Economic Overview | The "Yellowstone Bubble"On Thursday, May 16th, I was sipping coffee and watching The Today Show , when a guest appeared on the program to talk about how much money YOU are supposedly making in your 401(k). Oddly enough the commentator - who was identified as the "chief business correspondent for CNN" - then reminded viewers that "you really should only look at your 401(k) once or twice a year"....
What?....WHAT?
My first thought: we don't need to be lectured on how often we should be checking on our retirement funds.
But this got me thinking, WHY do these "professional money managers" insist that working people not pay attention to their money??
I am speculating here, but I assume it is because retirement fund managers (large investment institutions) are also in the business of making money and therefore TAKING PROFIT.
Is there any evidence for this?... Well, yes:
Now factor in all of the nonsense that is constantly pumped by television commentators, meme stock pumpers, crypto fantasies, immature CEOs, and more recently - celebrities and professional athletes.
Have you ever stopped to think about the fact that there is a television commercial for $QQQ... Things have become so obscene that money managers are paying for airtime to deceptively lure regular people into buying their securities, so they can take profits, after already receiving bailouts. You've seen it, there are several versions of the same commercial and the narrative goes something like "I'm investing in QQQ for the future".
The Unemployment Rate has bottomed - there is no more growth to be had and even if we were to see unemployment trend below 3%, we can go back to the early 1950s and 1960s to see that financial markets really DON'T return much more below 3% unemployment; again this is because there is no more growth below 3% and therefore marginally less return.
Credit card delinquency is rising rapidly, thanks to inflation from Covid helicopter money.
And Household Debt-to-GDP has also bottomed. This one is particularly concerning because as we just explained, there is no more growth to be achieved from here (UNRATE). So, ask yourself: what happens if GDP falls ? Answer: household debt as a proportion of GDP rises by at leas that amount (it's a ratio - it has no choice). Expanding on this question, ask yourself: what happens if household debt continues to rise, amid maxed out unemployment? Answer: the already record profit-margins of investment banks increase to highly unstable levels, thereby further incentivizing profit-taking.
Anyway, I am calling this market the Yellowstone Bubble . Everyone is a rich tough-guy cattle rancher, everyone is a crypto professional, everyone thinks "Tesla is the future" (LOL), everyone is an AI expert, everyone is a pro because they scroll forums and listen to some podcast.
In a world that runs on "users" and "clicks" and web traffic, you must remain vigilant!
Take care!
Artificialintelligence
OpenAI’s search engine: Implications beyond GOOG stockOpenAI has denied rumors claiming it's set to reveal an AI-powered search engine for its flagship chatbot, ChatGPT, on Monday.
Although there is speculation that OpenAI is being a bit of a wise guy, and the product it will demo is not a search engine, but what they might dub as the next generation of search engines.
It will be interesting to see if this product, whenever it is first demonstrated, is anything Google will have to worry about. According to Bloomberg, OpenAI’s search tool will be partly powered by Microsoft’s Bing search engine. Make of that what you will.
Either way, this could likely ratchet up competition with Google and the market might have already begun to price in this risk to GOOG stock. But what other stocks could face a negative or positive risk from this development?
Maybe we can ask ChatGPT:
“Online advertising companies: Companies that heavily rely on Google's advertising platform for revenue could face challenges if advertisers shift their budgets to the new search engine. This includes companies like The Trade Desk (TTD), Magnite Inc. (MGNI), and Criteo S.A. (CRTO).
E-commerce platforms: Google's search engine is a major source of traffic for many e-commerce platforms. If a rival search engine gains popularity, it could impact the flow of traffic and potentially affect revenue for companies like Amazon (AMZN), eBay Inc. (EBAY), and Shopify Inc. (SHOP).
Payment processing companies: Google's search engine facilitates e-commerce transactions, which benefits payment processing companies like PayPal Holdings Inc. (PYPL) and Square Inc. (SQ). If a rival search engine affects e-commerce traffic patterns, it could indirectly impact payment processing companies.
Digital marketing agencies: Companies that specialize in search engine optimization (SEO) and search engine marketing (SEM) services may see a shift in demand if advertisers allocate resources away from Google towards the new search engine. Examples include companies like WPP plc (WPP) and Omnicom Group Inc. (OMC).
Data analytics companies: Google's search engine generates vast amounts of data, which is valuable for analytics purposes. If a new search engine captures market share, it could impact data analytics companies that rely on Google's data for insights. Companies like Nielsen Holdings plc (NLSN) and The Dun & Bradstreet Corporation (DNB) could be affected.
Online review platforms: Google's search engine plays a significant role in driving traffic to online review platforms like Yelp Inc. (YELP) and TripAdvisor Inc. (TRIP). A new search engine could potentially change the dynamics of online reviews and impact these platforms' user base and revenue streams.”
AAPL in Risk of Bear MarketApple is having a tough period with weakening China demand, lack of growth and innovation, AI lag, regulatory challenges that threaten its lucrative walled garden business model and other headwinds. These challenges weigh on the stock, which shed more than 10% in the first quarter. Along with Tesla, they were the only stocks to fall, among the Magnificent Seven.
The situation deteriorated further in April, as AAPL hit the lowest levels in a year and is now in risk of a bear market . Moves below $160 would mean losses of 20% and more from the December record high, which is generally viewed as the threshold for a bear market.
On the other hand Apple is still one of the most valuable companies in the world and investors are unlikely to give up on it and there are reasons for optimism. iPhone sales have shown resilience and the smartphone market is poised for a rebound. Its CEO appears determined to not let China fall, but also looks to India, which has significant untapped potential. Apple also launched the Vision Pro AR headset this year, looking for an early entry to a nascent market, while AI progress could be showcased soon.
AAPL is having a good week and although we could see further rebound, the upside contains multiple roadblocks. Closes above the EMA200 would be required for the downside momentum to halt.
The stocks trajectory will be influenced by the upcoming earnings report, which is due on May 2. Top and bottom lines, China & India performance, guidance and AI progress, will be some of the focal points.
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Past Performance is not an indicator of future results.
Why did HPE Breakout?As shown on the one-hour chart, in the last trading session, HPE broke out of its usual trading
the range being the blue high-volume area on the profile. This is with increased volatility as
shown by the indicator and the large top wicks on the rising green candles. Why did this
occur? Were traders simply buying anything in the IT sector vaguely related to AI after the
NVDA breakout? Does HPE have a role in artificial intelligence? Was this a sympathy play?
The Luxalgo Supply / Demand indicator shows supply immediately overhead. The wicks on the
last several candles show a defined level. This might be called a " tweezer top " Overall,
I see this as an excellent short setup to be played with either short selling or a put option as the
retracement seems inevitable.
NUNET #NTX AI Crypto SEXY chart , sexy category DYOR
from my 2 mins of research ;
I doubt a real usage of graphic card buying and renting going on through this platform
A SCAM if u will
but that really doesn't matter for the price going forward if you only plan to ride a up wave... and not marry the bag.
Maybe one of these platforms will actually be used to provide decentralised compute
RNDR, GPU right ?? are those real? who knows
like I said doesn't matter
the chart looks good
So that's why I am sharing it.
SoundHound AI $SOUN - Artificial Intelligence is on the rise!SoundHound AI - Everybody is talking about Artificial Intelligence (AI). The social media buzz is all about AI. The world is asking questions like: What are the possibilities? How far will AI go in its reach into day-to-day society? Meanwhile, AI stocks like NASDAQ:SOUN are becoming more and more popular. If SoundHound's stock price makes it to the $9.00 key zone, it will be 100% away from the all-time high. Can the media buzz propel SoundHound AI to new heights?
PLTR pre-earnings play LONGPLTR has earnings on February 5 while on the 120 minute chart, the price action is that of
a rising wedge with price compressing between a rising support trendline and a falling
resistance trendline the extension of the neckline of the head and shoulders pattern of
November. PLTR fell today and is near and above support. In a long trade, I see the target
as 18 ( at the resistance trendline) with a narrow stop loss just under the support trendline.
This makes for a possible 6% profit with a very good reward-to-risk ratio. A call option
for the 2/16 expiration striking $17 is also under immediate consideration.
♨ Nvidia stocks are heading Up to recover, after September meltNvidia stocks moved higher in early Monday trading after analysts at Goldman Sachs NYSE:GS added the chipmaker, along with three other stocks, to its flagship list of stock recommendations.
Goldman Sachs analysts added Nvidia to the bank's "Americas Conviction List", a step up from the 'buy' rating it assigned to the stock in late August, while holding its price target in place at $605 per share.
"Look for Nvidia to maintain its statues as the accelerated computing industry standard for the foreseeable futures given its competitive moat and the urgency with which customers are developing and deploying increasingly complex AI models," Goldman argued.
The bank also added cybersecurity group Okta NASDAQ:OKTA , industrial supply group Cintas NASDAQ:CTAS and biotech Quanterix NASDAQ:QTRX to the "conviction buy" list while removing Salesforce NYSE:CRM and Johnson Controls NYSE:JCI .
Nvidia, the world's biggest AI chipmaker, forecast current quarter revenues of around $16 billion in August when it published stronger-than-expected second quarter earnings and later unveiled an make it easier for clients to run AI applications on Google Cloud NASDAQ:GOOGL using Nvidia-made chips with deeper integration between hardware and software offerings.
"We’re at an inflection point where accelerated computing and generative AI have come together to speed innovation at an unprecedented pace," said CEO Jensen Huang of the Google agreement. "Our expanded collaboration with Google Cloud will help developers accelerate their work with infrastructure, software and services that supercharge energy efficiency and reduce costs."
Nvidia shares were marked 3% higher in early Monday trading to change hands at $ 448 /share. The stock is up more than 200% for this year, and reached an all-time high of $487.84 on Aug 29, 2023.
Technical picture says, Nvidia NASDAQ:NVDA stocks are still on its positive path, and trading above 6- and 12-months simple moving averages.
Moreover the key breakout of technical indicator known as "a Triangle" is happening right here as stocks are recovering form the bearish hug.
SNOWFLAKE breaking long time resistanceThere is a multi year resistance around $205 for NYSE:SNOW
Signs I'm looking for:
Top of channel to become support, a bounce off there and a move into $220 should confirm that.
I want to see the SuperTrend indicator stay green, upwards of the level of where the red downtrend line exists.
SuperTrends on higher time frame charts work the best. It's often pretty solid when used on individual stocks rather than an index.
Take a look at the supertrend (strategy) and mess around with different time frames. You'll see the cumulative return is very high, often much higher than just buying and holding the equity.
Let me know what you think : )
AI in trading - 6 hottest topics (part 2/2)Alternative Data
ADs provide a better picture of a company's situation, raw materials, currencies. It also allows us to assess the "current state" (nowcasting) of significant indicators. Those data make trading signals better, more precise, less risky and more profitable.
It is a revolution accompanying the AI revolution and even preceding it. In my opinion, it is more important today than AI, which is only in its early stages (despite many impressive achievements). In my opinion, through AD, funds can earn more and build their competitive advantage over others.
ADs are not part of Artificial Intelligence. An example of AD is credit card sales data. This data can be used to predict the financial performance of companies. If we have historical data, then in the simplest case, to make forecasts, all we need is a spreadsheet!
And when we are interested in more advanced indicators of future profitability, such as consumer spending patterns, brand loyalty, switching between products/brands, trending moods, competitors performance, models created using Machine Learning can come into play.
With the increasing number of data sources and the complication of forecasting models, traditional ones will be replaced in a considerable part or even entirely by AI/ML-based models.
For a broader discussion of Alternative Data, see the separate article in this issue.
Visualization
It is easier for humans to look than to think. "Analysis" by sight developed long before abstract thinking.
There is something severe behind this remark. It is much easier for us to understand a situation when it is shown using images rather than just a verbal description. Therefore, as much as possible, use visual aids - graphics, pictures, diagrams, or charts to illustrate data, situations and processes.
Indeed, it is good practice to consider what goal we want to achieve, define the target group and identify which parts of the message will benefit from such enhanced presentation. The same applies to respecting the simplicity of the message, playing with colours and ensuring maximum readability.
Another good practice is to provide a benchmark, or reference point, to which we compare some quantity. Our mind performs better by observing the differences between some benchmarks and the current indication.
An excellent practice is to make it easy for the audience to understand the situation quickly. Thus, when preparing visual aids, try to help them understand the situation as quickly as possible – for example: whether we are in the realm of "normal" or have already gone beyond it.
All key, critical processes should have some sort of graphic representation. It should allow for a quick assessment of the situation, especially in unusual or crises. So let’s say I give you a colour scheme, where green means everything is going well, orange – attention required, and red – we have a critical situation. Sound familiar? It should.
As AI matures, the amount of information and complexity of systems (and portfolios) will only increase. Therefore, using standardized metrics within a company to illustrate key processes is something worth developing as a valuable skill.
Let me say it another way to emphasize the particular importance of this topic - the ability to graphically present important processes for a company is a competence worth developing. It is worth discussing what indicators to use, what types of graphs, what colours, and what schemes to facilitate and enhance understanding, ability, and speed of decision making.
Visual communication is one of the essential elements of building and consolidating a company's structural intelligence.
Automatization
Automatization is the critical process underlying the use of artificial intelligence.
It involves gradually learning and automating more functions of human intelligence. The ultimate stage of AI development in trading is full machine autonomy with a level of perception, "thinking", decision-making far exceeding human capabilities in every aspect.
What does this mean for traders and funds now and in the future?
Now
Today, automatization is one of the main topics because it takes the burden of routine activities and responsibilities off the shoulders of traders. One of the main problems that traders complain about is excessive workload and information overload.
The primary candidates for automation are routine activities that require no intellectual input. And over time, more and more activities will be automated - and more about that in a moment.
Suppose we have a great trader. Only some of his activities add value, and he should focus on them. You can consider using supporting programs or someone else to help with the remaining tasks.
What should not be automated are non-routine decisions, decisions in exceptional or critical situations and those requiring synthetic expertise beyond the reach of AI tools.
Instead, you can automate the execution of decisions in critical situations with confidence.
In an extreme situation, the trader only presses the appropriate key. A program then tries to escape from the market as quickly as possible. It tries to use liquidity, reduce costs and minimize the negative impact of the large order it exits. In nine out of ten situations, it will do this better than the trader and, in the case of substantial orders, in ten out of ten.
Automatization will expand to include more and more activities, including non-routine ones, over time.
In the future
To understand what automatization in a fund will look like in the future, we must first learn the decision-making process of a discrete trader or automated system.
The decision-making process consists of all the elements that lead from the initial analysis (what to trade and where to trade it) through the choice of location, entry, position management, exit, to post-trade analysis.
There can, of course, be many more of these steps if we take a more detailed approach (and the largest funds do).
Automatization here is about taking a single element of the decision-making process and trying to refine it first (to find the best practices) and then automate it.
It would also be beneficial to provide a feedback channel so that we and, in time, the AI system can improve this element based on the incoming and analyzed data. In short, we want the system to learn on its own.
In short, we automate best practices at each step and provide feedback so that the system learns and improves.
On the other hand, entry automation may involve breaking positions into smaller ones, examining order structures above and below, creating and executing entry strategies to minimize cost and adversarial price moves. Hiding positions and maximizing positions for the best signals may also be part of the automation.
Summary
We have discussed six of the "hottest" topics currently occurring in the Artificial Intelligence field. Two are sure to be the most important: XAI and Alternative data.
The first - because it opens up a powerful new trend of adjusting the latest tools to a trader's level of understanding. We already know that a gradient descent on a differentiable manifold tells him nothing. The second - because it is alternative data that gives traders and funds their main competitive advantage today.
In conclusion, it is worth repeating one important thought: the AI revolution is just beginning. It will completely change our world and ways of investing. This process is incredibly fascinating. The New City Trader was born out of a desire to share this fascination.
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AI in trading - 6 hottest topics (part 1/2)In this article, you'll learn about six of the most critical and "hottest" elements that make up or are associated with AI today. In addition, you'll learn the basics of the tools that will shape the future of trading. First in the biggest and wealthiest funds and then in smaller ones as well.
I invite you to take a journey into this near and far future of trading.
NLP
Natural Language Processing is the common name for many tools to analyze written and spoken text: company documents, press articles, news, analysis, web pages, social media posts, company’s product reviews.
Advanced NLP software recognizes context up to about a thousand words. That's a lot, and soon, there will be more.
NLP allows you to analyze many features of text, such as:
- whether the text about a particular company is positive or negative,
- whether it is clear and transparent or obscure and convoluted,
- whether the authors express themselves positively or negatively about the future.
When we analyze texts of reports and press statements, it turns out that all of the above elements can be a good indicator of future financial performance.
Social media texts
Already now, the analysis of posts in social media and online shops allows determining the sentiment - the opinion about the company and its products, which usually precedes the financial results.
Sometimes it is also possible to find and analyze the sentiment of different investor groups about the company and its future which also affects the share price.
"The stripper that will change our World"
The next gigantic step in the evolution of NLP come from extracting written knowledge from millions of books, academic articles and other texts and help create coherent theories of how the economy, or supply chains, works.
This development will give theoretical and practical insights into the factors that affect the financial performance of companies, industries and all relevant economic processes. Already today, we see the first signs of the creation of such tools.
Spoken text
NLP also covers spoken word analysis: statements from TV news, films, other video material, or telephone conversations are automatically transcribed and subject to the same analysis as written content.
Thanks to this, we now have access to knowledge about the level of Forex volume. The systems analyze the volume at major banks and brokers and traders' volume over the phone. Until last year, data on this was not available in real-time. I will find out if anything has changed yet and write about it in future issues. I know that there were plans to provide this volume in real-time as well.
Machine Learning
Machine learning is dozens of tools for machine problem-solving.
But today is different than you might think. We are in the first phase of the evolution of these tools. To understand this and to understand their potential, I will give a practical example: how does solving a problem using machine learning tools look like...
In simple terms:
1. the process consists of problem formulation and preparation of a mathematical model (specialist),
2. further collection and preparation of data (specialist),
3. selection of one of the ML solutions (specialist),
4. feeding the software with data (specialist),
5. data processing (software),
6. finally, we have the interpretation of the obtained result (specialist) - someone has to explain the result in non-mathematical terms.
Only one element of this sequence is automated - the fifth. All the rest requires the use of specialists' knowledge and experience.
Today, the real driving engine of AI is... the specialists. And it will remain so for a long time to come. "Real AI" is still very, very scarce.
Over time, each step will be done automatically. Only then will we see the true power of machine learning and AI. We are at the beginning of this journey, the first stage of evolution (and I believe there will be five).
I didn't want to start this thematic series by describing ML tools. I preferred to show their current place in general. In the future, I will describe some Machine Learning solutions and how they are used to create trading systems. I will also give examples of such systems so that you can form your own opinion about them.
What is worth knowing is that despite the impressive achievements of AI-related technology, this is just the beginning of this revolution.
It will change everything we know.
We are only at the beginning of the AI revolution. It will change everything we know.
XAI or Explainable AI
It is currently probably the hottest topic in AI.
Some ML tools are so complex that we don't know how the machine got the result, how it made the decision or the recommendation.
We call them "black box" for short - it's dark inside, and we don't know what's happening there. Nevertheless, the math behind it is excellent, and the results are often astounding.
So, we have a result, but we don't know how it was achieved. We don't know because the process leading to the development is very complex and has many steps. And if we don't understand "how it works", then several problems arise. I will describe them for the case where we have a black-box that gives input and output signals:
- beyond simply allocating a small % of capital to the position, risk management becomes problematic;
- we have little or no control over the position (except for the exit);
- we don't get the most out of a tool we don't trust. And this is a problem when we have spent several million in its creation;
- we don't know if a given series of losses is temporary because the market has changed, or maybe the system has stopped working for a given market. So it will only lose from now on.
And since the results are good, we will try to explain how it works in one way or another. The problem of finding an explanation and education for exploiting the potential of AI will also run through the following issues.
For a fund that employs traders, this problem is as practical as it gets.
A trader in his seventies would like to know how much money can be made on a "black box."
For example, let's take a trader who is 75 years old, active and, on top of that, a co-owner of a fund. And he would like to find out how "this new thing" works because it may be worth increasing the capital that this "new toy" has to use.
But how, without knowing what's going on inside, define the trading framework? What risks to assume, a reasonable capital commitment, when difficulties arise, and what to do when they do?
Moreover, after all, we have to adapt to the boss's scope of knowledge and experience. Thus, for example, we cannot start the lecture with the geometry of differentiable manifolds and Kullback - Leibler divergence for probability distributions (such mathematics can be used there) if he has no idea about it.
It is a fascinating problem. Important enough that we are preparing for publication a broader article on this topic: how to explain and help traders understand new tools, in particular black-boxes. How to estimate risk, build confidence, define a framework in which they will feel safe with the new device.
Someone who has an easygoing boss already thinks they can relax and not bother explaining the operating principle of their wonderful black box. But, unfortunately, this is not the case because other people on the horizon would like to know how it works.
There are several groups of such people.
Those who want or need to know what's going on inside
The first would be the law regulators and the courts. The Financial Supervisory Commission may want to know if, by any chance, the recent large positions, as claimed by us placed by a "black box", are not an instance of insider trading.
If you have a similar idea of defending yourself in court (from being accused of insider trading), then know that it makes no sense. We may not know what is inside, but the signal must appear again after simulating the conditions created by the signal.
Then we have the risk management department, which would also like to know how it works or at least what it resembles. All they have left is to allocate a small amount of capital to the signal.
A black box position is like a plane without windows. We take off on command, fly with no way to tell where we are and land on command. The only assurance of safety is the statistic that, for example, a position is profitable six times out of ten. This value means that we have four hard landings per every ten take-offs. It is a moderately comfortable situation, although in some cases, it will suffice.
Has the system already stopped working?
Now, we have something even less pleasant: if we do not know the rules of decision making, we cannot be sure that a given series of losses is not the end of the system because the market has changed and the previous rules no longer work. Exaggerated? Maybe, but only a little.
The use of various AI tools will only grow, including black-boxes, and this has to be dealt with one way or another. The major funds already have some prescriptions for what to do. In future articles, I will describe them.
The topic is even more important because, for the vast majority of non-mathematicians, i.e. traders, portfolio managers, C-level managers, practically every AI/ML tool is a black box. For some reason, explanations such as dealing with a multidimensional, differentiable manifold immersed in a vector space do not help.
Explainable AI fits into a broader trend - most people and traders have no idea what new tools do.
There is a great need to explain to users how AI/ML tools work, what they provide, their limits of use and when they stop working. Education is vital because a fund's competitive advantage will soon be created at the interface between the team and the tools, AI systems.
Competitive advantage in the future will depend on the so-called structural intelligence of the company. The largest funds are already working in this direction, although they can not name it so cleverly. We will also devote quite a few articles to this in the future, which is one of this magazine's goals.
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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.
AI Alchemy, The Future of InvestmentsArtificial Intelligence (AI) is no longer just a futuristic concept or a element of science fiction. A revolutionary transformation in technology has propelled AI into a leading force across various sectors of human life. In this context, looking ahead is not just a trend but a necessity. From healthcare to industrial automation, AI is becoming a key element in enhancing our quality of life and providing solutions to complex problems.
For example, in healthcare, AI can speed up diagnostic processes, assist in drug research, and improve the efficiency of medical care. In the industrial sector, AI automation can boost productivity, optimize supply chains, and reduce production costs. It's no wonder that AI companies are attracting investors looking to be part of this significant change.
Investing in AI Companies as the Top Choice
Incredible Growth Potential: AI companies offer incredible growth opportunities. With the increasing adoption of AI technology across various industries, these companies can experience significant long-term value appreciation.
Inevitable Innovation: Innovation is the key to success in this digital era, and AI companies hold the most strategic position in creating revolutionary technology. By focusing on developing smart algorithms, these companies can lead in creating new solutions and enhancing competitiveness in the market.
Social and Economic Impact: AI is not just about business and financial gains. The changes brought about by AI have the potential to create significant social and economic impacts. Investing in AI companies supports a vision of creating a more efficient, sustainable, and adaptive society.
Looking Ahead: How AI Shapes the Future
Smart City Development: AI will play a central role in developing smart cities. With systems that can monitor and manage traffic, energy, and public services, smart cities will become the norm in the future.
Enhanced Education Quality: AI can be integrated into educational systems to provide personalized learning experiences, assist in evaluating student progress, and create responsive curricula.
Energy and Environmental Revolution: AI solutions can be utilized to optimize energy usage, manage waste, and develop green technologies to address climate change.
Healthcare Technology Advancements: With AI, the healthcare sector will undergo a revolution with early diagnosis, personalized treatments, and more effective drug development.
Conclusion:
Looking to the future is not just about predicting trends but understanding how technology will shape our lives. Investing in AI companies is a smart move because it not only yields financial benefits but also supports positive changes in society and the environment. By researching and understanding the impact of AI across various life sectors, investors can make informed decisions and build resilient portfolios for this future full of potential.
Artificial Liquid Intelligence (ALI)Artificial Liquid Intelligence is somewhat of a new AI project, with respect to other well-known artificial intelligence projects. Anyway, it seems ALI is about to repeat its previous move; a descending wave followed by price breaking the curved downtrend line, then a sharp upward move. Let's see if ALI repeats this or not.
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.
Top 10 AI Stocks to Trade and add to Trading View WatchlistAI is definitely one of the key words for the century.
And yes, I believe these are great companies to add to our watchlist to trade. ANd Trading View has all of the companies to analyse their movements. .
We could even see AI companies being some of the safe-haven stocks to invest in 2024…
Here are my top 10 companies that are incorporating AI into their businesses and ones I'm trading lately.
1. Microsoft (MSFT):
Develops, licenses, and supports software, services, devices, and solutions.
2. Advanced Micro Devices (AMD):
Designs and sells computer processors and related technologies.
3. NVIDIA (NVDA):
It designs graphics processing units (GPUs) for gaming and professional markets.
4. Palo Alto Networks (PANW):
Offers cybersecurity solutions and firewall technology.
5. Customer Relationship Management (CRM):
This is a strategy that companies use to manage interactions with customers and potential customers.
6. Meta Platforms - formerly Facebook – (META):
Operates social media and virtual reality platforms (e.g., Facebook, Instagram, WhatsApp, Oculus)
Note: Oculus 3 Headset is coming out next year and it’s going to include and introduce Augmented Reality to the world.
7. Palantir Technologies (PLTR):
Develops data analysis software and provides data integration and analytics platforms.
8. Adobe Inc. (ADBE):
Creates software products for content creation, multimedia, and marketing.
9. Apple Inc. (AAPL):
Designs and markets consumer electronics, computer software, Virtual Reality and online services.
10. Micron Technology (MU):
Micron Tech. inc. designs, develops, manufactures, and sells memory and storage products worldwide
I have an entire watchlist just saying AI STOCKS...
There isn't an Index yet, so I'm watching them and trading accordingly.
Analyzing a Potential Bearish Channel: BTCUSD Medium-Term IdeaDear TradingView Community,
I share a medium-term outlook with you all. Please bear in mind that this prediction is subject to short-term price fluctuations, and its outcome hinges on how various technical factors align. Our AI system, having drawn insights from deep neural network analysis, has detected the potential emergence of a bearish channel pattern.
O n the chart's left side, you'll find a historical record of the linear regression algorithm's past accuracy. These patterns held on the given dates. However, since October 23, 2023, we have observed the formation of a possible rising channel pattern, which typically indicates a bearish trend. It's essential to remember that historical results don't guarantee future returns of investments.
S o, what technical indicators support the notion of a bearish channel pattern? Notably, the volume has declined since the mentioned date, hinting at consolidation or more. Historical data has shown that volume patterns often align with channel formations. It underscores the importance of the volume indicator about the channel pattern idea.
F urthermore, on November 01, 2023, many traders opened short positions, leading to a market shakeup that resulted in numerous liquidations within 12 hours. In chart analysis, channels frequently feature candles of various colors, as illustrated by the colored boxes on our chart. If we encase the 4-hour candles since the initial date within a similar rectangle, we already see a diverse range of candle colors. This candle analysis complements our bearish outlook.
I f the bearish channel materializes and Bitcoin (BTC) begins a descent on the BTCUSD market, standard patterns suggest the price could drop below $32,000 (marked by the horizontal white line). It might dip below $31,000 (indicated by the dotted horizontal line). While various indicators align with the possibility of a rising channel on the charts, it's crucial to recognize that this pattern is not confirmed yet. There's a chance that we may not witness this pattern at all in the coming months.
T herefore, I encourage caution and prioritizing the safety of your existing funds over aggressive day trading. Your financial security should always be your primary objective.
tl;dr
Position: Short
Target Price: $33000-$31000
Possible Pattern: Rising Channel
Near Trend: Bullish
Medium Trend: Potentially Bearish
Indicators: Candles, Volume, Tops and bottoms, Trendlines, Liquidation Metrics
Important: Don't forget your stop loss and trail profit if you decide to put any positions.
Have any questions? If you ask, we answer.
Warm regards,
ELY
AI-Assisted Channel Patterns: Visuals for Precision TradingTypes of Channel Pattern
In this educational post, we won't take a trading position, but rather equip you with valuable insights. Today, we delve into the world of channel chart patterns. Channels come in two primary forms: bullish and bearish. Understanding these patterns is essential. A bullish channel appears as a descending pattern, resembling a falling rectangle, while a bearish channel manifests as an ascending pattern within rising rectangles.
Technicals of Channel Patterns
But why are these channels so important? Bullish channels often precede a shift from a bearish trend to a bullish one, signaling a shift from a pessimistic to an optimistic market outlook. Conversely, bearish channels frequently herald a move from a bullish trend to a bearish one, indicating a transition from an optimistic to a pessimistic market sentiment.
Application of Channel Patterns
Channels serve various purposes, from brokers illustrating their expectations to traders preparing for upcoming trends. They also offer an excellent opportunity for automation, as modern AI systems can detect channels with remarkable precision, often exceeding 70%.
Our Notes to Channel Patterns
However, it's worth noting that channel patterns are seldom used in isolation. To make the most of them, traders often combine AI-assisted channel detection systems with volume analysis. When analyzing BTC-USD markets across nine exchanges and over five years, we found that volume frequently aligns with precisely defined channel patterns.
By incorporating volume as a technical indicator and leveraging AI-generated channels, you can enhance your trading strategies and increase your chances of success in the cryptocurrency markets. Best of luck in your trading endeavors!
Best regards,
ELI
#NMR/USDT 4h (Binance Futures) Descending wedge breakoutNumeraire regained both 50MA & 200MA and is pulling back to them, looks good for another bounce.
⚡️⚡️ #NMR/USDT ⚡️⚡️
Exchanges: Binance Futures
Signal Type: Regular (Long)
Leverage: Isolated (4.0X)
Amount: 6.9%
Current Price:
13.13
Entry Targets:
1) 13.10
Take-Profit Targets:
1) 15.94
Stop Targets:
1) 11.68
Published By: @Zblaba
$CRYPTOCAP:NMR BINANCE:NMRUSDT.P #Numeraire #AI numer.ai
Risk/Reward= 1:2.0
Expected Profit= +86.7%
Possible Loss= -43.4%
HOW TO: Use ChatGPT as your Trading-Coach & MentorIn this video, I explain how Traders can use ChatGPT 4 as their Coach and Mentor to accelerate their journey of becoming highly successful and professional traders.
ChatGPT's new image functionality enables this AI tool to help traders to transform their learning curves and build their capabilities at an unseen pace.