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Cloudy☁️ (Confidence: 0.41 )🌥️ Based on the Bitcoin chart index for the past hour, I forecast cloudy weather with some fluctuations ☁️ The confidence that the weather in the Bitcoin world will be sunny is quite low, only 0.41, which is less than the baseline of 0.864. 🌡️ The Close value is lower than the Open value suggesting a bearish trend, and the RSI of 44 and MACD of -6 confirm this notion. 💹 The EMAs are also lower than the previous levels, indicating a downward trend. With the Fast K and Slow D values also being low, it might be a good idea to keep a close eye on the market and wait for a more opportune time to invest in Bitcoin. ⚠️
BINANCE:BTCUSD
by ChatGPT_Bitcoin_Forecaster
MSFT - take profit before earnings?NASDAQ:MSFT reports after close this Tuesday. Everything that I see suggests that price is at an inflection point, and likely due to pullback. I expect that this earnings release will need to be a beat, communicate progress on AI, and provide strong forward guidance for additional upward continuation in the near term. While it's still 19% off of its all time high, MSFT has had strong performance YTD. It's been a clear favorite in the AI narrative with its investment in ChatGPT and potential for integration across its products. Anyone dismissing AI as a fad needs to sit back and look at the big picture. It's coming at us quickly and will be transformational. Every tight labor market in economic history has been followed by adoption of workforce productivity and automation. The TA suggests that MSFT is over extended in the short term. It's far above its 55EMA, RSI shows recent bearish divergence, MACD and leading MACD have rolled over, and price has been hovering at the 0.5 fib retrace from ATH and dense volume profile. In addition, we can see that recent price movement is supported entirely by short term inflows as long term positions have gradually drawn down. The longer term channel doesn't provide as much clarity, but any number of ways that I draw the fib time zones indicate that this is a time period for a shift in sentiment. While this isn't a company to bet against, I'm planning to take some profit prior to earnings and will look for opportunities between FWB:250 -265 for accumulation. A sustained movement above $295. would suggest further upside continuation.
NASDAQ:MSFTLong
by Ben_1148x2
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
Generative AI: are we witnessing an iPhone moment?Is ChatGPT the ‘iPhone moment’ for artificial intelligence (AI)? The iPhone catalysed many different things all in one device: It is hard to remember a time ‘before smartphones’, but the iPhone was only introduced in 2007. Apple is one of the world’s largest companies by market capitalisation, and there are many companies that make smartphones that are also quite large. It is hard to remember a time ‘before apps’, but there is now a so-called ‘app-economy.’ Spending in iOS and Android apps in 2022 is estimated to be almost HKEX:130 billion1. Why did the iPhone so successfully change the world? It combined an aura of excitement with an incredibly flexible set of potential future capabilities and an ease of use that competitors in 2007 struggled to match. Today, the iPhone is not even really a device, but rather a key into an ecosystem where many different services can be consumed. Will ChatGPT become something similar? It is a bold prediction to say ChatGPT is the next iPhone. It would mean OpenAI has a chance at becoming the world’s most highly valued company by market capitalisation and to stay in that position for numerous years, similar to what Apple has done. Apple created both hardware and software. It is likely that if Apple focused solely on hardware or solely on software, it wouldn’t have been as successful. Microsoft vs Alphabet In 2023, ChatGPT is software, and it is also clear the world’s largest companies see the potential value and are acting to capitalise on their slices of the economic pie. Microsoft has been the most direct, investing around HKEX:10 billion directly into OpenAI and indicating the technologies underpinning GPT-4 will be incorporated into programs, like Office 365. If Microsoft charges a small amount more for the Office 365 subscription that includes access to GPT-4, this could equate to tens of billions in incremental annual revenue2. Alphabet, through its Google platform, is seeking to create its own version of ChatGPT, but it does not seem to be moving as quickly as OpenAI, possibly due to the ‘innovator’s dilemma’, in that no other company has a greater than 90% market share of internet search, so it is tough to imagine Google desiring a new way of doing search3. Microsoft CEO Satya Nadella was masterful in creating the perception of a possible ‘search war’ before any behaviours actually shifted away from Google search. Both companies will deploy generative AI into their cloud services, seeking to invigorate growth in this important part of their revenue stream after 2022 posted slower growth than had been seen in prior years. This too could add tens of billions of dollars in incremental annual revenue. AI’s pie of economic value On a recent episode of the Lunar Society podcast, posted on 27 March 2023, Ilya Sutskever, OpenAI’s Chief Scientist, noted AI is going to be increasingly more valuable year after year, and that this could continue exponentially4. Who will capture all the value created by AI? Being involved with both software and hardware elements, as well as the app store, Apple captured lots of different slices of an ecosystem catalysed by the introduction of the iPhone, as well as maintained its staying power as the environment continued to evolve. In the case of generative AI, the technology that underlies such systems as ChatGPT, we see a catalyst for increased demand for cloud computing. It is not coincidental OpenAI has a significant relationship with the world’s second largest provider of cloud computing infrastructure with Microsoft’s Azure where AI models of this size can be efficiently trained, stored, and run. All of the cloud infrastructure players, be it Amazon Web Services, Microsoft Azure, Google Cloud, Oracle, and others, are surely seeking to create an Apple-like ecosystem that is not ‘friction free’ for a user to shift from iOS to Android. The more value-added services provided, the less likely customers would be enticed to switch their provider or bring more of their workload back on premise. They want cloud customers to get used to the cloud computing equivalent of air pods, the i-Watch, Apple TV, etc. The largest tech-oriented companies in the world are also ramping up their investments5: In 2022, Alphabet, Amazon, Apple, Meta, and Microsoft spent HKEX:223 billion on research and development (R&D), up from $109 billion in 2019. This was in addition to $161 billion in capital expenditures (CAPEX)—which represented a tripling in three years. These five companies were spending roughly 16% of their annual combined revenues on R&D and CAPEX in 2015, which had risen to 26% in 2022. Meta indicated AI is its biggest investment category and Alphabet is planning to reveal the size of its AI investment for the first time as part of its Q1 2023 earnings announcement. We also see various partnerships and integrations with ChatGPT being announced, a subset of which we mention here: Shopify is an interesting use case, where one can ‘Make AI-Powered Store.’ Think of customer service—ChatGPT-powered chatbots do not need to operate in terms of ‘hours’ so it would be possible that Shopify merchants could immediately garner 24/7 customer support6. Salesforce has referenced technology it is calling ‘EinsteinGPT’, which would combine its proprietary AI with that of outside partners, like OpenAI, to help businesses generate email drafts, customer-account information, and computer code7. The Coca-Cola company will leverage OpenAI’s generative AI technology for marketing and customer experiences. This includes personalised ad copy, images, and messaging8. Smaller companies for specialised services & acquisitions Smaller start-up companies may utilise different large language models as a base, but then allow their users to more easily incorporate a chatbot directly onto their website to help with customer service queries. In regulated industries, such as financial services, companies can feed past requests and questions, so large language models can ‘read’ and then ‘learn from’ the questions and the responses, thereby readying themselves for the future. Nuance Communications, for example, was acquired by Microsoft due to its specific expertise in natural language processing related to healthcare services. Medical transcription is a huge, but specific, market. Bottom line: be ready for diversified value creation across many different industries When generative AI ultimately is consumed through the cloud computing platforms, the impact will not be limited to any single area of the economy. On 27 March 2023, the Wall Street Journal cited US authorities responsible for antitrust enforcement felt it important enough to mention their intention to protect the AI market from actions that might yet be taken by large incumbents, fearing threats to their dominance in the space9. This is yet another indication this space is a technology with far reaching implications. Sources 1 Source: Business of Apps 2 Source: McGee, Patrick & Madhumita Murgia. “Microsoft to add AI co-pilot to its Office software suite.” Financial Times. 16 March 2023. 3 Source: “Is Google’s 20-year dominance of search in peril?” Economist. 8 February 2023. 4 Source: Apple Podcast : ilya-sutskever-openai-chief-scientist-building-agi 5 Source: “Big tech and the pursuit of AI dominance.” Economist. 26 March 2023. 6 Source: community shopify ecommerce-marketing chatgpt-for-shopify-store-make-ai-powered-store 7 Source: “Salesforce to add ChatGPT to Slack as part of OpenAI partnership.” Reuters. 7 March 2023. 8 Source: Johnston, Lisa. “Coca-Cola Signs As Early Partner for OpenAI’s ChatGPT, DALL-E Generative AI.” Consumer Goods Technology. 21 February 2023. 9 Source: Wolfe, Jan & Dave Michaels. “FTC Chair Lina Khan Vows to Protect Competition in AI Market.” Wall Street Journal. 27 March 2023.
N
by aneekaguptaWTE
All about artificial intelligenceOn the 10 March 2023 episode of the Behind the Markets podcast, we had the pleasure of speaking with Blake Heimann, Senior Associate, Quantitative Research at WisdomTree. Within our team, we spend a lot of time talking about artificial intelligence (AI) with Blake, especially lately. Years ago, he was bitten by the bug, gaining a passion to study such things as statistical methods, regressions and time series forecasting. He’s even pursuing a Masters degree presently, focused on AI and machine learning. With the release of ChatGPT from OpenAI in the latter part of 2022, AI entered into the public’s consciousness in a manner reminiscent of some of the world’s most successful applications—such as TikTok and Instagram. We wanted to have this conversation in order to provide perspective on AI and help people in thinking about the space itself as well as avenues of potential investment research. Some of the topics we covered included: ChatGPT It’s difficult to say how long we’ll be focused on ChatGPT or the value that it may bring, but it speaks to how AI is a space subject to nonlinear advances. A lot of work and investment went into creating ChatGPT, and then it represented something very tangible for any person to see and experience. We have to remind ourselves that next year we may be talking about something entirely different and even more capable, but we also admit that competing with ChatGPT purely on the basis of ‘viral adoption’ would be quite a feat. Graphics processing units (GPUs) and the discussion of Nvidia vs Intel Within semiconductors, Nvidia has had an excellent run, almost branding itself as the company most capable of designing the best semiconductors upon which to train AI models. Intel, on the other hand, has stumbled on some of its more cutting-edge hardware releases and has even had to cut its dividend recently. We were able to talk to Blake about how to consider the differences between these companies in the present market environment. Ambarella for computer vision chips We didn’t want to fall into the trap of only discussing some of the largest and most recognisable semiconductor firms, so we asked Blake if there were any semiconductor companies out there that he, as someone more deeply involved in AI and machine learning, is excited about. He did not hesitate and noted Ambarella. Ambarella is designing specific chips that are involved in computer vision, specifically as it relates to autonomous driving, and Blake went into a discussion about image segmentation and how these chips are getting better and better at performing these rather involved calculations directly, in almost real-time. Proliferation of data and opportunities for AI disruption The biggest reason, in our opinion, that we are discussing things like ChatGPT and autonomous driving, is that we have recently gotten to a place globally where we are generating more data than ever before. We asked Blake, with the proliferation of this data, what industries he believes are most ripe to be disrupted in terms of AI providing something powerful in a faster, more efficient way. Blake noted that this can happen in many industries, but then he did settle on the concept of using AI for drug discovery—namely how models can suggest potential compounds and molecules that can have the potential to react in a beneficial way to help with therapeutics. While AI may not directly create drugs end-to-end, it may suggest interesting paths for researchers to try and cut down the overall time from concept to finished drug. Conclusion: always look at the direct functional expertise for any AI company There is a big difference between a company that mentioned AI a few times on a recent earnings call, versus a company that is directly providing AI as a solution to real, current-day problems. Blake noted in numerous ways the importance of always being able to see the specific AI function being performed by a given AI company in order to help judge is potential attractiveness as an investment.
N
by aneekaguptaWTE
APEUSDT Ranging Looking at the chart, we can see a period of consolidation where the price has been trading within a certain range. During this time, there has been a high level of trading volume, which could suggest that larger market players are accumulating the asset. To navigate this situation, it might be best to continue trading within the established range until there is a clear breakout to the upside. If the price does break above the local highs and this move is supported by significant volume, it could be a sign of a potential bullish trend reversal.
by PavlOK
Trade talk with A.IQ1. Your Advise to the experienced traders. As an AI language model, I cannot provide personalized advice to specific individuals. However, here are some general tips that experienced traders may find useful: 1.1 Stick to your trading plan: Experienced traders know the importance of having a solid trading plan and sticking to it. Your trading plan should include your risk management strategy, entry and exit points, and profit targets. Avoid deviating from your plan based on emotions or market noise. 1.2 Continue learning: The markets are constantly evolving, so it's important for experienced traders to continue learning and adapting to changes. Stay updated with market news and events, learn new trading strategies, and be open to new ideas and perspectives. 1.3 Practice good risk management: Experienced traders understand the importance of managing risk. Make sure you understand risk management principles and use tools like stop-loss orders and position sizing to limit your risk. 1.4 Stay disciplined: Trading can be a stressful and emotional experience, but experienced traders know the importance of staying disciplined and keeping their emotions in check. Stick to your trading plan, avoid impulsive decisions, and don't let fear or greed drive your trading decisions. 1.5 Review and analyze your trades: Successful traders are always looking for ways to improve their performance. Take the time to review and analyze your trades, identify areas for improvement, and make adjustments to your trading plan as necessary. Q2. How to trade Head and shoulder patterns? 2.1 The head and shoulders pattern is a technical analysis pattern that is commonly used by traders to identify potential trend reversals. Here are the basic steps for trading a head and shoulders pattern: 2.2 Identify the pattern: The head and shoulders pattern consists of three peaks, with the middle peak (the "head") being higher than the other two (the "shoulders"). The neckline is a line that connects the lows between the shoulders. 2.3 Wait for confirmation: Once you have identified the pattern, wait for a confirmed breakout below the neckline. This confirms that the pattern is valid and increases the probability of a trend reversal. 2.4 Set a stop-loss: Place a stop-loss order above the right shoulder of the pattern to limit your risk if the market moves against you. 2.5 Set a profit target: Calculate the distance between the head and the neckline, and use this to set a profit target for your trade. 2.6 Monitor the trade: Monitor the trade closely and adjust your stop-loss and profit target as necessary. Q3. Do people get rich day trading? While it is true that some traders have become wealthy from trading, it is important to understand that trading is not a guaranteed path to riches. Trading involves risk, and losses are a natural part of the process. In fact, the majority of traders lose money, and only a small percentage are consistently profitable over the long term. Furthermore, becoming a successful trader requires a significant amount of time, effort, and dedication. It's not something that can be achieved overnight, and there are no shortcuts or easy solutions. In addition, it's important to have realistic expectations about trading. While it is possible to make money from trading, it is not a get-rich-quick scheme. Successful traders typically have a long-term perspective, and they focus on building consistent profits over time. In summary, while trading can potentially lead to wealth, it is not a guaranteed path to riches. Trading involves risk, requires a significant amount of time and effort, and success is not guaranteed. As with any other profession, success in trading requires hard work, dedication, and a commitment to ongoing learning and improvement. Q.4 Can you help someone to become a profitable trader? As an AI language model, I can provide educational information and guidance to help someone become a profitable trader. However, becoming a profitable trader requires more than just information and guidance - it requires a combination of knowledge, experience, discipline, and a commitment to ongoing learning and improvement. Some of the ways in which I can help someone become a profitable trader include: 4.1 Providing educational resources on trading fundamentals, technical analysis, risk management, and other key trading concepts. 4.2Assisting with the development of a personalized trading plan based on an individual's unique goals, risk tolerance, and trading style. 4.3 Offering insights into market conditions and potential trading opportunities across a range of asset classes. 4.4 Providing support and guidance on managing emotions and maintaining discipline in the face of market volatility and uncertainty. 4.5 Offering feedback and analysis on trading strategies and performance, with a focus on identifying areas for improvement and optimization. 4.6 It's important to note that becoming a profitable trader is a long-term process that requires patience, discipline, and a willingness to learn from both successes and failures. With the right guidance and approach, however, it is possible to achieve consistent profitability and success in the markets. Q5. Do prayers help in trading? As an AI language model, I do not have a religious belief, but I can provide a general response to your question. Prayer is a personal spiritual practice that may bring comfort and guidance to an individual. However, it is not a guaranteed method for achieving success in trading or any other activity. Trading success depends on a combination of factors, including knowledge, experience, discipline, and a sound trading strategy. It's important to approach trading with a rational and analytical mindset, rather than relying on external factors such as prayer or luck. While prayer may provide emotional support and help manage stress, it is not a substitute for a well-developed trading plan and disciplined approach to the markets. In summary, while prayer may have personal and emotional benefits, it is not a reliable or effective means for achieving success in trading. A disciplined and rational approach to trading, based on sound principles and a well-developed trading plan, is key to long-term success. Q6. How to trade triangle pattern? The triangle pattern is a common chart pattern that occurs when the price of an asset consolidates between two converging trendlines. Trading the triangle pattern involves identifying the breakout direction and taking a position in that direction. Here are some general steps to trade the triangle pattern: 6.1 Identify the triangle pattern: Look for a period of consolidation where the price moves between two converging trendlines, forming a triangle shape on the chart. 6.2 Determine the direction of the breakout: As the price nears the apex of the triangle, it will typically break out in one direction or the other. Look for a breakout that is accompanied by high volume and follow-through price action. 6.3 Enter a position: Once you have determined the direction of the breakout, enter a position in that direction. This can be done through buying or selling, depending on whether the breakout is to the upside or downside. 6.4 Set stop-loss and take-profit levels: It's important to manage your risk by setting stop-loss levels to limit potential losses and take-profit levels to lock in profits. These levels should be based on your risk tolerance and trading strategy. 6.5 Monitor the trade: Keep an eye on the price action after the breakout to ensure that it continues to move in your favor. If the price fails to continue in the breakout direction, it may be wise to exit the trade with a small loss. 6.6 Consider other factors: It's important to consider other factors, such as market conditions and economic events, that may affect the price of the asset. This can help you make more informed trading decisions. In summary, trading the triangle pattern involves identifying the breakout direction and taking a position in that direction while managing risk through stop-loss and take-profit levels. However, it's important to consider other factors that may affect the price of the asset and to monitor the trade closely to ensure that it continues to move in your favor.
Education
by Aadil1000x
ETH is testing the strong level of 1840.PatternThe probable pattern here is closely related to the labor market report. We have data coming out on unemployment in the US. In addition, today the stock exchanges of the US, European countries, and Hong Kong are closed due to the Good Friday holiday. What pattern? We have a strong support level + negativity ahead of important data. Very often, things work the other way around for us. If prices are pushed down before important events, we get a positive market reaction at the event. This also works in reverse, if we grow before an event, the market reacts negatively afterwards. Today, I am considering the probability of this pattern after the data is released. If we go below 1840 (a confirmed drop, not a fake one), then the scenario of an upward trend takes a "back seat".
BITSTAMP:ETHUSD
by PavlOK
INTUSDT technical Analysis From technical analysis wise, a clear breakout from a falling wedge on W & 3D frames, it is retesting, I think it will make 10x from here. According to INTchain twitter account: “The INT Chain Foundation officially announced that it will conduct in-depth research and expansion on AIGC and ChatGPT related technologies, with an ever-changing pace, profoundly, quickly, and thoroughly changing the existing IoT chain model.” That’s BULLISH but you need to keep in mind the following. 1- It’s a small cap project with huge potential but with a low liquidity “ manage your risks” 2- Take profit along the way and enjoy. Good luck
ILong
by BadrAAA
Chat GPT indicator research I recently shared a video post about using "Chat GPT" to create custom pinescript indicators. In the video, I just showed the potential for starting your journey off! Here's an updated view of the very indicator I had Chat GPT build in the video. This got me thinking as to the possibilities, so I made a start on a new indicator. One for looking at "footprint trading" a technique I used to use a lot earlier in my career, in essence it's all to do with volume profiles. Starting off I looked at ways to find key value areas using a type of periodic view of volume and then wanted to test momentum at these levels. I asked chat GPT for a couple of different variations and got a little stuck on the complexity. (not being a coder n all) So I asked for a little help from a friend @peterhammer, who I now call Chat GPT 5.0. He added and tweaked, enhanced the overall idea. So starting with periodic profile. This helps spot local key areas as highlighted here... As we move into a range - we can see the volume momentum clearly highlighted here. This has been a fun first project with the aid of Chat GPT for sure! More to follow!!! 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:BTCUSD
by Mayfair_Ventures
3
DOGE to the MOON or just a Fantasy Hey there fellow traders! Can you believe it? I was playing around with the Fibonacci tool on my chart, and even though it might not be applied perfectly, it somehow makes perfect sense and reflects the levels just right! My theory is that the last time Doge was pumped, it was so artificially driven that it actually reached a mind-blowing level of 3.618 without facing any significant resistance. Crazy, right? So, I decided to take 0.20, a nice and round number that has served as both resistance and support multiple times, as our level 1. And guess what? The picture it paints is pretty exciting! Based on the fundamental background and the momentum of Doge, I have a strong hunch that it might just reach 0.30 in the near future. Go, go ELON! Let's see how this wild ride unfolds! 🚀🌕💰📈📉🐶🤑 #ToTheMoon #DogeArmy #TradingIdeas #FunWithFibonacci 📊📈😄🐕🚀🌕💰📉🤑🚀🌕💰📊📈🐶😄🤑📉📊🚀🌕💰📈🐕🤑😄🚀📉📈🚀🐶💰🤑🌕📊📉😄🐕💰🚀📈🤑🌕🚀📉🐶😄📊💰📈🚀🐕🤑🌕📉📊📈🚀💰😄🐶🚀📈📉💰📊🐕🤑🌕😄🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀
Long
by PavlOK
Bili Short update: Using ChatGPT indicator ESVO Using this opportunity to cover where we are at with Bili on this short, targets, and explaining how the ESVO works and how to use it to find price targets and stop loss pockets to manipulate for your own profits. The ESVO is called the equilibrium Singularity Volume Oscillator. I used Chatgpt to code it, which is amazing because I can not code. I love this indicator as it shows me supply and demand. It shows me where pockets of stop losses are and paints a picture of where price is going and who is in control.... Everything price action isn't showing you, it is showing you. If you like this video or find it intriguing pls hit the boost button, like, and sub. Thanks by iCantw84it 04.04.23
NASDAQ:BILIShort
by iCantw84it
BTC possible 100k? Based on the historical significance of certain levels for Bitcoin (BTC), if the current level is broken, the closest new high for BTC is likely to reach 100k. This prediction is based on the fundamental background and previous bull runs, where applying the Fibonacci retracement levels highlights key levels that align with the new ones. For example: Bull run in 2017, Level 1.618(2017) -> 0.618 ( 2020) -> 0.382 (2022-2023) Bull run in 2021, level 1.618(2021) -> 1(2022)
Long
by PavlOK
2
What ChatGPT has to say about Retail vs Professional Indicators?When it comes to trading, novice traders may be tempted to rely solely on retail trading indicators such as RSI, MACD, Stochastic RSI, Bollinger Band, and ADX. However, relying on these indicators can lead to traders losing money in the long run. One of the main problems with retail indicators is that they tend to generate false signals, which can lead to traders entering and exiting trades at the wrong time. Retail indicators are based on historical price data and do not take into account other factors that can affect market movements, such as news events, economic data, or geopolitical developments. In contrast, professional trading indicators such as market internals, volume profile, market profile, open interest, and volume delta are essential for traders who want to stay profitable in the long run. These indicators provide a deeper understanding of market conditions, which allows traders to make more informed trading decisions. Market internals can provide insights into the underlying market sentiment and identify potential changes in trend. For example, the NYSE Tick Index measures the number of stocks on the New York Stock Exchange that are trading on an uptick minus the number of stocks that are trading on a downtick. A high tick reading can signal bullish market sentiment, while a low tick reading can signal bearish market sentiment. Volume profile, market profile, and open interest can help traders identify support and resistance levels, potential breakout points, and market structure, which can improve the accuracy of their trading decisions. For example, volume profile analysis can reveal where the most significant buying and selling activity is happening, which can help traders identify potential turning points in the market. Market profile analysis can reveal the market's value area, which is the price range where the majority of the trading activity has occurred. This information can help traders identify potential breakout points or reversal areas. Volume delta can help traders identify market imbalances and potential trend changes. For example, if the price is going up, but the volume delta is negative, it can indicate that selling pressure is starting to build, which could lead to a potential reversal. Professional traders also tend to use more advanced techniques, such as order flow analysis and footprint charts, which allow them to see the actual orders being executed in the market. This provides a more accurate view of market conditions and can help traders identify potential trading opportunities. For example, order flow analysis can help traders identify potential order imbalances and see where the big players are positioning themselves in the market. Understanding the difference between lagging and leading indicators is crucial for traders who want to stay ahead of the market. While lagging indicators may provide some insights into past market conditions, they are not sufficient for making profitable trading decisions. Traders must learn to use leading indicators, such as professional trading indicators and advanced techniques, to gain a deeper understanding of market conditions and make more informed trading decisions. In conclusion, relying solely on retail trading indicators can lead to traders losing money in the long run. Professional trading indicators, such as market internals, volume profile, market profile, open interest, and volume delta, provide a more accurate view of market conditions, which allows traders to make more informed trading decisions. Advanced techniques, such as order flow analysis and footprint charts, can help traders identify potential trading opportunities and gain a competitive edge in the ever-changing market.
NASDAQ:TSLAEducation
by PtGambler