictions or unreliable models. In order to minimise such risks, developers need to carefully tweak existing AI algorithms and develop new ones that can accurately predict market outcomes. This requires complex mathematical models as well as an in-depth understanding of the data being analyzed. Ensuring System Security: Financial markets involve sensitive information which needs to be kept secure at all times. As such, security should be one of the top priorities for any organization utilizing AI in finance. Strong passwords and authentication protocols should be implemented and regularly tested, while any vulnerabilities should be actively monitored and patched immediately. Additionally, organizations should use encryption techniques such as Secure Socket Layer (SSL) or Transport Layer Security (TLS) whenever possible when transmitting or storing data on their servers or networks. Predicting Ethical Implications: The ethical implications of using AI in finance also need to be considered before integrating these technologies into existing systems and processes. This includes analyzing how decisions made by these systems could affect individuals or groups of people – both positively and negatively – as well as exploring potential legal ramifications of using AI-based trading systems. Organizations must consider these issues carefully before deploying any new technology in their operations and ensure they have the necessary safeguards in place if needed. Responding To Unstructured Data: Another challenge associated with using AI in finance is its ability to handle unstructured data accurately in real-time. Unstructured data can come from sources such as news stories, social media posts, customer feedback surveys etc., all of which can offer valuable insights into current market trends and conditions that may not otherwise be apparent from structured numerical data alone. As such, developing algorithms which can effectively interpret this type of data is an important area of research for financial institutions looking to utilize the power of AI in their operations. Exploring Long-Term Implications: Finally, organizations must consider the long-term implications of utilizing AI technologies when making decisions related to their financial operations. This includes considering whether there will be any unintended consequences associated with relying too heavily on automated decision making processes; whether there are sufficient safeguards against manipulation by malicious actors; and whether there are strategies in place which enable companies to remain competitive over time without sacrificing customer privacy or other ethical considerations.. Ultimately, organizations need to think carefully about how they integrate AI into their existing infrastructure before taking action so they can make informed decisions about how best utilize this technology going forward 5. How to Prepare for the Impact of AI on Financial Markets As AI continues to gain prominence in financial markets, companies must be proactive in understanding the risks and benefits of incorporating it into their trading strategies. To get ready for the impact of AI on financial markets, a strategic approach is necessary that includes comprehending how regulatory bodies interact with this technology, identifying potential partners who can help navigate its complexities, and remaining aware of advancements with AI. Here are several tips to prepare: 1. Assess Risks & Benefits: Investigate current trends in AI to detect both possibilities and drawbacks. Additionally, familiarize yourself with rules or laws related to using AI in finance industries so you can ensure following regulations while still gaining from its benefits. 2. Design Strategies: Develop tactics that maximize advantages while minimizing risks. This may include automating processes or creating algorithms that enable you to recognize opportunities quickly and make wise decisions faster than before. Consider partnering up with experts who understand integrating AI into existing infrastructure and procedures. 3. Stay Updated: Companies running finance businesses must be cognizant of new technologies like artificial intelligence so they remain competitive without compromising customer privacy or other ethical standards--this entails subscribing to industry news sources, attending conferences such as FinTech Connect Live!, reading industry blogs such as FintechToday or TechCrunch’s Fintech section among other options! 4. Analyze Regulatory Bodies: Organizations operating within the finance sector should have an idea on how regulatory bodies view machine learning applications when it comes to making decisions within the organization--this data will help them stay compliant without sacrificing customer confidentiality or other moral considerations by providing guidance on acceptable usage policies or suggesting alternate options if one is disapproved by a certain body plus researching various jurisdictions' regulations depending where services need be offered globally.. 5. Find Partnerships: Experienced partners may be essential when introducing artificial intelligence into your operations--not only they provide technical support but also share advice on merging machine learning applications into existing infrastructure and processes as well as helping produce suitable usage policies meeting all applicable regulation standards across global locations.. Cooperating allows leveraging resources more efficiently plus benefiting from shared experiences thus increasing success chances! By taking these steps, companies operating within financial sectors can benefit from any opportunities presented by artificial intelligence while avoiding associated risks—ensuring their compliance is met without endangering customer confidentiality or other ethical issues along the way! Traders, if you liked this idea or if you have your own opinion about it, write in the comments. I will be glad 👩‍💻
SP:SPXEducation
by Lingrid
14
CyberAggent At the Supply Line of an Ascending Broadening WedgeCyberAgent currently sits at the Supply Line of a long established Ascending Broadening Wedge and while my first instinct would usually be to short, I think this one is showing signs that it will give us a strong bullish reaction off of this supply line as we have a bunch of Bullish Divergence on the MACD and RSI and are at the 55 Moving Average on the 2 Month while trading in a very tight more local falling wedge pattern. Upon Breakout i think we could go up to make a 0.886 Retrace before ultimately confirming a Partial Decline and Breaking Down. But in the meantime im very bullish here.
TSE:4751Long
by RizeSenpai
AI, Reversal to the UPSIDE in order..AI is consolidating at 20-22 price levels -- a strong order block support which has been tested quite a few times already. Net buying / accumulation has been spotted at the current levels forming 4H higher lows. Expect a reversal to the upside from this price point. Spotted at 22.0 TAYOR. ----------------- LAST QTR results Jan 2023 for reference: Jan 2023EPS beat by 72.57% EPS (USD) Expected -0.22 Reported -0.06 Surprise 72.57% Revenue (USD) Expected 64.25M Reported 66.67M Surprise 3.77%
NYSE:AILong
by JSAL
Market Sentiment and Trend Prediction System. Predictive Model. The codes listed below (free&easy;), detailed steps to follow for developing the event prediction system: 1. **Collecting Data**: we will need to gather data from various sources. We can use Python-based web scraping libraries like Beautiful Soup and Scrapy to extract data from news websites and social media platforms (scraping exports data from websites, it is safe and legal, but better contact website admins and ask for authorization) 2. **Cleaning and Preprocessing Data**: After collecting the data, we need to clean and preprocess it. We can use Python libraries like Pandas and NumPy to remove duplicates, missing values, and irrelevant information. 3. **Natural Language Processing**: Once the data is cleaned, we can use natural language processing (NLP) techniques to extract insights from the text data. For example, we can use the NLTK library to perform tokenization, stemming, and lemmatization on the text data. 4. **Model Building**: We can use machine learning algorithms like Random Forest, Gradient Boosted Trees, or Support Vector Machines (SVMs) to build predictive models. These models can help us predict the occurrence of an event or the sentiment associated with a specific topic. 5. **Dashboard and Visualization**: Finally, we can create an intuitive dashboard using tools like Tableau or Power BI to display the analyzed data in real-time. We can use interactive visualizations like bar graphs, pie charts, and heat maps to provide users with a clear understanding of the events and their impacts. 6. **Testing and Deployment**: Once the system is developed, we need to test it thoroughly to ensure that it is delivering the expected results. We can use various testing frameworks like pytest, unittest, or nosetests to automate the testing process. Once testing is completed, we can deploy the system to the production environment. 7. **Regular Maintenance and Updates**: We also need to ensure that the system is continuously monitored. The codes : Termux (the app is in playstore, github etc, to excute python files, or commands,for every step, some general commands and libraries that you might find useful: 1. Collecting Data: - To install Scrapy, run `pip install scrapy`. - To install Beautiful Soup, run `pip install beautifulsoup4`. - To scrape data from a webpage using Scrapy, run `scrapy crawl `. - To scrape data from a webpage using Beautiful Soup, use Python's built-in `urllib` or `requests` module to fetch the webpage's HTML. Then, use Beautiful Soup to parse the HTML and extract the relevant data. 2. Cleaning and Preprocessing Data: - To install Pandas, run `pip install pandas`. - To install NumPy, run `pip install numpy`. - To remove duplicates, use Pandas' `drop_duplicates()` function. - To remove missing values, use Pandas' `dropna()` function. - To filter out irrelevant data, use Pandas' indexing functions like `loc` and `iloc`. 3. Natural Language Processing: - To install NLTK, run `pip install nltk`. - To perform tokenization, run `nltk.tokenize.word_tokenize(text)`. - To perform stemming, run `nltk.stem.PorterStemmer().stem(word)`. - To perform lemmatization, run `nltk.stem.WordNetLemmatizer().lemmatize(word)`. 4. Model Building: - To install scikit-learn, run `pip install scikit-learn`. - To instantiate a Random Forest classifier, run `from sklearn.ensemble import RandomForestClassifier; clf = RandomForestClassifier()`. - To fit the model to the data, run `clf.fit(X_train, y_train)`, where `X_train` is the input data and `y_train` is the output labels. - To use the model to make predictions, run `clf.predict(X_test)`. 5. Dashboard and Visualization: - To install Tableau, follow the instructions on their website. - To install Power BI, follow the instructions on their website. - To create a bar graph in Python, use the `matplotlib` library: `import matplotlib.pyplot as plt; plt.bar(x, y); plt.show()`. - To create a pie chart in Python, use `plt.pie(values, labels=labels); plt.show()`. - To create a heat map in Python, use `sns.heatmap(data, cmap='coolwarm'); plt.show()` (assuming you have installed the Seaborn library). These are general commands and libraries that you can use as a starting point. If you need me to explain how to use termux, let me know.
Education
by Moroccolover
3
IMGNAI to the moon?RISK/DISCLAIMER As you are aware, many tokens have rug pulled in the past. This token may even rug pull, but I don't think it will. The developers have not been doxed and remain anonymous. Additionally, I have not been paid in any way to promote this token. Remember to always do your own due diligence. Consumer Level AI NSFW Content is Here! The new trend in cryptocurrency is AI, and we are just at the beginning. IMGNAI has already developed a functioning product that has produced phenomenal results. I encourage you, the reader, to try it out on their Discord server (link available on their website). Currently, it is relatively illiquid due to limited listings. However, there is a Huobi listing in the works according to on-chain activity, and it is likely that other high-tier exchanges will follow, given the quality of the product. It can generate many things including NSFW content, your imagination is the limit literally speaking. Currently, there are eight models available that can be prompted to generate content (as listed on their Gitbook) /nai - Our base model, /nai, is trained on the most popular anime art styles. This model is optimized for fun and flexibility so you can input detailed or basic prompts and generate a near-infinite range of beautiful, anime-inspired art. /real - Our /real model lets you create photo-quality realism with incredible prompt responsiveness. From photo-realistic portraits to believable animal hybrids, make your imagination a reality with our /real model. /hyper - /hyper was built to bridge the gap between digital renders and photo-realism. From detailed 3D renders to lifelike realism, hyper has all the rizz. /cin - Our cinematic model is trained on rich textures and cinematic lighting. This model is among the first in the world to produce perfect dark, and is capable of stark lighting contrasts. We recommend selecting “wide” image dimensions with /cin for best results. /art - Our /art model is designed to produce detailed images with a high degree of artistic flair. Include your favorite artist or art style in the prompt for best results. /ani - A community favorite, /ani is trained on classic anime and produces lush, detailed anime images. Create anime art or bring the waifu of your dreams to life with our /ani model. /cgi - Derived from our realism training set, /cgi produces highly detailed digital renders with a high degree of prompt responsiveness. /fur - Fur is trained on classic anime art styles with a furry twist. It can produce anthropomorphic animals, beautiful fursonas, and a range of fictional characters. Be aware, this model can be quite nsfw. At the time of this post, only /nai, /ani, and /fur have NSFW content generation. /real is coming soon to beta access. It's going to disrupt the adult content industry, imo. Image Generation AI (IMGNAI) is potentially in process of repeating the same type of pattern that happened earlier. Kaspa actually just did something similar as well, infact it overshot. I believe IMGNAI has similar potential. Should be noted that all altcoins follow a form of the BTC pattern depending on their respective supply ratios. For example BTC 1M chart next to 1D chart of KASPA or BTC 1M chart next to 1D chart of IMGNAI or BTC 1M chart next 2W chart of LTC (LTC had already caught up, soo BTC had to get ahead for it to catch up again, soon that will happen for the CBDC bullrun) It's all programmed, and the longer BTC exists the harder newer coins with little price history pump; because they have more distance to catch up. Some background (from their website): ImgnAI is a crypto-native team with one goal: to build a new leader in consumer AI. We aim to push the boundaries of what’s possible and create products that allow users to have fun while exploring the full range of their imagination. Our flagship product, Nai, is an AI-powered text-to-image bot that’s currently compatible with Telegram and Discord. We offer seamless UI and 8+ custom image models with ever-increasing quality. Nai allows users to bring their imagination to life regardless of artistic ability. While we’re initially focused on image generation, ImgnAI is built to be so much more. In the coming months and years, we’ll drastically expand our image generation tooling and launch new, innovative product verticals in the realm of consumer AI. Our roadmap includes products and key partnerships for crypto-natives and no-coiners alike. But what does the $IMGNAI token do (from their website): $IMGNAI TOKEN $imgnAI is our primary utility token and plays a key role in our growing ecosystem. Soon, the token will be used to unlock premium features (HD/4K resolution, upscaling, NSFW models, etc) and mint user generations directly as NFTs. Spent tokens across Premium and NFT tooling will be burned, thus deflating the total supply of $imgnAI. The token will also be given a range of important utilities as we build out our ecosystem and launch new product verticals in the field of consumer AI. In addition to ecosystem utility, we aim to decentralize governance of imgnAI via the $imgnAI token. Decentralization can be accomplished by providing governance and voting power to $imgnAI stakers as part of the imgnAI DAO operating policies. We look forward to rolling this out and moving imgnAI toward true decentralization in the future. At this time, we also offer access to unreleased beta models to those holding at least 100k $imgnAI. In this way, our stakeholders contribute significantly to the development of new image models and future products. Summary: I think it's undervalued at it's present marketcap, and with liquidity from the future listing, and all the updates; and due to it being a solid product; it should be valued at least at 500M - 1.2B+; the pattern says something similar. Price "can" 50x-100x+ or so from here. :O I think it's justifiable to grab a suicide stack of 100,000 IMGNAI. Website: imgnai.com Market Cap: $15,471,757 Fully Diluted Valuation: 19,916,443 Circulating Supply: 776,833,333 Total Supply: 1,000,000,000 Max Supply: 1,000,000,000
Long
by i_am_noone
2
$INJ/USDT 1D (#Bybit) Rising wedge on resistanceInjective Protocol is entering overbought territory and could retrace down to 100EMA support, after a last push up into supply zone. ⚡️⚡️ #INJ/USDT ⚡️⚡️ Exchanges: Binance Futures, ByBit USDT Signal Type: Regular (Short) Leverage: Isolated (2.2X) Amount: 4.9% Current Price: 7.0720 Entry Zone: 7.1290 - 8.0450 Take-Profit Targets: 1) 5.8905 2) 4.6130 3) 3.3355 Stop Targets: 1) 9.0065 Published By: @Zblaba CRYPTOCAP:INJ #INJUSDT #Injective #DeFi #Web3 injective.com Risk/Reward= 1:1.2 | 1:2.1 | 1:3.0 Expected Profit= +49.2% | +86.2% | +123.3% Possible Loss= -41.2% Estimated Gaintime= 1-2 months
Short
by TradingOn
3
How can AI help to improve algorithmic trading strategies?AI is transforming the field of algorithmic trading, which involves using computer programs to execute trades based on predefined rules and strategies. AI can help to improve algorithmic trading performance and efficiency by providing advanced data analysis, predictive modeling, and optimization techniques. In this article, we will explore some of the ways that AI can enhance algorithmic trading and some of the challenges and opportunities that lie ahead. One of the main advantages of AI in algorithmic trading is its ability to process and interpret large and complex data sets in real-time. AI algorithms can leverage various sources of data, such as market prices, volumes, news, social media, sentiment, and historical trends, to identify patterns, correlations, and anomalies that may indicate trading opportunities. AI can also use natural language processing (NLP) and computer vision to extract relevant information from unstructured data, such as text, images, and videos. Another benefit of AI in algorithmic trading is its ability to learn from data and adapt to changing market conditions. AI algorithms can use machine learning (ML) and deep learning (DL) techniques to train on historical and live data and generate predictive models that can forecast future market movements and outcomes. AI can also use reinforcement learning (RL) techniques to learn from its own actions and feedback and optimize its trading strategies over time. A further aspect of AI in algorithmic trading is its ability to optimize trading performance and reduce costs. AI algorithms can use mathematical optimization methods to find the optimal combination of parameters, such as entry and exit points, order size, timing, and risk management, that can maximize profits and minimize losses. AI can also use high-frequency trading (HFT) techniques to execute trades at high speeds and volumes, taking advantage of small price fluctuations and arbitrage opportunities. AI can also help to reduce transaction costs, such as commissions, fees, slippage, and market impact, by using smart order routing and execution algorithms that can find the best available prices and liquidity across multiple venues. However, AI in algorithmic trading also faces some challenges and limitations that need to be addressed. One of the main challenges is the quality and reliability of data. AI algorithms depend on accurate and timely data to perform well, but data sources may be incomplete, inconsistent, noisy, or outdated. Data may also be subject to manipulation or hacking by malicious actors who may try to influence or deceive the algorithms. Therefore, AI algorithms need to have robust data validation, verification, and security mechanisms to ensure data integrity and trustworthiness. Another challenge is the complexity and interpretability of AI algorithms. AI algorithms may use sophisticated and nonlinear models that are difficult to understand and explain. This may pose a problem for traders who need to monitor and control their algorithms and regulators who need to oversee and audit their activities. Moreover, AI algorithms may exhibit unexpected or undesirable behaviors or outcomes that may harm the traders or the market stability. Therefore, AI algorithms need to have transparent and explainable methods that can provide clear and meaningful insights into their logic and decisions. However, there are also ethical and social implications of AI in algorithmic trading. AI algorithms may have an impact on the market efficiency, fairness, and inclusiveness. For example, AI algorithms may create or amplify market inefficiencies or distortions by exploiting information asymmetries or creating feedback loops or cascades. AI algorithms may also create or exacerbate market inequalities or exclusions by favoring certain groups or individuals over others or by creating barriers to entry or access for new or small players. Therefore, AI algorithms need to have ethical and social principles that can ensure their alignment with human values and interests. In conclusion, AI is a powerful tool that can help to improve algorithmic trading strategies and performance by providing advanced data analysis, predictive modeling, and optimization techniques. However, AI also poses some challenges and risks that need to be addressed by ensuring data quality and reliability, algorithm complexity and interpretability, and ethical and social implications. By doing so, AI can create a more efficient, effective, and equitable algorithmic trading environment for all stakeholders.
BITSTAMP:BTCUSDEducation
by JS_TechTrading
AI, 10d+/-77.49%falling cycle -77.49% more than 10 days. ================================================================================================================================================================== This data is analyzed by robots. Analyze historical trends based on The Adam Theory of Markets (20 moving averages/60 moving averages/120 moving averages/240 moving averages) and estimate the trend in the next 10 days. The white line is the robot's expected price, and the upper and lower horizontal line stop loss and stop profit prices have no financial basis. The results are for reference only.
NYSE:AIShort
by Tonyder
AI - Running Away From The NazzyAn AI index, comprised of: Shown in Blue Google Microsoft NVIDIA C3.AI Nasdaq Shown in Orange We see that they have broken away from the returns offered by the Nazzy Tech Index Bottom of the Graph: Spread between the above defined AI index and the Nazzy. Has reached its All Time High.. Traders would have earned an additional 20+% by investing in the AI index in lieu of the Nazzy Mind you, these companies have Zero additional profits resulting from AI, at this time. How to profit from this? Accepting ideas.
NASDAQ:NVDA
by WillNixTrading
INTUSDT technical Analysis From technical analysis wise, a clear breakout from a falling wedge on W & 3D frames, it is retesting, I think it will make 10x from here. According to INTchain twitter account: “The INT Chain Foundation officially announced that it will conduct in-depth research and expansion on AIGC and ChatGPT related technologies, with an ever-changing pace, profoundly, quickly, and thoroughly changing the existing IoT chain model.” That’s BULLISH but you need to keep in mind the following. 1- It’s a small cap project with huge potential but with a low liquidity “ manage your risks” 2- Take profit along the way and enjoy. Good luck
ILong
by BadrAAA
C3.AI - Ready for the next wave upHello everyone, The letter from the Kerrisdale Capital made C3.AI plummet by 40% on possible allegations that C3.AI used agressive accounting tactics to boost it's quarterly results and improve margins on paper. Even if that is true, revenue is still revenue and each and every company uses all the possible legal ways to improve it's finances. Still, this does not immediately mean that C3 should be 40% cheaper than 3 days before. The company was way overbought and needed some cool-down, but -40% is brutal for such allegations. The $20 has been a fantastic support a few times in the past and I think that this time is no different. I am expecting heavy buying and the first TP would be $25 and then $30. After hitting $30, it all depends on the allegations being cleared as well as the overall market sentiment, so nothing can be certain past that point. If there is a heavy break below $18, $15 is next and then $10. Still, I don't think that C3 is over just yet and this is all a show to drop the company. Good luck to all!
NYSE:AILong
by donaitelo