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Understanding The Basics Of AI/Inference Engine ConstructionRecently, there has been a lot of discussion related to my SPY Cycle Patterns and how they work. In short, without disclosing proprietary code/quants, I built an inference engine based on Fibonacci, GANN, and Tesla theories. Part of this inference engine is to identify the highest probable outcome related to the patterns. This is not rocket-science. This is the same process your brain does when determining when and what to trade. The only difference is I'm doing a bunch of proprietary calculations/quants related to data and price theory in the background, then the inference engine determines the best, most likely outcome. Take a few minutes to watch this video and try to understand the difference between static and dynamic modeling. Again, my objective is to help as many traders as possible. My Plan Your Trade videos are my opinions based on my skills, knowledge, and proprietary modeling systems/tools. None of my tools are 100% accurate all the time - nothing is. But, I do believe the quality of information and instructional information I provide is invaluable to most traders. Get some. #trading #research #investing #tradingalgos #tradingsignals #cycles #fibonacci #elliotwave #modelingsystems #stocks #bitcoin #btcusd #cryptos #spy #es #nq #gold
NASDAQ:QQQEducation
by BradMatheny
2
Why Large Language Models Struggle with Financial Analysis.Large language models revolutionized areas where text generation, analysis, and interpretation were applied. They perform fabulously with volumes of textual data by drawing logical and interesting inferences from such data. But it is precisely when these models are tasked with the analysis of numerical, or any other, more-complex mathematical relationships that are inevitable in the world of financial analysis that obvious limitations start to appear. Let's break it down in simpler terms. Problem in Math and Numerical Data Now, imagine a very complicated mathematical formula, with hundreds of variables involved. All ChatGPT would actually do, if you asked it to solve this, is not really a calculation in the truest sense; it would be an educated guess based on the patterns it learned from training. That could be used to predict, for example, after reading through several thousand symbols, that the most probable digit after the equals sign is 4, based on statistical probability, but not because there's a good deal of serious mathematical reason for it. This, in short, is a consequence of the fact indicated above, namely that LLMs are created to predict patterns in a language rather than solve equations or carry out logical reasoning through problems. To put it better, consider the difference between an English major and a math major: the English major can read and understand text very well, but if you hand him a complicated derivative problem, he's likely to make an educated guess and check it with a numerical solver, rather than actually solve it step by step. That is precisely how ChatGPT and similar models tackle a math problem. They just haven't had the underlying training in how to reason through numbers in the way a mathematics major would do. Financial Analysis and Applying It Okay, so why does this matter for financial analysis? Suppose you were engaging in some financial analytics on the performance of a stock based on two major data sets: 1) a corpus of tweets about the company and 2) movements of the stock. ChatGPT would be great at doing some sentiment analysis on tweets. This is able to scan through thousands of tweets and provide a sentiment score, telling if the public opinion about the company is positive, negative, or neutral. Since text understanding is one of the major functionalities of LLMs, it is possible to effectively conduct the latter task. It gets a bit more challenging when you want it to take a decision based on numerical data. For example, you might ask, "Given the above sentiment scores across tweets and additional data on stock prices, should I buy or sell the stock at this point in time?" It's for this that ChatGPT lets you down. Interpreting raw numbers in the form of something like price data or sentiment score correlations just isn't what LLMs were originally built for. In this case, ChatGPT will not be able to judge the estimation of relationship between the sentiment scores and prices. If it guesses, the answer could just be entirely random. Such unreliable prediction would be not only of no help but actually dangerous, given that in financial markets, real monetary decisions might be based on the data decisions. Why Causation and Correlation are Problematic for LLMs More than a math problem, a lot of financial analysis is really trying to figure out which way the correlation runs—between one set of data and another. Say, for example, market sentiment vs. stock prices. But then again, if A and B move together, that does not automatically mean that A causes B to do so because correlation is not causation. Determination of causality requires orders of logical reasoning that LLMs are absolutely incapable of. One recent paper asked whether LLMs can separate causation from correlation. The researchers developed a data set of 400,000 samples and injected known causal relationships to it. They also tested 17 other pre-trained language models, including ChatGPT, on whether it can be told to determine what is cause and what is effect. The results were shocking: the LLMs performed close to random in their ability to infer causation, meaning they often couldn't distinguish mere correlation from true cause-and-effect relationships. Translated back into our example with the stock market, one might see much more clearly why that would be a problem. If sentiment towards a stock is bullish and the price of a stock does go up, LLM simply wouldn't understand what the two things have to do with each other—let alone if it knew a stock was going to continue to go up. The model may as well say "sell the stock" as give a better answer than flipping a coin would provide. Will Fine-Tuning Be the Answer Fine-tuning might be a one-time way out. It will let the model be better at handling such datasets through retraining on the given data. The fine-tuned model for sentiment analysis of textual stock prices should, in fact, be made to pick up the trend between those latter two features. However, there's a catch. While this is also supported by the same research, this capability is refined to support only similar operating data on which the models train. The immediate effect of the model on completely new data, which involves sentiment sources or new market conditions, will always put its performance down. In other words, even fine-tuned models are not generalizable; thus, they can work with data which they have already seen, but they cannot adapt to new or evolving datasets. Plug-ins and External Tools: One Potential Answer Integration of such systems with domain-specific tooling is one way to overcome this weakness. This is quite akin to the way that ChatGPT now integrates Wolfram Alpha for maths problems. Since ChatGPT is incapable of solving a math problem, it sends the problem further to Wolfram Alpha—a system set up and put in place exclusively for complex calculations—and then relays the answer back to them. The exact same approach could be replicated in the case of financial analysis: Once the LLM realizes it's working with numerical data or that it has had to infer causality, then work on the general problem can be outsourced to those prepared models or algorithms that have been developed for those particular tasks. Once these analyses are done, the LLM will be able to synthesize and lastly provide an enhanced recommendation or insight. Such a hybrid approach of combining LLMs with specialized analytical tools holds the key to better performance in financial decision-making contexts. What does that mean for a financial analyst and a trader? Thus, if you plan to use ChatGPT or other LLMs in your financial flow of analysis, such limitations shall not be left unattended. Powerful the models may be for sentiment analysis, news analysis, or any type of textual data analysis, numerical analysis should not be relayed on by such models, nor correlational or causality inference-at least not without additional tools or techniques. If you want to do quantitative analysis using LLMs or trading strategies, be prepared to carry out a lot of fine-tuning and many integrations of third-party tools that will surely be able to process numerical data and more sophisticated logical reasoning. That said, one of the most exciting challenges for the future is perhaps that as research continues to sharpen their capability with numbers, causality, and correlation, the ability to use LLMs robustly within financial analysis may improve.
CRYPTO:BTCUSDEducation
by C_Cosmin_C
My rec who loses too much on trading. Relax and gain knowledge I recommend to stop trading who loses too much. Instead you can run AI trading bot in OKX platform. My referral okx.com 1) Choose Future DCA Martingale 2) Go to AI strategy tab 3) Choose ETHFI/USDT perpetual (with other pairs you should put big amount, but with like ETHFI, TON you can only put around 30 coins) 4) Choose Mid-term moderate bullish (Short-term aggressive bullish, doesn’t work well for me and haven’t tried long-term last one from list. Only use mid-term, that’s should be enough) 5) Don’t touch leverage, leave as is 6) Put amount of sum (I actually put all what I have) 7) But you should check after BTC movement, if BTC in downtrend stop the bot, if it reversal to uptrend then run again AI bot. With other all bots, didn’t get the same result. 8) That is it. Just wait and you can see how AI earns for you. This is better than lost money every day. With this method you can relax. When you will ready and gained trading knowledge, you can return to analysis and to earn more. You can also select SPOT DCA Martingale and same settings. I think this method fits to new in trading system. This is my referral okx.com
Education
by Nakens
1
Trading AutomationI am just going to put it out there, as you know I have said time and time again in my streams. Personally, the whole automated trading concept is not for me. However, that’s not to say there are not some good strategies, tools and instruments that could work for some people. Risk tolerance, time frames, bull vs bear markets all play a role in trading. This is emphasised when the trading is automated. A few weeks back, myself and @Paul_Varcoe starting streaming about shorter timeframes and automation. We said we were working on something in the background – mostly to do with trading via prop firms. Here’s on of my streams on that topic. So, the next part was automation. Here's one of these streams: www.tradingview.com I have been lurking around a couple of services, tools and platforms – one of these is a company/product called 3Commas. A few things I found interesting. One of which is that it supports multiple cryptocurrency exchanges, allowing users to trade on various platforms using a single interface. For the Tradingview community this is a very useful option. You can even go as far as connecting your bot to one or more TradingView indicators of your choice, and the bot will automatically receive alerts and open trades accordingly. My reluctance of automation has always been, if a bot can do it – we won’t need Doctors or Police officers as they will all want to be professional traders. I have also spent some time in the money management sector and know the investment and effort some very large operators have put into the automation game. What I liked about this 3commas platform, is that it opens the door for retail to play in this world. Having access to trading bots that can execute trades automatically based on predefined strategies is one factor, it still requires users to set up custom trading strategies or choose from a marketplace of existing strategies developed by other users. So, what this means is if you have a specific trading strategy you can link directly from Tradingview and just allow it to open trades. I have taken this image as an example from their site, it’s easier than trying to write it myself. There also seems to be a lot of open-source code, literature and information readily available online. All beneficial factors if you’re planning on going down the automation route. Myself and Paul have been more conventional traders, operating in well established markets. But of course we have had our dabbles in alt coins, Bitcoin and so on. It seems to be the way the world is shifting. I have been using webhooks on Tradingview recently to trade Aussie dollar and Euro on smaller timeframes just sending an alert to one of my channels – but the ability to take out the execution stage is a new one on me. If you’re a crypto fanatic I can say this is worth a look for sure! When looking at this automation, I found another editors pick here on @TradingView So, although I know very little about the strategy or the individual trader @Bjorgum who wrote the article, it’s a great example of the type of power mixing things like 3Commas and Tradingview can yield. Throughout 2023 I have shown and shared several articles on Prop firm trading, shorter timeframes and even how to use Chat GPT to write Tradingview indicators. Link to one of them: www.tradingview.com My next step is to use chat GPT to program an indicator I can fully automate (market condition depending) to link to 3Commas using TV as the glue. Here’s an example of what I mean: I literally asked ChatGPT this question “can you write a pinescript version 4 code to enter trades based on pivot point breakouts taking profits at S2 and R2 with stop losses in the other direction at R1 and S1.” I got a reply; Before you ask - The code will probably get rejected to put out as an indicator as Pinescript will say “Pivot point indicators are readily available” but copy and paste my question above and you should get a similar result. Of course, this is only an example. Feel free to play around with your own strategies and concepts. The idea then is to take this through the papertesting and backtesting to refine a strategy that you feel comfortable with in terms of plugging into a bot and connecting to your broker. The whole concept for me is mind blowing, the fact that anyone can have a Tradingview account, use ChatGPT to build and indicator and execute a trade via your broker on a platform like 3Commas. Over the next couple of weeks I intend on digging a little deeper with these and either start with using ChatGPT to link a strategy via Tradingview into 3Commas or take a strategy or indicator off the shelf and test drive it in a stream or sequence of streams. Maybe give me some ideas, if you like? what timeframes? What instruments etc... This will be part of the educational, how to make trading automation a real thing series. Anyways! Enjoy the Holidays - Merry Christmas and a Happy New Year to you all! Disclaimer This idea does not constitute as financial advice. It is for educational purposes only, our principle trader has over 20 years’ experience in stocks, ETF’s, and Forex. Hence each trade setup might have different hold times, entry or exit conditions, and will vary from the post/idea shared here. You can use the information from this post to make your own trading plan for the instrument discussed. Trading carries a risk; a high percentage of retail traders lose money. Please keep this in mind when entering any trade. Stay safe.
COINBASE:BTCUSDEducation
by Mayfair_Ventures
2
How Artificial Intelligence is Revolutionizing the MarketArtificial Intelligence (AI) has permeated almost every aspect of our lives, from virtual assistants to self-driving cars. In recent years, AI has also made significant inroads into the world of finance, particularly in trading. This article explores the transformative impact of AI in trading, shedding light on how it's revolutionizing the market, shaping trading strategies, and offering new opportunities to investors. AI in trading is not a futuristic concept but a present-day reality. Sophisticated algorithms and machine learning models are being employed by traders and financial institutions to gain a competitive edge, make data-driven decisions, and navigate the complex landscape of global financial markets. In this article, we'll delve into the key ways AI is reshaping the trading landscape. One of the primary contributions of AI in trading is the development of highly advanced trading strategies. These strategies leverage AI's ability to analyze vast amounts of data, identify patterns, and make predictions based on historical data and real-time market information. The Role of AI in Trading Strategies: 1. Algorithmic Trading: AI-powered algorithms are designed to execute trades automatically based on pre-defined criteria. These algorithms can process information at speeds impossible for human traders, enabling them to capitalize on fleeting market opportunities. AI algorithms can incorporate technical indicators, news sentiment analysis, and market data to make split-second trading decisions. 2. Sentiment Analysis: AI-driven sentiment analysis tools scour news articles, social media, and other sources to gauge market sentiment. This helps traders understand how public perception may impact asset prices. For example, if a particular stock is trending negatively on social media due to a scandal, AI algorithms can detect this and make informed trading decisions. 3. Risk Management: AI can enhance risk management by providing real-time risk assessment. It can continuously monitor a portfolio's exposure to various assets, assess potential risks, and suggest adjustments to maintain an acceptable risk level. This helps traders avoid catastrophic losses. The future of AI in trading looks promising, with several trends and developments on the horizon: 1. Reinforcement Learning: AI models, particularly reinforcement learning, are expected to play a more significant role in trading. These models can adapt and learn from their actions, making them capable of evolving strategies in response to changing market conditions. 2. Explainable AI: As AI becomes more prevalent in trading, the need for transparency and interpretability is paramount. Explainable AI aims to provide insights into how AI models arrive at their decisions, helping traders understand and trust AI-driven strategies. 3. Retail Investor Access: AI-powered trading tools that were once exclusive to institutional investors are becoming more accessible to retail investors. This democratization of AI-driven trading may empower individual investors to make more informed decisions. 4. Regulatory Challenges: As AI becomes more integrated into financial markets, regulatory bodies will need to address new challenges related to algorithmic trading, market manipulation, and data privacy. Striking the right balance between innovation and oversight will be crucial. In conclusion , AI is revolutionizing the trading landscape by offering powerful tools for analyzing data, developing trading strategies, and managing risks. While AI has already made a significant impact, its influence is expected to grow in the coming years. Investors and traders who adapt to these changes and embrace AI technology are likely to gain a competitive advantage in the evolving world of finance. However, it's essential to remain mindful of ethical and regulatory considerations as AI continues to transform the trading landscape.
NASDAQ:AMDEducation
by financialflagship
AGIX COIN tutorialAGIX, formerly known as AGI, is the native cryptocurrency token of SingularityNET, a blockchain-based platform designed to create, share, and monetize artificial intelligence (AI) services at scale. SingularityNET aims to be a decentralized, open market for AI services, accessible to anyone. The platform uses blockchain technology to ensure transparency and security while facilitating AI service transactions. Key Features of SingularityNET: 1. Decentralization: SingularityNET operates on a decentralized network, which means there is no central control over the AI services. This decentralization promotes a democratic and open ecosystem for AI development and utilization. 2. Marketplace for AI Services : It provides a marketplace where developers can offer their AI services, and users or businesses can purchase these services. This model aims to democratize access to AI technology. 3. Interoperability of AI Services: The platform is designed to support various AI services to interact and collaborate, potentially leading to more advanced and integrated AI solutions. 4. Token Use: The AGIX token is used as a medium of exchange on the platform. It is used to buy and sell AI services within the SingularityNET ecosystem. As for the future outlook of AGIX, it largely depends on several factors: 1. Adoption and Growth of the Platform : The more widespread the adoption of SingularityNET's marketplace for AI services, the more demand there might be for the AGIX token. 2. Advancements in AI Technology: As AI technology continues to advance and becomes more integral in various sectors, platforms like SingularityNET could see increased interest. 3. Competition : The AI and blockchain space is highly competitive and rapidly evolving. The success of AGIX will depend on how well SingularityNET can innovate and differentiate itself from other players in the market. 4. Regulatory Environment : The regulatory landscape for cryptocurrencies and AI technologies is still evolving, and changes in regulations can significantly impact the adoption and use of platforms like SingularityNET. 5. Community and Developer Support: The strength and engagement of the community, as well as the number and quality of developers building on the platform, are crucial for the long-term success of AGIX. 6. Partnerships and Collaborations: Strategic partnerships and collaborations can enhance the utility and adoption of the SingularityNET platform, thereby potentially increasing the value of AGIX. It's important to note that cryptocurrency markets are highly volatile and speculative. Therefore, predictions about the future of AGIX, like any other cryptocurrency, should be approached with caution and based on thorough research and analysis.
AEducation
by Aibullz
AI-Driven Market Analysis: Revolutionizing Financial InsightsIntroduction Market analysis has long been the cornerstone of financial decision-making, offering insights into market trends, asset valuation, and investment opportunities. Traditionally, this analysis has relied on a combination of statistical methods, fundamental analysis, and expert judgment to interpret market dynamics and forecast future movements. However, the finance industry is currently undergoing a seismic shift with the introduction and integration of Artificial Intelligence (AI). AI, with its unparalleled ability to process and analyze vast quantities of data at unprecedented speeds, is revolutionizing market analysis. Unlike traditional methods, which often struggle with the sheer volume and complexity of modern financial data, AI algorithms can quickly sift through global market data, news, and financial reports, identifying patterns and correlations that might escape human analysts. This capability is not just about handling data efficiently; it's about uncovering deeper market insights and offering more nuanced, informed perspectives on market movements. The growing role of AI in financial market analysis is multifaceted. It encompasses predictive analytics, which forecasts market trends and asset price movements; risk assessment, which evaluates potential risks and market volatility; and sentiment analysis, which gauges market sentiment by analyzing news, social media, and financial reports. These AI-driven approaches are transforming how investors, traders, and financial institutions make decisions, offering a more data-driven, precise, and comprehensive view of the markets. As we delve deeper into the world of AI-driven market analysis, it's crucial to understand both its potential and its limitations. While AI provides powerful tools for market analysis, it also introduces new challenges and considerations, particularly around data quality, algorithmic bias, and ethical implications. In this article, we'll explore how AI is changing the landscape of market analysis, examining its applications, benefits, and future prospects in the ever-evolving world of finance. The Evolution of Market Analysis A Brief History of Market Analysis in Finance Market analysis in finance has a storied history, evolving through various stages as it adapted to changing markets and technological advancements. Initially, market analysis was predominantly fundamental, focusing on the intrinsic value of assets based on economic indicators, financial statements, and industry trends. Technical analysis, which emerged later, shifted the focus to statistical trends in market prices and volumes, seeking to predict future movements based on historical patterns. Over the decades, these approaches were refined, incorporating increasingly sophisticated statistical models. However, they remained limited by the human capacity to process information. Analysts were constrained by the volume of data they could analyze and the speed at which they could process it. This often led to a reactive approach to market changes, rather than a predictive one. Transition from Traditional Methods to AI Integration The advent of computer technology brought the first major shift in market analysis. Computers enabled quicker processing of data and complex mathematical modeling, allowing for more sophisticated analyses that could keep pace with the growing volume and velocity of financial market data. The introduction of quantitative analysis in the latter part of the 20th century marked a significant step in this evolution, as it used complex mathematical and statistical techniques to identify market opportunities. The real transformation, however, began with the integration of AI and machine learning into market analysis. AI's ability to learn from data, identify patterns, and make predictions, has taken market analysis to an entirely new level. AI algorithms can analyze vast datasets — including historical price data, financial news, social media sentiment, and economic indicators — much faster and more accurately than any human analyst could. This integration of AI into market analysis has led to the development of predictive models that can forecast market trends and anomalies with a higher degree of accuracy. AI-driven tools are now capable of real-time analysis, providing instantaneous insights that help traders and investors make more informed decisions. Furthermore, AI's ability to continually learn and adapt to new data sets it apart from static traditional models, allowing for a more dynamic and responsive approach to market analysis. The transition from traditional methods to AI integration represents a paradigm shift in market analysis. This evolution is not just about adopting new tools but signifies a fundamental change in how financial markets are understood and navigated. As we continue to advance in the realm of AI, the potential for even more sophisticated and insightful market analysis grows, promising to reshape the landscape of finance in ways we are only beginning to comprehend. Fundamentals of AI in Market Analysis The integration of Artificial Intelligence (AI) and machine learning into market analysis marks a significant advancement in the way financial data is interpreted and utilized. Understanding the fundamentals of these technologies is essential to appreciate their impact on market analysis. Explanation of AI and Machine Learning AI refers to the simulation of human intelligence in machines that are programmed to think and learn like humans. In the context of market analysis, AI enables the automation of complex tasks, including data processing, pattern recognition, and predictive analytics. Machine learning, a subset of AI, involves the development of algorithms that can learn and improve from experience without being explicitly programmed. In market analysis, machine learning algorithms analyze historical data to identify patterns and predict future market behavior. The more data these algorithms are exposed to, the more accurate their predictions become. Types of AI Models Used in Market Analysis 1. Neural Networks: Inspired by the human brain's structure, neural networks consist of layers of interconnected nodes that process data in a manner similar to human neurons. In market analysis, neural networks are used for their ability to detect complex patterns and relationships within large datasets. They are particularly effective in predicting price movements and identifying trading opportunities based on historical market data. 2. Regression Models: These models are fundamental in statistical analysis and are used to understand relationships between variables. In finance, regression models help in forecasting asset prices and understanding the impact of various factors (like interest rates, GDP growth, etc.) on market trends. 3. Time Series Analysis Models: Time series models are crucial in financial market analysis, as they are specifically designed to analyze and forecast data points collected over time. These models help in understanding and predicting trends, cyclicality, and seasonal variations in market data. 4. Natural Language Processing (NLP): NLP is used to analyze textual data, such as financial news, earnings reports, and social media posts, to gauge market sentiment. By processing and interpreting the nuances of human language, NLP models can provide insights into how public sentiment is likely to impact market movements. 5. Decision Trees and Random Forests: These models are used for classification and regression tasks. In market analysis, they can help in categorizing stocks into different classes based on their characteristics or in predicting the likelihood of certain market events. 6. Reinforcement Learning: This type of machine learning involves algorithms learning optimal actions through trial and error. In trading, reinforcement learning can be used to develop strategies that adapt to changing market conditions to maximize returns. Each of these AI models brings a unique set of capabilities to market analysis. Their ability to handle large volumes of data, recognize complex patterns, and make informed predictions is transforming the field of financial analysis, allowing for more nuanced and sophisticated market insights. As AI technology continues to evolve, its applications in market analysis are poised to become even more integral to financial decision-making. Key Applications of AI in Market Analysis The incorporation of Artificial Intelligence (AI) in market analysis has opened up new frontiers in understanding and predicting market behavior. AI's ability to process vast datasets and uncover intricate patterns provides invaluable insights for investors, traders, and financial analysts. Here are some key applications of AI in market analysis: 1. Predictive Analytics for Market Trends One of the most significant contributions of AI in market analysis is predictive analytics. AI algorithms, particularly those based on machine learning, are adept at analyzing historical data to forecast future market trends. These algorithms can identify subtle patterns and correlations that might be invisible to the human eye, enabling predictions about price movements, market volatility, and potential trading opportunities. As these models are exposed to more data over time, their accuracy in forecasting trends continues to improve. 2. Real-time Data Processing and Interpretation The financial markets generate vast amounts of data every second. AI excels in processing this data in real-time, providing instantaneous insights that are critical in a fast-paced trading environment. This capability allows for the monitoring of live market conditions, immediate identification of market shifts, and quick response to unforeseen events. Real-time analysis ensures that trading strategies can be adjusted promptly to capitalize on market opportunities or mitigate risks. 3. Automated Technical Analysis Technical analysis involves the study of historical market data, primarily price and volume, to forecast future market behavior. AI-driven automated technical analysis takes this to a new level by using algorithms to scan and interpret market data at scale. These algorithms can automatically identify technical indicators, chart patterns, and other key metrics used in technical analysis. This automation not only speeds up the analysis process but also eliminates human bias and error, leading to more objective and reliable insights. 4. Sentiment Analysis from News and Social Media Market sentiment, the overall attitude of investors towards a particular market or security, can significantly influence market movements. AI, particularly through Natural Language Processing (NLP), plays a crucial role in analyzing sentiment. It processes vast amounts of unstructured data from news articles, financial reports, social media posts, and other textual sources to gauge public sentiment towards the market or specific investments. By analyzing this data, AI can provide insights into how collective sentiment is likely to impact market trends and investment decisions. These applications highlight the transformative role of AI in market analysis. By leveraging AI for predictive analytics, real-time data processing, automated technical analysis, and sentiment analysis, market participants can gain a more comprehensive, accurate, and nuanced understanding of market dynamics. This advanced level of analysis is not only enhancing traditional market analysis methods but is also shaping new strategies and approaches in the financial sector. Case Studies: Success Stories of AI-Driven Market Analysis The integration of Artificial Intelligence (AI) in market analysis has not only been a topic of academic interest but has also seen practical applications with significant impacts on market decisions. Several real-world case studies illustrate how AI-driven analysis has transformed trading strategies and financial insights. Here are a couple of notable examples: Case Study 1: AI in Predicting Stock Market Trends One of the most prominent examples is the use of AI by a leading investment firm to predict stock market trends. The firm developed a machine learning model that analyzed decades of market data, including stock prices, trading volumes, and economic indicators. This model was designed to identify patterns that precede significant market movements. In one instance, the AI system predicted a substantial market correction based on unusual trading patterns it detected, which were subtle enough to be overlooked by traditional analysis methods. The firm acted on this insight, adjusting its portfolio to mitigate risk. When the market did correct as predicted, the firm was able to avoid significant losses, outperforming the market and its competitors. Case Study 2: Enhancing Hedge Fund Strategies with AI Another case involves a hedge fund that integrated AI into its trading strategies. The fund employed deep learning algorithms to analyze not just market data but also alternative data sources such as satellite images, social media sentiment, and supply chain information. This comprehensive analysis allowed the fund to identify unique investment opportunities and trends before they became apparent to the market at large. For example, by analyzing satellite images of retail parking lots, the AI could predict quarterly sales trends for certain companies before their earnings reports were released. Combining these insights with traditional financial analysis, the fund made informed decisions that led to substantial returns, demonstrating the power of AI in enhancing traditional investment strategies. Impact of AI on Specific Market Decisions These case studies illustrate the profound impact AI can have on market decisions. AI-driven market analysis allows for more accurate predictions, better risk management, and the identification of unique investment opportunities. It enables market participants to make more informed, data-driven decisions, often leading to better financial outcomes. Moreover, the use of AI in these examples highlights a shift towards a more proactive approach in market analysis. Rather than reacting to market events, AI allows analysts and investors to anticipate changes and act preemptively. This shift is not just about leveraging new technologies but represents a broader change in the philosophy of market analysis and investment strategy. In summary, these real-world applications of AI in market analysis showcase its potential to transform financial strategies and decision-making processes. As AI technology continues to evolve and become more sophisticated, its role in market analysis is set to become even more integral and impactful. Future of AI in Market Analysis The landscape of market analysis is rapidly evolving, with Artificial Intelligence (AI) at the forefront of this transformation. The future of AI in market analysis is not just about incremental improvements but also about paradigm shifts in how financial data is processed, interpreted, and utilized for decision-making. Here are some emerging trends and potential shifts that could redefine the role of AI in market analysis: Emerging Trends and Technologies 1. Advanced Predictive Analytics: The future will likely see more sophisticated predictive models using AI. These models will not only forecast market trends but also provide probabilistic scenarios, offering a range of possible outcomes with associated probabilities. 2. Explainable AI (XAI): As AI models become more complex, there will be a greater need for transparency and interpretability. XAI aims to make AI decision-making processes understandable to humans, which is crucial for trust and compliance in financial markets. 3. Integration of Alternative Data: AI's ability to process and analyze non-traditional data sources, such as satellite imagery, IoT sensor data, and social media content, will become more prevalent. This will provide deeper, more diverse insights into market dynamics. 4. Real-time Risk Management: AI will enable more dynamic risk assessment models that update in real-time, considering the latest market data and trends. This will allow for more agile and responsive risk management strategies. 5. Automated Compliance and Regulation Monitoring: AI systems will increasingly monitor and ensure compliance with changing regulatory requirements, reducing the risk of human error and the burden of manual oversight. 6. Quantum Computing in Market Analysis: The potential integration of quantum computing could exponentially increase the speed and capacity of market data analysis, allowing for even more complex and comprehensive market models. Potential Shifts in Market Analysis Strategies 1. From Reactive to Proactive Analysis: AI enables a shift from reacting to market events to proactively predicting and preparing for them. This will lead to more forward-thinking investment strategies. 2. Personalization of Investment Strategies: AI can tailor investment advice and strategies to individual investors' profiles, risk appetites, and goals, leading to more personalized financial planning and portfolio management. 3. Democratization of Market Analysis: Advanced AI tools could become more accessible to a broader range of investors and firms, leveling the playing field between large institutions and smaller players. 4. Increased Emphasis on Data Strategy: As AI becomes more central to market analysis, there will be an increased focus on data strategy - how to source, manage, and leverage data effectively. 5. Redefining Skill Sets in Finance: The rising importance of AI will change the skill sets valued in finance professionals. There will be a greater emphasis on data science skills alongside traditional financial analysis expertise. In conclusion, the future of AI in market analysis is not just promising but revolutionary. It is poised to redefine traditional practices, introduce new capabilities, and create opportunities for innovation in the financial sector. As these technologies advance, they will continue to shape the strategies and decisions of market participants, marking a new era in financial market analysis.
AMEX:SPYEducation
by JS_TechTrading
People want to earn but not learnThe issue is everyone wants to make money (well, maybe not everyone) but nobody wants to take the time to learn how to do it properly. This is NOT a sales pitch by the way! it's FACT!! People often ask why I bash influencers so much, it's mainly for this reason. Majority of noobs, come into trading expecting to make a fortune. If only it was that easy, every man and his dog would be a professional trader. Over the years, I have talked about things like Bots and AI that are programmed to make you money - think logically, if again it is this easy wouldn't the founders go to the bank, loan $10million based on their results and just not bother selling and shilling to customers and retail. NOBODY wants to provide customer service, especially to the world's population. Unfortunately, regardless of the market. Trust me if you stick around long enough you get to see this behaviour in Forex, Commodities, Stocks and more recently crypto with a splash of A.I. The story goes pretty much the same way. "man (or woman) hears about an opportunity to make money through a thing called trading, they do their research which leads to the old You of Tube and that leads to "Lamborghini promises from kids with fake watches, drawing random trendlines on 3 minute charts" There's often a "sign-up" bonus if you click their shill link. So let's get this straight, they make money on watch time and those links you click. The reason I chose fish in the image above, is that most people have memories that last about 2 seconds. Mark Cuban said "everyone is a genius in a bull market" Algorithms work and influencers claim to be experts with 3 months of experience. Easy to show in a market only going one way. Trading is hard enough, let alone having the ability to lose money from scams. If a trading algorithms promises a 90% win rate - run and don't buy it. ============================================================== There are fundamental things to do and you can deploy to get you off on the right track. Firstly think of the obvious. 90% of new traders lose 90% of their money in only 90 days. Hence a 50% sign-up bonus whereby you think you gained "free cash" often has small print that you can't access it until you lost your original investment. Affiliates tend to get 25% or more of the deposit - the exchanges know full well, your about to lose your money. Second thing I try to emphasis for newer traders, is that you need to treat trading as a profession. You wouldn't watch a video and expect to be a doctor, you also wouldn't buy an algorithm or Artificial Intelligence software and expect to become New York's latest Hot Shot Lawyer You see where this is going? There is no secret sauce, no silver bullet and no short cuts. If you want to trade and make money trading, you need the basics. You need to keep doing the basics well and evolve your mindset more than a strategy. Areas that will really help you include proper risk management. If your willing to be sat in negative 20, 30 or even 50% equity positions. This won't take you long to lose your entire trading pot. Instead risking 1-2% with a risk strategy of 2 -1 or greater. it's a slower game, but it keeps you playing the game. If you take a 3 or even a 4 reward trade with only 1 risk. For every time you are right, it's giving you 4 times as much as when you are wrong. Imagine winning 20% of your trading days and still being at breakeven... simple 1:4 ratio. This is only one small aspect to keep in mind. As I mentioned above, if strategies or software is pitched with high percentage win rates - run. You need to understand the market acts differently and past results do not indicate future performance. Everyone is a genius in a bull market, remember. You do not need to go looking for the silver bullet. These strategies do not exist, instead spend the time working on strategies that can be consistent in various market conditions. This is no small task, your strategy might identify entries in a counter trend differently than it would in say a ranging market. The answer to resolve this, is BACKTESTING Don't just run your strategy on replay mode, although @TradingView has a great little tool for this. Spend the time to look at things such as "repainting" this means that when your strategy triggers an entry, does it disappear and reappear. If so, do some manual back testing. Then Dig deeper and analyse the type of market condition it was more profitable or less profitable. This could be things like "I lose more on a Monday, compared to other days" or when the market goes sideways, It triggers too many trades. I've written several articles here on pure education. Here's a few examples. In this post (worth clicking on) it has a whole bunch of lessons inside. Think of trading like you would a university course, there's plenty to learn but you can have some fun along the way! Stay safe! Disclaimer This idea does not constitute as financial advice. It is for educational purposes only, our principle trader has over 20 years’ experience in stocks, ETF’s, and Forex. Hence each trade setup might have different hold times, entry or exit conditions, and will vary from the post/idea shared here. You can use the information from this post to make your own trading plan for the instrument discussed. Trading carries a risk; a high percentage of retail traders lose money. Please keep this in mind when entering any trade. Stay safe.
Editors' picks
COINBASE:BTCUSDEducation
by Mayfair_Ventures
37
Inflation vs Innovation Can the Markets Handle the HeatGlobal markets face contradictory forces in 2023. Inflation still simmers as central banks tighten money supply worldwide. Geopolitical friction continues while economic growth likely slows ahead. Yet technological transformation charges ahead, with artificial intelligence poised for explosive improvements. Investors and policymakers must stay nimble in this uncertain environment. After plunging painfully in 2022, stocks have rebounded with vigor so far this year. This despite raging inflation and the Federal Reserve's hawkish stance on interest rates. Hefty liquidity efforts in China likely buoyed prices. Investors may also have grown too pessimistic amid still-sturdy corporate profits. But sentiment could sour again if supply chain snarls resurface. In bond markets, yields continue reflecting dreary growth expectations after last year's surge. The inverted yield curve especially screams pessimism on the near-term economy. Meanwhile, the Fed's bond portfolio shrinkage has yet to rattle markets. This implies the Fed's quantitative easing and tightening have limited impact on actual money supply, defying popular perception. On inflation, early 2023 figures show it easing from 40-year heights but still well above the Fed's 2% bullseye. The Fed remains leery of declaring victory prematurely. Taming inflation sans triggering severe recession is an epic challenge. Geopolitical wild cards like the Russia-Ukraine war that evade the Fed's grasp will shape the outcome. Amidst these crosscurrents, technological forces advance relentlessly. The frantic digitization around COVID-19 now gives way to even more seismic innovations. The meteoric success of AI like ChatGPT provides a mere glimpse of the transformations coming for healthcare, transportation, customer service and virtually every industry. The promise appears gargantuan, with AI generating solutions and ideas no human could alone conceive. But the warp-speed pace also carries perils if ethics and safeguards fail to keep up. Mass job destruction and wealth hoarding by Big Tech could ensue absent mitigating policies. But wisely harnessed AI also holds potential to uplift living standards globally. For investors, AI has already jet-propelled leaders like Google, Microsoft, Nvidia and Amazon powering this tech revolution. But smaller firms wielding these tools may also see jackpot gains, as costs plunge and new opportunities emerge across sectors. That's why non-US and smaller stocks may provide superior opportunities versus overvalued big US tech. In conclusion, the global economic and financial landscape simmers with familiar threats and novel technological promise. Inflation may moderate but seems unlikely to vanish given lingering supply dysfunction and distortions from massive stimulus. Stocks navigate shifting sentiment amid rising rates and demand doubts. And machine learning progresses rapidly into a future we can now scarcely envision. Nimbly navigating such turbulence requires flexibility, tech savviness and philosophical courage. Responsibly steering AI's development is a herculean challenge, to maximize benefits and minimize pitfalls. Individuals need to stay skilled while advocating protections against job disruption. Policymakers face wrenching tradeoffs between growth, inflation and financial stability - all compounded by geopolitics. Yet within uncertainty lies opportunity for those poised to seize it. The future remains ours to shape, if we summon the wisdom and will to guide technology toward enriching human life rather than eroding it. The road ahead will be arduous but need not be hopeless, if compassion and conscience inform our creations.
NASDAQ:NDXEducation
by BitcoinMacro
Investing In Artificial Intelligence (AI) : Beginner’s GuideThe field of artificial intelligence (AI) has emerged as a highly attractive investment option, captivating the attention of investors worldwide. With its capacity to reshape industries and drive innovation, AI has gained prominence as a transformative technology. By simulating human intelligence and performing intricate tasks, AI is revolutionizing sectors ranging from transportation to finance and beyond. Given the rapid growth of the AI market, which is projected to reach revenues of up to $900 billion by 2026, having a comprehensive understanding of how to invest in this dynamic field has become crucial for investors. In this comprehensive guide tailored for beginners, we will delve into the fundamentals of AI, exploring its underlying concepts, methodologies, and applications across various industries. By gaining insight into the inner workings of AI, investors can grasp the potential impact it can have on different sectors, enabling them to identify promising investment opportunities. Moreover, we will examine diverse investment strategies that investors can consider when venturing into the AI market. These strategies will encompass a range of approaches, from investing in established AI companies and technology giants, to exploring opportunities in startups and early-stage ventures that are driving innovation in the AI space. Additionally, we will explore investment vehicles such as AI-focused exchange-traded funds (ETFs) and mutual funds, providing investors with a broader exposure to the AI market. Throughout this guide, we will address the key factors to consider when investing in AI, including the evaluation of AI technologies, understanding regulatory and ethical implications, and staying updated with the latest industry trends. By equipping investors with the necessary knowledge and insights, this guide aims to empower them to make informed investment decisions in the dynamic and evolving landscape of AI. As AI continues to redefine industries and shape the future, investing in this transformative technology presents an exciting opportunity for investors seeking long-term growth and exposure to cutting-edge innovation. Through this beginner's guide, we invite investors to embark on a journey into the world of AI investment, unlocking the potential for both financial returns and contributions to the advancement of society as a whole. Artificial Intelligence (AI) Explained Artificial Intelligence (AI) has emerged as a groundbreaking technology that aims to replicate human intelligence in computers and machines, surpassing human capabilities in terms of speed and accuracy. Leading companies like Microsoft (MSFT) and Google (GOOGL) utilize AI to develop systems capable of problem-solving, answering inquiries, and executing tasks that were traditionally performed by humans. The advancement of AI systems has expanded their applications across diverse industries and sectors. One notable transformation is occurring in the transportation industry, where electric and autonomous vehicles are revolutionizing travel and poised to contribute trillions of dollars to the global economy. In the banking sector, AI is employed to enhance decision-making processes in high-speed trading, automate back-office functions such as risk management, and even introduce humanoid robots in branches to reduce costs. These examples only scratch the surface of the extensive range of AI applications. Analysts at International Data Corp. (IDC), a renowned market intelligence provider, project that the AI market will generate global revenues of up to $900 billion by 2026. This estimate reflects a significant compound annual growth rate of 18.6 percent from 2022 to 2026, underscoring the exponential growth potential of AI. What was once considered a luxury has now become an essential component of modern businesses. The global pandemic has accelerated the adoption of AI, making it pervasive across all aspects of business operations. From healthcare and manufacturing to finance and customer service, AI has demonstrated its value in enhancing efficiency, optimizing processes, and driving innovation. Investing in AI presents an opportunity to capitalize on its transformative potential. However, it is essential for investors to approach AI investments with a thorough understanding of the technology, its applications, and the companies leading the way. As AI continues to shape industries and redefine the future, investors who navigate this dynamic landscape stand to benefit from its long-term growth and the potential for significant returns. How To Invest In Artificial Intelligence As a retail investor, you may already have exposure to artificial intelligence (AI) through various prominent U.S. public companies that utilize AI or invest in this technology. However, if you're specifically interested in investing in AI, there are several approaches you can consider: Individual Stocks: Conduct thorough research and invest directly in companies that specialize in AI development, application, or integration. Look for companies with a strong focus on AI, a robust research and development program, and a history of innovation in the field. Exchange-Traded Funds (ETFs): Explore AI-focused ETFs that concentrate on companies involved in AI technologies. These funds offer diversification by investing in a portfolio of AI-related stocks, providing exposure to a broad range of companies in the AI sector. Index Funds: Invest in broad market index funds that include leading companies at the forefront of AI development. These funds track major market indices like the S&P 500, which often include prominent players in the AI industry. Additionally, Contract for Difference (CFD) trading is another option for investing in AI. CFDs allow you to speculate on the price movements of AI-related assets without actually owning the underlying assets. By taking long or short positions, you can potentially profit from both upward and downward price movements in the AI sector. However, it's important to note that CFD trading carries a higher level of risk and requires a good understanding of market dynamics. Top AI Stocks To Consider: Microsoft (MSFT) As of May 2023, Microsoft, the renowned developer of the Windows operating system, holds the position of the largest Artificial Intelligence (AI) company. In recent times, Microsoft has made significant strides in the field of AI, unveiling a range of new features and initiatives across its product line. One notable development is the integration of AI-powered enhancements into Edge, Microsoft's web browser. These enhancements leverage AI technology to provide users with improved browsing experiences, including enhanced performance, personalized recommendations, and advanced security features. Furthermore, Microsoft has incorporated AI capabilities into Bing, its search engine. The integration of AI allows Bing to deliver more accurate and relevant search results, enhancing the overall search experience for users. Highlighting its commitment to AI, Microsoft announced a substantial investment in OpenAI, the creator of ChatGPT, a widely used language model. This multiyear and multibillion-dollar partnership have resulted in the deployment of OpenAI models across Microsoft's product range, including the Azure OpenAI Service. Additionally, Microsoft's Azure cloud platform serves as the exclusive provider for OpenAI's cloud-based services. By investing in OpenAI and integrating AI capabilities into its products and services, Microsoft aims to harness the power of AI to deliver innovative solutions and enhance user experiences. This strategic focus on AI demonstrates Microsoft's recognition of the transformative potential of this technology and its dedication to remaining at the forefront of the AI industry. Tesla (TSLA) In the realm of electric vehicles (EVs), Tesla stands apart from technology giants like Microsoft and Alphabet by leveraging AI and robotics to drive innovation. The company has positioned itself as a leader in self-driving cars, an area heavily reliant on AI for tasks such as visual processing and strategic planning. Tesla is actively pursuing the development of self-driving technology and has been working on AI inference chips that are specifically designed to run its full self-driving software (FSD). These chips enable efficient and powerful processing capabilities, enabling Tesla vehicles to make real-time decisions and navigate autonomously. Beyond self-driving vehicles, Tesla has expanded its AI endeavors into the realm of humanoid robots. In October 2022, CEO Elon Musk unveiled "Optimus," a highly anticipated robot. Musk envisions a future where Tesla's robot business surpasses the value of its cars, indicating a broader ambition to extend beyond the automotive industry. In addition to self-driving technology and robotics, Tesla is actively involved in various AI fields. This includes the development of Dojo chips and systems, which aim to enhance AI training and accelerate computational processes. Tesla also focuses on neural networks, autonomy algorithms, code foundations, and evaluation infrastructure to continuously improve and refine its AI capabilities. By applying AI and robotics to the EV industry, Tesla is at the forefront of technological advancements and aims to shape the future of transportation. Its commitment to developing cutting-edge AI solutions demonstrates the company's dedication to pushing the boundaries of innovation and redefining the possibilities within the automotive industry. IBM (IBM) In May 2023, IBM, a computing giant with a long-standing history in the technology industry, made a significant announcement regarding its platform called Watsonx. This platform is designed to empower developers by providing them with a comprehensive set of tools for creating AI models. Watsonx equips developers with machine learning tools, foundational models, hardware resources, and data storage capabilities, enabling them to build sophisticated AI applications. By offering a range of resources within a unified platform, IBM aims to streamline the AI development process and make it more accessible to developers. In collaboration with Hugging Face, a prominent provider of open-source AI libraries, IBM has integrated the benefits of Hugging Face's libraries and extensive collection of open models and datasets into the Watsonx.ai studio. This collaboration allows developers to leverage Hugging Face's resources and tap into a vast array of pre-trained models and datasets, accelerating the development of AI solutions. Beyond its AI offerings, IBM has been actively involved in AI integration research. The company's Global AI Adoption Index explores the impact of AI adoption on businesses and society as a whole. This research initiative aims to provide insights into the current state of AI adoption, identify trends, and understand the potential implications of AI on various industries and sectors. IBM's commitment to advancing AI technology, as demonstrated by its Watsonx platform and research initiatives, highlights the company's ongoing efforts to drive innovation and facilitate the integration of AI into diverse domains. By empowering developers and exploring the broader implications of AI adoption, IBM continues to play a significant role in shaping the future of artificial intelligence. Alphabet (GOOGL) Alphabet, the parent company of Google, has been actively investing in the AI sector, demonstrating its commitment to advancing artificial intelligence technologies. In April, Alphabet's venture capital subsidiary, CapitalG, played a leading role in a $100 million funding round for AlphaSense, an AI startup. This investment not only highlights Alphabet's financial support for AI innovation but also strengthens its presence in the AI industry. In addition to its investment activities, Google, as a part of Alphabet, has made substantial investments in other AI-related companies. For instance, Google has invested approximately $400 million in Anthropic, a competitor to ChatGPT, further expanding its involvement in the AI landscape. Furthermore, Google has acquired Alter, a startup specializing in AI avatars, which showcases its strategic focus on enhancing AI capabilities and exploring new applications for the technology. Within its own product ecosystem, Google has introduced various generative AI tools that leverage the power of artificial intelligence. One notable example is Bard, Google's own counterpart to ChatGPT, which provides real-time access to information from the web. This demonstrates Google's efforts to develop AI models capable of generating dynamic and contextually relevant content. Moreover, Google is incorporating AI functionality into its Workspace suite, starting with popular tools like Gmail and Google Docs. By integrating AI capabilities into these productivity tools, Google aims to enhance user experiences, improve efficiency, and enable new possibilities for collaboration and content generation. Alphabet's investments in AI startups, acquisitions, and the development of generative AI tools highlight the company's dedication to harnessing the potential of artificial intelligence. Through these initiatives, Alphabet continues to shape the AI landscape and drive innovation in the field. Amazon (AMZN) Amazon, a prominent player in the AI field, has established itself as a leader by offering a comprehensive suite of AI and machine learning (ML) services through its cloud computing platform, Amazon Web Services (AWS). AWS provides a wide range of tools and services that empower developers and businesses to integrate AI and ML functionalities into their applications and workflows efficiently. Notably, Amazon not only provides AI services to other businesses but also harnesses AI capabilities within its own operations. For instance, the company employs sophisticated AI algorithms in its online store to deliver personalized product recommendations to customers, creating a more tailored and engaging shopping experience. One of Amazon's most recognizable AI applications is Alexa, the virtual assistant powering Echo devices. Powered by natural language processing and ML algorithms, Alexa can comprehend and respond to user commands, enabling users to interact with their devices using voice commands. This integration of AI technology has revolutionized the way people interact with their smart devices and has become a prominent feature in many households. Amazon's commitment to AI is further evident through its ongoing investments in AI research and development. The company continually seeks to advance AI technologies, exploring new applications and improving existing capabilities. By embracing AI in various aspects of its business, Amazon aims to enhance customer experiences, drive innovation, and remain at the forefront of AI integration in the industry. Oracle (ORCL) Oracle (ORCL), a renowned provider of cloud computing solutions, has emerged as a leading player in the AI landscape by offering the Oracle Cloud Infrastructure. This robust cloud platform serves as the foundation for various workloads, including AI applications, empowering businesses to leverage the benefits of AI technology. Recognizing the growing significance of AI, Oracle has taken steps to enhance its AI capabilities for enterprise customers. Notably, the company has expanded its collaboration with Nvidia, a prominent chipmaker specializing in AI hardware. This strategic partnership allows Oracle to harness the power of Nvidia's advanced AI-focused GPUs (Graphics Processing Units) and other hardware technologies. By integrating Nvidia's hardware into its infrastructure, Oracle aims to deliver enhanced AI performance to its enterprise customers. This collaboration equips businesses with the ability to process vast datasets and execute complex AI algorithms more efficiently, leading to improved insights and outcomes. By leveraging Nvidia's powerful AI hardware, Oracle demonstrates its commitment to providing cutting-edge AI solutions that address the evolving needs of businesses in the era of digital transformation. Through its collaboration with Nvidia and its focus on advancing AI capabilities, Oracle solidifies its position as a leading provider of AI-enabled cloud infrastructure and reinforces its commitment to empowering businesses with the tools and technologies needed to harness the potential of AI in their operations. How To Select The AI Stocks To Invest In : When selecting AI stocks to invest in, it's important to conduct thorough research and consider various factors. Here are some key considerations to help guide your decision-making process: 1) Company's fundamentals: Review the financial health and performance of the company. Analyze its financial statements, including the balance sheet, income statement, and cash flow statement. Look at key indicators such as the price-to-earnings (P/E) ratio, return on equity (ROE), and debt-to-equity (D/E) ratio to assess its profitability and financial stability. 2) Technical analysis: If you're a short-term trader, utilize technical analysis to study price patterns and trends. Use technical indicators and candlestick charts to identify entry and exit points based on historical price movements. 3) Analyst ratings: Consider the latest analyst ratings and commentary on specific stocks. Analyst opinions can provide valuable insights, but keep in mind that they are subjective and should be considered alongside other factors. 4) Latest company news: Stay updated on a company's news and developments. Look for announcements related to AI investments, acquisitions, R&D initiatives, and new product offerings. This information can indicate a company's growth potential and competitive positioning. 5) Competitive landscape: Assess the company's position within the AI industry and its competitive advantage. Consider its technology, market share, and ability to innovate. Evaluate how it compares to other players in the market. 6) Management team: Evaluate the leadership and management team of the company. Look for experienced executives who have a track record of success and a clear vision for the company's future. 7) Industry trends: Stay informed about the latest trends and advancements in the AI industry. Understand how AI is being adopted across different sectors and the potential impact it may have on the company you're considering. 8) Regulatory environment: Consider the regulatory landscape surrounding AI. Assess how regulations and policies may affect the company's operations and growth prospects. 9) Diversification: Manage risk by diversifying your investments across different AI stocks and sectors. This helps mitigate the impact of individual stock performance and provides exposure to a range of opportunities. Conclusion: Investing in AI presents unique opportunities for investors as this cutting-edge technology continues to transform industries and drive innovation. The potential for AI to revolutionize various sectors, enhance efficiency, and create new business models is immense. Whether through individual stock investments, AI-focused ETFs, index funds, or even CFD trading, investors can participate in the AI market and potentially benefit from its growth. However, investing in AI requires careful consideration and research. It is important to understand the fundamentals of AI, including its applications and potential impact on industries. Analyzing company financials, such as balance sheets and income statements, can provide insights into the financial health and long-term potential of AI-focused companies. Staying updated on industry trends, news, and developments is crucial. Monitoring AI-related investments, partnerships, research, and product advancements can help identify companies that are at the forefront of AI innovation. Diversification is also key in AI investing. Spreading investments across different AI stocks, sectors, and geographies can help mitigate risk and capture opportunities in various segments of the AI market. Lastly, it is important to remain informed and adaptable as AI technology continues to evolve. Regularly assessing and adjusting investment strategies based on market conditions and emerging trends is essential to capitalize on the transformative potential of AI. By understanding the fundamentals, conducting thorough research, and staying informed, investors can position themselves to potentially benefit from the growth and impact of AI in the years to come.
NASDAQ:TSLAEducation
by FOREXN1
10
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
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
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
🔥SECRET METHOD TO IDENTIFY LONG TERM TREND:VOLUME PROFILE+POC🚀🔥Hi, friends! Trend is the most important thing in trading, so you have to know how to identify it and be a successful trader. In this idea, I will explain to you the most easiest and useful method for trend identification. I know that you haven't heard about that. 📊 THE INSTRUMENTS WE USE TO IDENTIFY THE TREND: 🔥 Fixed Range Volume Profile (FRVP) 🔥 Point of control or POC 🔥 Bitcoin monthly candles/bars. They help to identify the long-term trend without local noise ✅ IF YOU WANT TO UNDERSTAND IF THE BULL MARKET BEGINS AND CONTINUES, YOU NEED: 1. Use the Volume Profile. Just pick it at the left side of the TradingView chart at "Prediction and Measurement Tools". 2. Stretch the volume profile on a monthly candle/bar. 3. After this you will see the Point of Control (POC, red line) which shows you where the most liquidity is concentrated. 4. If the price continues to close above the POC each month, this means that we have a healthy bull market. 5*. If you see that the monthly candle is close below the POC, you need to be more careful with your trends. This can indicate about a trend change, but it happens at least 1 time in the middle of the bull market. Check the precious bull market. Thats work perfectly! 🚩 You can check if it rule works for the BEAR market and write it in the comments. 📊 WHY IT'S IMPORTANT TO UNDERSTAND THE LONG TERM TREND I know that you guys know the most famous trading quote: "Trend is your friend." . Naive but very useful recommendation. The understanding of the long term trend helps you to reduce 50-70% of your losing trades and increase your winrate at least to 60-80%: 🔥 you can use only trend following strategy and make much more money (open only long trades on bull market or open only a short trades on bear market) 🔥 reduce the risk when opening a trade against the trend and cut the losses. 🚩 Additionally, if you buy crypto (BTC, ETH or other alts) on spot, you can use this method to buy and hold crypto till BTC not change the trend. This method helps you to make a huge profit. So friends, this 5 min educational idea helps you to grow your deposit much faster and don't get big losses during the trading. Traders, was it useful for you? I know I have is a lot of experienced traders. Write your most useful trading tips in the comments to help the newbies. 💻Friends, press the "boost"🚀 button, write comments and share with your friends - it will be the best THANK YOU. P.S. Personally, I open an entry if the price shows it according to my strategy. Always do your analysis before making a trade.
Education
by Yuriy_Bishko
4
Chat GPT Ai stocks list for 2023Is chat gpt and Artificial Intelligence the next boom theme? Why are so many people talking about it recently? How long can this new trend create opportunity for , years, decades? How do we value the opportunity and avoid paying too much? Dont get ripped off and caught up in the hype, use math and valuation to price the reward and risk in a balanced logical way. The legends of investing have given us they formulas through lifetimes of trial and error. We just need to apply them. Benjamin Graham, Peter Lynch, Warren Buffett, Phil Fisher, these guys used simple math to make fantastic decisions. Concepts like Price to Earnings vs growth, and balance sheet valuation concepts. Stick to the valuation math principles and make better logic decisions in these uncertain markets. Cheers!
NYSE:AIEducation
by optionfarmers
4
TOP ASSETS of the AI NARRATIVE | PART 2In the comments of “Top AI assets part 1” you mentioned some more promising projects, the main product of which is AI. We decided to tell you more about them and check their metrics iExec RLC iExec is considered as a project with the AI narrative, but it is partly wrong. The main specialization of iExec is providing computing power and organizing the market around this sector. iExec forms large volumes of data and if we check their products, we will see that these volumes of data are being used actively but we have to understand that this is a side line of their business. In general, iExec as a project is more like Flux than any project in the AI narrative. Metrics of the $RLC token: Price: $1.75 ATH price: $11.6 Market.cap: $141m ATH market.cap: $800m FDMC: $152m Over the past 2 months, the $RLC token has grown more than 2 times. We do like iExec as a project with its own goals and values and that’s why we listed it on our platform for trading Vectorspace AI The team focuses on creating AI and ML solutions in space biosciences, general life science and capital markets. So far the team has launched two products: A financial product for protecting investment portfolios and finding stock and cryptocurrency market correlations for long or short trades. A product for biosciences in a Protein Relationship Networks area. Metrics of $VXV token: Price: $0.57 ATH price: $18.1 Market.cap: $27m ATH market.cap: $347m FDMC: $28m Over the past 2 months, the $VXV token has grown more than 2 times. Matrix AI Network Project that focuses on an AI integration directly into the crypto. Matrix has 4 main products: Mania - a platform for trading AI algorithms in an NFT type Airtist - a generative art creation platform for NFT Manta - an automatic machine learning platform Matrix - an AI service platform Metrics of $MAN token: Price: $0.02254 ATH price: $1.7 Market.cap: $4.8m ATH market.cap: $6m FDMC: $22.5m Over the past 2 months, the $MAN token has grown more than 4 times. Numeraire Platform for Data Science and Machine Learning specialists. Project supports DS and ML specialists, conducts predictive ML contests and builds its own progressive community. Metrics of $NMR token: Price: $16.7 ATH price: $84 Market.cap: $98m ATH market.cap: $487m FDMC: $183m Over the past 2 months $NMR has grown by 64% Streamr A project for data transferring within web3. Streamr is primarily an infrastructure project, preparing the basis for the data economy. Metrics of $DATA token: Price: $0.03308 ATH price: $0.3102 Market.cap: $25m ATH market.cap: $223m FDMC: $28m Over the past 2 months $DATA has grown by 50% Conclusion As we’ve told you earlier, the benefits that AI offers, along with its increasing adoption and application, guarantee the expansion of AI projects and a profitable market. Let us know in the comments about more AI projects we should look at. Share your investing or trading experience with such projects.Thanks for reading!
Education
by Samura0x
6
TOP ASSETS of the AI NARRATIVEThe release of ChatGPT into an open test version allowed everyone to use AI for their own purposes and needs. We were able to independently evaluate all of the benefits of AI. This trend has resulted in a surge in the number of projects in the crypto industry that use AI technologies. This idea is about the most effective and promising projects. FETCH.ai A product ecosystem in which the flagship products are: CoLearn – joint creation and training of a neural network Axim – combining and analyzing ML-based data Atomix – providing stable liquidity and getting profit from the income generated by the protocol Metrics of the $FET token: Price: $0.27 ATH price:$0.9475 Market.cap: $220m ATH market.cap: $708m FDMC: $930m Over the past 2 months, the $FET token has grown 4 times. SingularityNET A network of decentralized interconnected AI that can be combined to form a single AI that outperforms separate private components. Singularity NET is also a project incubator for AI-based projects in a variety of fields. Metrics of the $AGIX token: Price: $0.2 ATH price: $1.03 Market.cap: $235m ATH market.cap:$452m FDMC: $400m Over the past 2 months, the $AGIX token has grown 2 times Artificial Liquid Intelligence A network that aims to create a metaverse called Noah's Ark in order to preserve humanity's culture, history, and collective intelligence. AlethiaAI developed iNFT technology for AI-powered NFT avatar creation, animation, and generation Metrics of the $ALI token: Price: $0.036 ATH price: $0.12 Market.cap:$58m ATH market.cap:$86m FDMC: $362m Over the past 2 months, the $ALI has grown 3 times Ocean A data monetization protocol based on ERC-721 and ERC-20 data tokens. Any user can buy and sell datasets on the project's marketplace Metrics of the $OCEAN token: Price: $0.26 ATH price:$1.7 Market.cap: $164m: ATH market.cap: $322m FDMC: $378m Over the past 2 months, the $OCEAN token has grown 2 times ORAICHAIN Layer 1 blockchain for AI-powered data economy and Oracles. Oraichain combines AI and blockchain for innovation and, as a result, should revolutionize both directions to make them compatible and integrable. Oraichain focuses on providing decentralized platforms for data and AI, standardizing methods to validate AI-based calculations on the chain, and ensuring AI correctness. In the field of blockchain, Oraichain focuses on the scalability and compatibility of its oracle solutions and services with other networks in order to expand the usefulness of the Oraichain ecosystem Metrics of the $ORA token: Price: $4.24 ATH price: $102 Market.cap: $8.7m ATH market.cap: $77m FDMC: $83.9m Over the past 2 months, the $ORAI token has grown 4 times BitTensor Bittensor is an open source protocol that provides a decentralized blockchain-based machine learning network. Machine learning models are co-taught and rewarded in TAO based on the information value they provide to the team. TAO also provides external access, allowing users to extract information from the network and tailor its activities to their needs. $TAO is not traded on DEX or CEX; the only way to purchase $TAO is through the OTC market and transfer tokens to the polkadot wallet. Since the beginning of trading in $TAO in July, the price of the token has increased nearly tenfold Conclusion The advancement of AI adoption and usage, as well as the benefits that it provides, ensures the growth of AI projects in addition to the positive market. We implement and use AI for our operational tasks as a company that wants to be successful in the market and gain advantages. Write in comments your thoughts about mentioned projects and ways of using AI for you! Thanks for reading
Editors' picks
Education
by Samura0x
31
🌊 ELLIOTT WAVES CHEAT SHEET 🌊10 Rules to 🏄‍♂️ them all! Hello, You may have never heard of Elliott Wave Theory before! Here is a cheat sheet for Elliott Waves for top 10 Rules, so you can master them all! print this out and keep on your desk. How do you read Elliott waves? The Elliott Wave Theory is interpreted as follows: Five waves move in the direction of the main trend, followed by three waves in a correction (totaling a 5-3 move). This 5-3 move then becomes two subdivisions of the next higher wave move (fractal). The Elliott wave principle is a form of technical analysis that finance traders use to analyze financial market cycles and forecast market trends by identifying extremes in investor psychology, highs and lows in prices, and other collective factors. Ralph Nelson Elliott (1871–1948), a professional accountant, discovered the underlying social principles and developed the analytical tools in the 1930s. He proposed that market prices unfold in specific patterns, which practitioners today call Elliott waves , or simply waves. Elliott published his theory of market behavior in the book The Wave Principle in 1938, summarized it in a series of articles in Financial World magazine in 1939, and covered it most comprehensively in his final major work, Nature's Laws: The Secret of the Universe in 1946. Elliott stated that "because man is subject to rhythmical procedure, calculations having to do with his activities can be projected far into the future with a justification and certainty heretofore unattainable." The empirical validity of the Elliott wave principle remains the subject of debate. OpenSea version in signature below
COINBASE:BTCUSDEducation
by ArShevelev
15
So what is the METAVERSE?Think of it like building blocks of a new form of communication. Now in the vague sense, you could replace metaverse with cyberspace. And for you less technology versed people, think of it like "online" or "digital" a lot of the time the term will be referring to an array of various technologies. Like most things in life there are pro's and cons to this - whilst playing games with your friends or taking zoom meetings for work could be done face to face but not face to face using holographic technology, other elements of the metarverse will include things like NFT's, AI, AR and OBVIOUSLY Crypto along with other elements such as digital land. Think of it like a fake world - where you can social distance till your hearts content. As we transition into the METAVERSE; AI, AR as well as limiting and restricting our own travel. Monitoring each and every interaction let alone every transaction. So to over simplify the metaverse - think of it like the way we interact with technology rather than a specific technology. Evolution, a few years back a business partner bought me an Amazon Echo device for Christmas, I said to him what will I do with that?! I ended up with electricity going down for two days and realised how much I used it, I now have 11 Alexa's at home, lights, heaters, TV controls, surround sound, even heated beds. All controlled by Alexa. So much so, I can connect through the app of one of the cars and Alexa will tell me where the car is or how much fuel is left. So this all started long ago - imagine the game sims; You will soon be able to visit virtual galleries, play board games or online games with your friends like being in the same room. Go shopping and pay in crypto. Hang a digital Mona Lisa on the pretend wall that you can view when you stick your fighter jet helmet on. As we have seen with NFT's it has the potential for a big play overall, it's just a question of finding the correct problem to wrap such a solution. Whilst NFT's give a clear advantage to content creators by paying royalties throughout the process - with such high gas fees, the advantage is somewhat limited. At the moment we are going through the teething process in a very infant market. What can it become? I don't know. I wrote a post a few weeks ago on NFT's - be good to get your opinions on this topic too. click the link to follow through to the post. It al translates to a digital economy in essence. where people can create, buy, and sell goods. And, in the more idealistic visions of the metaverse, it's interoperable, allowing you to take virtual items like clothes or cars from one platform to another. ================================================================================================================== Pro's n con's of the MetaVerse; Ok as a pro - it's pretty cool technology and as I mentioned about my Alexa consumption, I am sure there will be practical applications for it. There of course will be completely pointless use cases also. This is where my first fear comes in; the definition of a market bubble. Then you read news like this... Tokens.com Corp, a Canadian investment firm focused on crypto assets, announced it had closed on the “largest metaverse land acquisition in history” through its subsidiary Metaverse Group, whose real estate portfolio spans several different virtual worlds and is reportedly worth “in excess of seven figures.” So the trick is clearly going to be how companies monetize their purchases and who's left carrying the bags? I can say it's going to be an interesting discussion with my grandmother "nan I bought a virtual painting for $2m to hang on a virtual wall inside my virtual mansion I paid another $5m for. Put your headset on and I'll teleport you over. OH hang on, I've been hacked or maybe the server just needs resetting" here response - "WTF you been talking about for the last 10 minutes?" ======================================================================================= So I guess to wrap it up - it's now going to be a race to create a viable business model to package up the hype. Disclaimer This idea does not constitute as financial advice. It is for educational purposes only, our principle trader has over 20 years’ experience in stocks, ETF’s, and Forex. Hence each trade setup might have different hold times, entry or exit conditions, and will vary from the post/idea shared here. You can use the information from this post to make your own trading plan for the instrument discussed. Trading carries a risk; a high percentage of retail traders lose money. Please keep this in mind when entering any trade. Stay safe.
COINBASE:BTCUSDEducation
by Mayfair_Ventures
8
Open Tutorial ⚪ How To Never Lose Money? "Losing is the part of the game." - said the loser and kept losing. Are you a loser? Or do you open your mind? Losers lose because they BELIEVE in their loss. They refuse to comprehend reality. In reality, you can't learn from failures. A loss is a loss. Nothing more. In truth, you can learn only from successes. But what if you only lost so far? Good news: It doesn't have to be your success. You can learn from the success of others. Let's specify an ideal strategy. The ideal strategy is never wrong. You don't have to know this strategy. It suffices if it exists. Somewhere. To someone. We experimented with pattern matching + AI a lot. Our theory: Wedges can approximate any strategy. You can draw wedges. You don't have to know an ideal strategy. Yet you can approximate it with wedges. Is it possible to learn this power? Not from a Jedi. What we know: It works on all major cryptocurrencies with USDT. +100% profit on BTC/USDT: It works on altcoins. +200% profit on XEM/USDT (x10): It works on cryptocurrency-cryptocurrency pairs. +300% profit on TVKBTC (x10): Thus, +100% success rate. More than +700% profit. All within a week. "One stoke, two halves." - said the winner and kept winning.
CRYPTOCAP:USDT.DEducation
by Elysian_Mind
3
High Probability Intraday Trade Setup for Gold FuturesThe following are trades setup ideas in 15 mins chart for Gold Futures. There are 2 distinctive dotted lines labelled as 1. AI Day Resistance 2. AI Day Support These 2 signals are generated by machine learning AI robots as a high probability trade setup where to long or short. If price action was above the AI Day resistance line AND price closed above Pivot Point R1 line, the ideas is to long and take profit at Pivot R2 line OR If price action was below the AI Day support line AND price closed below Pivot Point S1 line, the ideas is to short and take profit at Pivot S2 line Instead of relying on 100% discretionary (human) trading, the robots will provide trade execution plan and it is entirely up to the human trader's decision to follow.
COMEX:GC1!Education
by WolfeDayRocker