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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
Long AI Short HypeFighting innovation is a fool’s errand. Getting entangled in hype is no less. Generative AI is drawing attention. ChatGPT skyrocketed in popularity since launch last November. With its intuitive responses, it has become the fastest-growing app in history reaching one million users in five days and 100 million in two months. In contrast, Google took 12 months and Facebook required four years to get there. The virality highlights the potential disruptive power of generative AI. Disruptive innovation is not new. Railways in 1800s to Blockchain in the recent past provide ample history. As observed before, innovation takes time to mature. Yet the hype cycle races ahead only to plunge in time to normalise. This paper uses iShares Exponential Technologies ETF (XT) as a proxy to cutting edge innovation. XT invests in global firms with exposure to exponential tech, which displaces older tech. It invests across nine themes comprising of firms in both developed and emerging markets that create or use exponential tech. This paper argues for gains to be harvested from sinking hype using a spread trade. A long position in CME Micro E-Mini Nasdaq Futures (MNQ) combined with a short position in XT will deliver a compelling 1.49x reward to risk ratio. HISTORY OF HYPED INNOVATION Gartner hype cycle graphically depicts disruptive innovation journey. First comes the climb to peak hype. Second, fall to trough of disillusion. Third, slope of enlightenment followed by plateau of productivity. Using Google Trends as a proxy for hype cycle, it shows that market mania around AI is not new. AI searches surged in 2011 with the launch of Siri, Cortana, and IBM’s Watson. With natural language processing tech still in infancy, practical applications were limited then. And soon, the frenzy fizzled. Innovation in new machine learning algo such as convolutional neural networks and deep learning led to the launch of ChatGPT. Its potential is clear. Yet the tech is in early stages requiring a lot more work before it can mount serious challenge to existing tools. Tech parity will take considerable time let alone the meaningful monetisation which requires legal and ethical AI use hurdles to be cleared. One of the foremost examples of Gartner’s Hype Cycle is the boom in US Railways between 1840-1860. Hopes of ever-increasing returns attracted large scale investments only to result in eventual disappointment. Illustrations from recent past (Crypto, IoT, and Blockchain) shows similar fate of over-hyped tech. CURRENT HYPE IN XT, C3 AI, AND BEIJING DEEP GLINT A 23% surge in price in iShares Exponential Technologies ETF since mid-October last year is emblematic of Gartner’s hype cycle. This is even more evident in the share price of C3.ai. Founded by legendary entrepreneur Tom Siebel, this company was named C3 Energy when formed. It changed its name to C3 IoT in 2016 and then renamed again to C3.ai in 2019 to ride the waves of hype. US equities cannot claim monopoly over hype. Equities elsewhere get swayed too. Shares in Beijing Deep Glint Technology also rallied 80% spurred by ChatGPT. However, last week, the company announced challenges in offering ChatGPT-linked products causing its shares to tank 10%. ROAD AHEAD FOR GENERATIVE AI Generative AI is here to stay. Infancy for now but the tech will mature. Competition will rise. Winners will emerge. But monetization is another story altogether. Favouring innovation while frowning on hype fuelled by inflated expectations, this case study proposes a spread trade. A long position in CME Micro E-Mini Nasdaq Futures (MNQ) combined with a short position in iShares Exponential Technologies ETF (XT) delivers a compelling 1.49 reward to risk ratio. TRADE SET UP Why a spread trade? In the short term, elevated levels of uncertainty have left experts puzzled on whether we are in a bull market or a bear market rally. Hence, to extract pure alpha (by neutralising beta) of securing gains from diminishing hype, this case study proposes a spread trade. The spread will gain in a bullish market when MNQ rises relative to XT. Similarly, the spread will gain in a bearish market when XT falls more than MNQ. CME’s Micro E-Mini Nasdaq-100 Index Futures expiring in June 2023 (MNQM2023) provides a notional exposure to $2 x Nasdaq-100 index. With MNQM2023 settling at 12,525.50 on February 17th, the futures provide a notional exposure of $25,051. XT settled at $52.58 on the same day. A spread requires notional value of both the legs to be identical. Therefore, this requires short selling 476 units of XT for a short exposure of $25,028. • Entry: 238.218 • Target: 255 • Stop: 227 • Profit at Target: $ 1,760 • Loss at Stop: $ 1,180 • Reward-to-Risk Ratio: 1.49x MARKET DATA CME Real-time Market Data helps identify trading set-ups and express market views better. If you have futures in your trading portfolio, you can check out on CME Group data plans available that suit your trading needs www.tradingview.com DISCLAIMER This case study is for educational purposes only and does not constitute investment recommendations or advice. Nor are they used to promote any specific products, or services. Trading or investment ideas cited here are for illustration only, as an integral part of a case study to demonstrate the fundamental concepts in risk management or trading under the market scenarios being discussed. Please read the FULL DISCLAIMER the link to which is provided in our profile description. REFERENCES www.cmegroup.com www.cmegroup.com
CME_MINI:MNQM2023Long
by mintdotfinance
18
MSFT $350 AND BEYOND Microsoft has been a big player in the AI industry recently and has been making a lot of buzz lately. Their acquisition of ChatGPT was an strategic play against GOOGLE to position themselves better as a search engine. GOOGLE has been the leader as a search engine but things look like they are changing soon. Other than that, MSFT has a couple good reasons why it is a good investment: Established brand and resources: Microsoft is an established technology company with significant resources and a long history of innovation. Its vast network of developers and engineers, coupled with its access to cutting-edge technology and data, could give it a significant advantage in the AI market. Diversification: By entering the AI market, Microsoft could diversify its revenue streams and reduce its reliance on traditional software products. As AI technology continues to develop and become more ubiquitous, Microsoft's investment in this field could position it for long-term growth and success. Customer base: Microsoft has a large customer base that includes both consumers and businesses. Its AI technology could be integrated into existing products, such as Office 365 and Azure, providing a seamless experience for customers. This integration could create significant value for both Microsoft and its customers, making it an attractive investment opportunity.
NASDAQ:MSFTLong
by CryptoGao1
NVDA Nvidia A100 and OpenAI's trend: Long Lower ShadowThe technical rating for NVDA is "Buy." The stock has a strong buy rating for moving averages, and oscillators rating is "Sell." The 1-month high for NVDA is 275.89, and the 1-month low is 204.21. The stock is currently trading at 271.82, and the change percentage is -0.24038903 with a change of -0.655. The stock has a YTD performance of 83.09204767 and a 5-year performance of 348.69636964. The volatility week is 3.61977509, and the volatility is 3.86286999. The technical indicators for NVDA are mixed. The average directional index (14) is 25.53672889, and the commodity channel index (20) is 100.91947669. The relative strength index (14) is 71.93093942, and the stochastic %K (14, 3, 3) is 85.20138725. The stochastic %D (14, 3, 3) is 86.77363202, and the momentum (10) is 16.33. The moving averages for NVDA are strong buy, with the simple moving average (10) at 263.48646, the exponential moving average (20) at 255.16254544, the exponential moving average (30) at 249.3622347, the exponential moving average (50) at 239.95627651, the exponential moving average (100) at 221.04912237, and the exponential moving average (200) at 199.5298234. Other technical indicators for NVDA include the awesome oscillator at 24.44188382, the average true range (14) at 7.27553041, the MACD level (12, 26) at 9.77699817, the MACD signal (12, 26) at 8.17087914, the bull bear power at 19.08011698, and the parabolic SAR at 251.41589934. The stock's current pattern is "Long Lower Shadow." The pivot Fibonacci P is 248.03, and the pivot Camarilla R1 is 260.89016667. The pivot Camarilla P is 248.03, and the pivot Camarilla R3 is 268.4105. The pivot Camarilla S2 is 249.60966667, and the pivot Camarilla S3 is 245.8495. The pivot Fibonacci R1 is 263.69964. What is Long Lower Shadow? A Long Lower Shadow is a candlestick pattern that indicates a potential reversal in the current trend. It occurs when the price opens at a certain level, then drops significantly during the trading session, but eventually recovers and closes near the opening price. This creates a long lower shadow below the real body of the candlestick . Traders may interpret a Long Lower Shadow as a sign of bullishness, as it suggests that the bears (sellers) tried to push the price down, but the bulls (buyers) were able to drive it back up. However, it's important to consider other technical indicators and market factors before making any trading decisions based on a single candlestick pattern.
NASDAQ:NVDALong
by BorsaMerkezi
2
BIDU —— just a pitch deck!!Online, some people said immediately after the event that Baidu wouldn’t have shied away from chatting with Ernie Bot live if it had mature capabilities. “I get it, it’s not ready yet, just a pitch deck!!” wrote one viewer on the event’s English-language live stream on YouTube. “Two words: that’s it,” wrote another on the Chinese Twitter-like platform Weibo, adding: “What was on display is just a patchwork version of Baidu Baike,” referring to the company’s Wikipedia-like product. Baidu shares also fell on the day of the conference, although the later rebound, but from the 1-hour chart, in a wide range of oscillation, 155 this position has been adjusted 3 times have not been broken, MACD green area is also gradually reduced again, technically speaking the oscillation will continue, we wait for an intra-day shorting opportunity
NASDAQ:BIDUShort
by CryptoParadiseVIP
I ask ChatGPT, he told me to buy dollarQuestion: can you explain this like I'm 5 ? "To improve the swap lines' effectiveness in providing U.S. dollar funding, the central banks currently offering U.S. dollar operations have agreed to increase the frequency of 7-day maturity operations from weekly to daily. These daily operations will commence on Monday, March 20, 2023, and will continue at least through the end of April." Answer: Ok, let's imagine you and your friend like to share toys. Sometimes, your friend has a really cool toy that you really want to play with, but you don't have any toys to give in exchange. So, your friend might lend you the toy for a little while and you promise to give them something else back later. In the same way, countries can lend each other money when they need it. Sometimes, countries need US dollars but they don't have any to give in exchange. So, other countries (called "central banks") might lend them some US dollars for a little while and the first country promises to pay them back later. Now, the central banks that lend US dollars have decided to make it easier for other countries to borrow money by making these lending operations happen more often. Before, they would lend money once a week, but now they will lend money every day. This will start on March 20th, 2023, and will continue for at least a month.
FX:XAUUSDShort
by User54215116081
$AMD: Long term trend is up$AMD is a leading chipmaker that provides processors for various applications of generative AI, such as ChatGPT, Bing, and Bard. Generative AI is a technology that can create new content based on existing data, such as text, code, images, and music. ChatGPT is a generative AI model that can write prose, code, and much more. It has been hailed as the most powerful and unpredictable technology of this generation. Generative AI has the potential to revolutionize and expand the creator economy by enabling new forms of expression and innovation. It can also improve efficiency and productivity in various sectors by automating tasks and generating insights. By adopting generative AI, chatgpt can transform the economy with higher efficiency, which allows economic growth despite slowing demographics long term. $AMD benefits from this trend by providing high-performance chips that power generative AI applications and platforms. $AMD seems ready to rally steadily from here, long term trend is up and we should participate on the upside with low risk entries like this one. Try to not miss out on this one. Best of luck! Cheers, Ivan Labrie.
NASDAQ:AMDLong
by IvanLabrie
MSFT Microsoft AI: New Google like Lion King / OpenAI ChatGPTAs a day trader, let's analyze the stock data for Microsoft Corporation (MSFT). The current price for MSFT is $278.29, which is a 0.749% increase from the previous day's close. The stock has seen a 1-month high of $283.33 and a 1-month low of $245.61. Looking at the technical indicators, the Oscillators Rating is Neutral, while the Average Directional Index (14) is at 25.914. The Awesome Oscillator is at 15.175, and the Average True Range (14) is 4.936. The Commodity Channel Index (20) is at 223.176, while the MACD Level (12, 26) is at 5.050, and the MACD Signal (12, 26) is at 2.228. The Relative Strength Index (14) is at 75.934, and the Stochastic %K (14, 3, 3) is at 92.917, while the Stochastic %D (14, 3, 3) is at 94.501. The Aroon Up (14) is at 100, and the Aroon Down (14) is at 42.857. The Bull Bear Power is at 30.674, and the Parabolic SAR is at 254.903. The Simple Moving Average (10) is at 263.583. Based on these technical indicators, the Moving Averages Rating is Strong Buy, and the Pivot Fibonacci P is at 252.107. The Ichimoku Conversion Line (9, 26, 52, 26) is at 264.53, and the Technical Rating is Buy. The YTD Performance for MSFT is at 14.489%. In terms of sector performance, MSFT operates in the Technology Services sector. The 1-Year Beta is at 1.130, while the 3-Month High is at $283.33, and the 3-Month Low is at $219.35. The 5-Year Performance for MSFT is at 196.885%. The Exponential Moving Average (20) is at 261.493, the Exponential Moving Average (50) is at 257.127, and the Exponential Moving Average (200) is at 251.111. Overall, based on the technical indicators and sector performance, it seems that MSFT is a strong buy for a day trader. However, it's important to keep an eye on any market fluctuations and news that may affect the stock's performance.
NASDAQ:MSFTLong
by BorsaMerkezi
AI - Breakout Falling Trend [MIDTERM]- AI has broken through the ceiling of a falling trend channel in the medium long term. - AI has given a positive signal from the rectangle formation by a break up through the resistance at 21. - Further rise to 28 or more is signaled. - AI has marginally broken down through support at 23. - An established break predicts a further decline. - Volume has previously been high at price tops and low at price bottoms. - Volume balance is also positive, which strengthens the stock. - Overall assessed as technically neutral for the medium long term. *EP: Enter Price, SL: Support, TP: Take Profit, CL: Cut Loss, TF: Time Frame, RST: Resistance, LT TP: Long Term Target Price Verify it first and believe later. WavePoint ❤️
NYSE:AI
by wavepoint99
What is ChatGPT and why is AI suddenly a big deal?The latter part of 2022 and the early part of 2023 have seen many developments around ‘generative AI’. The big story recently concerns the ChatGPT system. Conceptually, there is a prompt, and then the system can come up with text to match the prompt. ChatGPT has ‘gone viral’ in that many people have delighted in experimenting with different prompts to see what comes up. It’s also the case that other systems have also recently been developed where the output may be a picture or a video. Broadly, these systems are taking a prompt and then using their training data in order to predict something that makes sense against the prompt as an output. The world hasn’t seen these capabilities until now, so there are many speculations as to what it means in terms of intelligence or what types of business models will make sense to build against it. More than just a craze: the real-world applications of AI A tool like ChatGPT is training on large amounts of data to make predictions. People use it now as a novelty—it can predict the next likely word in a string of text within the context of a prompt. It can, however, be trained to predict other things, and these predictions, if they are accurate, could be valuable. There are tools already in existence that help software developers with coding, predicting the likely ‘next line of code’ for them to write. It will be interesting to see how Microsoft, a major investor in OpenAI, the company behind ChatGPT, looks to integrate the technology into something like Microsoft Office 365, which would mean nearly instant exposure to billions of users. It’s really only when you expose billions of people to a given piece of technology that you really start to see all the various potential use cases. During 2022, DeepMind unveiled new results from its AlphaFold system, which is designed to predict the shape of proteins. The system had come up with outputs specifying the predicted shape of more than 200 million proteins, and a significant percentage of these predictions were viewed as being as accurate as experimental results. Predicting the shape in which a given protein will fold, by itself, means nothing, but it becomes exciting when you start to consider that frequently the shape of the protein encodes the function of the protein, and the function could relate to many distinct therapeutic outputs that could then be used to fight diseases and other health problems. Who stands to gain? It is difficult to predict which industry or company will benefit the most from AI applications because it’s possible that any company or industry that uses data could benefit. It is natural to think of the large technology companies—Amazon, Apple, Meta, Alphabet, Microsoft to name a few—and you can see how AI is being used to directly enhance the experience of their customers. Amazon and Microsoft, in particular, offer AI services through their cloud computing platforms. However, it’s also true that pharmaceutical companies could benefit in drug discovery, insurance companies could benefit from better predictions - the list is endless. We find it exciting to think about how different developments can build on each other. Take fusion power as an example. We have already seen that different machine learning systems may unlock novel ways to manage the reactions and control the system. If we can combine machine learning with certain quantum computing capabilities, maybe the calculations can broaden in scope and advance in speed in ways to allow further developments beyond what is possible today. AI depends on data, and quantum computing may allow certain types of calculations to occur in parallel, taking on more data and having flexibility to instantly adjust. One thing we remind people of is that, 20 years ago, many of us didn’t have regular internet access—certainly not high speed. Can we really predict where we’ll be 20 years from now? AI against the macroeconomic backdrop If we are looking at the world in March 2023, the biggest near-term catalyst is most likely the macroeconomic backdrop as viewed through 1) announcements from the US Federal Reserve (Fed) 2) data on the US labour market 3) data on the path of inflation and 4) anything related to whether or not there is a recession. Many of these announcements have directions that are either ‘more positive’ or ‘more negative’ for the companies that represent the AI landscape. For example, a Fed that is less likely to be raising interest rates further is better for AI stocks than a Fed that believes that many more rate hikes are necessary to fight inflation. 2022 was a tough year for equities, especially technology stocks. Within artificial intelligence, the companies that were newer and that did not yet have positive earnings in established businesses saw their valuations decline. Part of this is natural, in that higher interest rates and expected positive earnings far in the future combine into an entity with an overall lower valuation. Additionally, we consider many specific semiconductor companies to be heavily exposed to artificial intelligence. Many of these companies have seen share prices drop due to declining demand for smartphones and personal computers, which means the demand for chips has been lower in light of increasing supplies and capital spending projects. What next for AI? Artificial intelligence is a megatrend that has a chance to impact every sector and nearly every other megatrend. Currently, there is a viral excitement around ‘generative AI.’ ChatGPT is the key example of this concept. Even if articles abound on the expected valuation for OpenAI, the company behind ChatGPT, it is not yet clear how generative AI will create revenues and profits in the near term. The giant cloud computing platforms, like Microsoft Azure and Amazon Web Services, allow many users to take advantage of AI and machine learning and may be best positioned to drive revenue from the theme. Either way, artificial intelligence is a growing landscape and recent developments have once again brought AI conversations to the fore. Whilst a software like ChatGPT may, at first glance, be dismissed as a ‘novelty’ it is clear that applying the power of AI to different industries (from manufacturing to healthcare) could have a genuinely transformative effect on the world we live in.
NASDAQ:NQINTEL
by aneekaguptaWTE
The Role of ChatGPT in Algorithmic TradingThe Role of ChatGPT in Algorithmic Trading 1. Introduction In recent years, algorithmic trading has become an increasingly important aspect of the financial markets. Algorithmic trading involves using computer programs to execute trades based on predetermined rules and algorithms, with the goal of maximizing returns and minimizing risk. The use of algorithms allows traders to make rapid, data-driven decisions and respond to market conditions faster than traditional human traders. Natural language processing (NLP) is a field of computer science that focuses on the interactions between computers and human language. In the context of algorithmic trading, NLP techniques are used to analyze vast amounts of financial news, social media, and other sources of information to identify potential trading opportunities. By analyzing this data, traders can make informed decisions and gain a competitive edge in the market. One of the key tools used in NLP for algorithmic trading is ChatGPT, a large language model trained by OpenAI. ChatGPT is a powerful tool that can analyze vast amounts of text data and generate human-like responses. Its capabilities include natural language understanding, machine translation, text summarization, and text completion. With its ability to analyze and understand large amounts of text data, ChatGPT is an essential tool for traders looking to gain a competitive edge in the market. For example, ChatGPT can be used to analyze financial news articles and social media posts to identify companies that are likely to experience a significant change in their stock price. By analyzing the sentiment of these articles and posts, ChatGPT can determine whether there is a positive or negative outlook for a particular company, which can be used to inform trading decisions. In addition to sentiment analysis, ChatGPT can also be used to generate summaries of news articles, which can save traders valuable time and allow them to quickly digest important information. ChatGPT can also be used to generate text responses to customer inquiries, freeing up traders to focus on more important tasks. Overall, the use of NLP and ChatGPT in algorithmic trading is becoming increasingly important. As the amount of data available to traders continues to grow, the ability to quickly and accurately analyze that data will become essential for achieving success in the market. With its powerful NLP capabilities, ChatGPT is poised to play a significant role in the future of algorithmic trading. 2. NLP Techniques for Algorithmic Trading Natural language processing (NLP) is an essential tool for algorithmic trading, enabling traders to quickly and accurately analyze large volumes of text data. In this section, we'll explore some of the key NLP techniques used in algorithmic trading, including analysis of financial news and social media, sentiment analysis, and identification of potential trading opportunities. One of the most powerful applications of NLP in algorithmic trading is the analysis of financial news and social media. By analyzing news articles and social media posts, traders can gain insight into the market sentiment and identify emerging trends or potential trading opportunities. For example, if a large number of news articles and social media posts are discussing a particular company, it may be an indication that the company is about to experience a significant change in its stock price. Sentiment analysis is another important NLP technique in algorithmic trading. Sentiment analysis involves using NLP algorithms to determine the emotional tone of a particular piece of text. By analyzing the sentiment of news articles, social media posts, and other sources of information, traders can gain insight into the market sentiment towards a particular company or industry. This information can then be used to inform trading decisions. Identification of potential trading opportunities using NLP is another key application of this technology. By analyzing large volumes of data, including news articles, social media posts, and other sources of information, traders can identify emerging trends or potential trading opportunities. For example, by analyzing news articles and social media posts, traders may identify a new technology that is rapidly gaining popularity, indicating a potential investment opportunity. Overall, the use of NLP techniques in algorithmic trading is becoming increasingly important. With the amount of data available to traders continuing to grow, the ability to quickly and accurately analyze that data will be essential for achieving success in the market. NLP techniques, including the analysis of financial news and social media, sentiment analysis, and identification of potential trading opportunities, are powerful tools that can help traders gain a competitive edge and achieve success in the market. 3. Predictive Models with ChatGPT Predictive models are an essential tool for algorithmic trading, enabling traders to identify patterns and predict future market trends. In this section, we'll explore how ChatGPT can be used to develop predictive models and the advantages of using this technology. At its core, predictive modeling involves using historical data to identify patterns and predict future trends. This process involves analyzing large volumes of data to identify patterns and trends that can be used to inform trading decisions. With the increasing amount of data available to traders, the ability to quickly and accurately analyze that data is becoming essential for achieving success in the market. ChatGPT is a powerful tool that can be used to analyze large datasets and identify patterns that may be missed by other analytical tools. With its ability to understand natural language, ChatGPT can analyze vast amounts of financial news, social media, and other sources of information to identify patterns and trends. This information can then be used to develop predictive models that can be used to inform trading decisions. One of the key advantages of using ChatGPT in developing predictive models is its ability to understand the context of the data it is analyzing. Unlike other analytical tools, which may only be able to identify patterns based on simple statistical analysis, ChatGPT can analyze text data to understand the context and nuances of the information being analyzed. This allows traders to identify patterns and trends that may not be immediately apparent using other analytical tools. Another advantage of using ChatGPT in developing predictive models is its ability to learn from new data. As more data becomes available, ChatGPT can be trained to recognize new patterns and trends, improving the accuracy of its predictions over time. 4. Machine Learning with ChatGPT Machine learning is a critical component of algorithmic trading, allowing traders to develop sophisticated models that can identify patterns and make real-time trading decisions. In this section, we'll explore how ChatGPT can be used in machine learning models for algorithmic trading, the advantages of using this technology, and some examples of its use. Machine learning involves using algorithms to analyze large amounts of data, identify patterns, and make predictions. This process involves training the algorithm on historical data to recognize patterns that can be used to inform trading decisions. With the increasing amount of data available to traders, the ability to quickly and accurately analyze that data is becoming essential for achieving success in the market. ChatGPT can be used in machine learning models to analyze text data and make real-time trading decisions based on that data. For example, ChatGPT can be used to analyze financial news and social media to identify patterns that may not be immediately apparent to other analytical tools. This information can then be used to inform machine learning models that make real-time trading decisions. One of the key advantages of using ChatGPT in machine learning models for algorithmic trading is its ability to understand natural language. Unlike other analytical tools, which may only be able to analyze structured data, ChatGPT can analyze unstructured data such as news articles and social media posts. This ability to understand the context of the data being analyzed is essential for developing accurate machine learning models. Another advantage of using ChatGPT in machine learning models is its ability to learn from new data in real-time. As more data becomes available, ChatGPT can be trained to recognize new patterns and trends, improving the accuracy of its predictions over time. This ability to adapt to changing market conditions is essential for achieving success in the algorithmic trading market. There are several examples of machine learning models that use ChatGPT in algorithmic trading. For example, ChatGPT can be used to analyze financial news to identify patterns and inform machine learning models that make real-time trading decisions. ChatGPT can also be used to analyze social media sentiment to inform trading decisions based on public perception of a particular stock or market. 5. Limitations and Future Directions While ChatGPT and NLP techniques have a lot of potential in algorithmic trading, there are also limitations to their use. In this section, we'll discuss some of the challenges associated with using ChatGPT and NLP in algorithmic trading, as well as potential future directions for these technologies. One of the main limitations of using ChatGPT and NLP in algorithmic trading is the potential for bias in the data being analyzed. NLP techniques rely on training data to identify patterns and make predictions, but if that data is biased in some way, it can lead to inaccurate predictions. For example, if a machine learning model is trained on historical data that reflects biased trading practices, it may perpetuate those biases in future trading decisions. Another limitation of using ChatGPT and NLP in algorithmic trading is the potential for the model to be fooled by fake or misleading information. As we've seen in recent years, social media platforms can be manipulated by bad actors to spread false information or manipulate public sentiment. If ChatGPT is trained on this misleading information, it can lead to inaccurate predictions and trading decisions. Despite these limitations, there are several potential future directions for ChatGPT and NLP in algorithmic trading. One of these is the development of more sophisticated machine learning models that can better handle unstructured data. While ChatGPT has shown promise in this area, there is still much work to be done to improve the accuracy of these models. Another potential future direction for ChatGPT and NLP in algorithmic trading is the use of natural language generation (NLG) to create more sophisticated trading strategies. NLG involves using machine learning to generate human-like language that can be used to describe trading strategies and other complex financial concepts. This can help traders better understand the decisions being made by their machine learning models and make more informed decisions. In conclusion, while ChatGPT and NLP techniques have a lot of potential in algorithmic trading, there are also limitations to their use. By addressing these limitations and exploring new directions for these technologies, we can continue to improve the accuracy and effectiveness of algorithmic trading models. As the amount of data available to traders continues to grow, the importance of these technologies in the trading industry will only continue to increase. 6. Conclusion In conclusion, ChatGPT and natural language processing techniques have become increasingly important in algorithmic trading. By analyzing large amounts of unstructured data from sources such as financial news and social media, ChatGPT can help identify potential trading opportunities and provide valuable insights to traders. One of the key advantages of using ChatGPT in algorithmic trading is its ability to analyze and understand human language. By analyzing sentiment and other linguistic patterns, ChatGPT can provide valuable insights into public opinion and market trends, which can be used to inform trading decisions. Another advantage of ChatGPT in algorithmic trading is its ability to analyze large datasets and identify patterns that may not be immediately apparent to human traders. By using machine learning models to analyze historical data, ChatGPT can identify trends and make predictions that can help traders make more informed decisions. Looking to the future, it's likely that ChatGPT and other NLP techniques will continue to play a significant role in algorithmic trading. As the amount of data available to traders continues to grow, the importance of these technologies in the trading industry will only continue to increase. However, there are also potential challenges and limitations associated with using ChatGPT and NLP in algorithmic trading. It's important to be aware of these limitations and to work to address them in order to ensure that these technologies are used in a responsible and effective way. Overall, the use of ChatGPT in algorithmic trading represents an exciting development in the field of finance. By using machine learning and natural language processing techniques to analyze large amounts of data, traders can gain new insights and make more informed decisions. With continued research and development, the potential applications of ChatGPT and other NLP techniques in algorithmic trading are sure to grow and evolve in the years to come.
BITSTAMP:BTCUSDEducation
by JS_TechTrading
85
NVDA Microsoft and OpenAI's ChatGPT 4 RevolutionNVIDIA Corporation (NVDA) is a semiconductor company that designs and manufactures graphics processing units, as well as other specialized processors for a wide range of applications such as gaming, professional visualization, data centers, and autonomous vehicles. As of the current market data, NVDA is priced at $240.82 with a 1.02% increase in the last trading session. The 1-month high and low for the stock were $244.54 and $204.21, respectively. The Relative Strength Index (RSI) for NVDA is at 58.90, indicating a neutral market sentiment for the stock. NVDA has been showing strong bullish momentum, as evidenced by its Average Directional Index (ADX) of 16.02. Additionally, the Moving Averages Rating for NVDA is Strong Buy, indicating that the stock is trading above its short-term and long-term moving averages. However, the Awesome Oscillator for NVDA is at 5.08, indicating a weak bullish momentum. The MACD Level (12,26) is positive at 2.95, but the MACD Signal (12,26) is at 3.01, indicating a bearish trend reversal in the short term. The Commodity Channel Index (CCI) is currently at 85.94, suggesting that the stock may be overbought. The company's financials also look strong, with a YTD performance of 4.28%. The Exponential Moving Averages (EMA) for NVDA are also positive, with the 20-day EMA at 234.73, 50-day EMA at 225.89, and 200-day EMA at 191.54. Overall, NVDA appears to be in a bullish trend, with strong financials and technical indicators. However, investors should be cautious of a possible bearish trend reversal in the short term. Microsoft new partner OpenAI will make Microsoft better than Google. Will see
NASDAQ:NVDALong
by BorsaMerkezi
MSFT still respecting key levels (options)Trading options based on key levels to take profit can be an effective strategy for investors looking to capitalize on short-term price movements. These key levels, also known as support and resistance levels, are often identified by analyzing historical price data and identifying areas where prices have previously stalled or reversed. By entering trades at these key levels and setting profit targets accordingly, investors can limit their risk and potentially realize gains in a shorter period of time. However, it's important to remember that trading options involves risk and investors should have a solid understanding of options trading and market analysis before attempting to use this strategy. I created these key levels on MSFT in December and they still hold true today. Red levels are daily levels, yellow are hourly levels, and orange are your intraday levels. By my estimation, MSFT looks like it will bounce and find resistance at $260, but if it breaks through, it should go to $264 next. However, if MSFT breaks down tomorrow, we should reasonably target a move to $253 and a further move would go to about $247. Todays price action was lackluster. There was a nice trendy move at the beginning of the day but it almost completely reversed itself. Watch the wavemaster indicator on lower timeframes for a sign of what is to come next. Right now, I see us in the middle of a range with a higher potential we bounce tomorrow vs go down.
NASDAQ:MSFT
by bitdoctor
CHATGPT crashes and Microsoft rejects the 200 day moving averageChatGPT went down world wide at the open of trading Monday causing MSFT to reject a retest of the 200 day moving average. This could add more structural problems in VOL since hedged equity and option flows are weakest until next week. My target is 248 today with an extension possible as low as 245. These AI fumbles are a good sign that AI is still a slave to human mistakes and errors. See how this plays out but certainly is a headwind to the bullish break higher in tech NVDA is another AI moving stock to watch if this is a rejection it may add to an acceleration to the downside. I'll likely get out of short position if ChatGPT comes back online.
NASDAQ:MSFTShort
by SPYvsGME
1
Disinflation – Fact or Fiction?CME: S&P Technology Select Sector ( CME_MINI:XAK1! ) The U.S. consumer price index (CPI) rose 0.5% in January and +6.4% year over year, reported the Bureau of Labor Statistics (BLS) on Tuesday. Excluding food and energy, Core CPI increased 0.4% monthly and 5.6% yearly. Economists surveyed by Dow Jones expected the headline CPI to grow 0.4% monthly and 6.2% yearly. Expectations for core CPI changes were 0.3% and 5.5%, respectively. On Tuesday, US stocks fell at open in response to the hotter-than-expected CPI report. But major indexes recovered somewhat at the close of the day. The Dow Jones Industrial Average slipped 156 points, or -0.46%, after initially losing over 300 points. The S&P 500 was flat at 4,136 (-0.03%), and the Nasdaq 100 gained 68 points to 11,960. US Treasury yields ticked higher. 2-year yield went up 94 ticks to 4.628%, while 10-year yield lifted 36 points to 3.755%. Bond investors widely expected the Federal Reserve to raise rates by 25 basis points to the 4.75%-5.00% range in March. Mega Trend in US Inflation While we usually focus on the percentage changes in inflation, CPI data are constructed as indexes, each using 1982-84 price data as a baseline at 100. January CPI reading of 299.170 is 0.8% above December of 296.797. It is up 6.4% from 281.148 in January 2022 (Data in this section is from Table 1 in the January CPI release). Interpretation: Today, the average price of goods and services in the U.S. is about 3 times as high as the price level from nearly four decades ago. This translates into a compound annual growth rate (CAGR) of 2.93% for the past 38 years. Insights: Long-run inflation rate is almost one percentage point higher than the Fed policy target. With less restrictive monetary policy on one hand, but more expansive fiscal policy on the other, the 2% goal appears to be far fetching. Barring a major recession, I expect the US inflation to stay above its 3% historical average in the foreseeable future. In the past four decades, cost of many consumer goods tripled in price, including Food (+219%), Energy (+183) and Core CPI (+202%). But there are noticeable outliners: • Tobacco and smoking products, +1289% • Motor vehicle insurance, +559% • Medical care services, +502% • New vehicle, +77% • Apparel, +28% January CPI Readings Before diving into the data, we should know that when BLS releases CPI data in February, it readjusts the weighting to account for the latest changes in the cost of living. For 2023, CPI weights are updated annually based on a single calendar year of consumer expenditure data. This reflects a change from prior practice of updating weights biennially. The changes of weighting by product and service category in the January report: CPI Category Old Weight New Weight Change Housing 46.40% 44.40% +2.0% Entertainment 5.70% 5.40% +0.3% Food 14.50% 14.40% +0.1% Clothing 2.50% 2.50% 0.0% Other 2.60% 2.70% -0.1% Medical 7.70% 8.10% -0.4% Education 5.20% 5.80% -0.6% Transport 15.30% 16.70% -1.4% Rising shelter costs accounted for nearly half the monthly price increase. The component accounts for more than one-third of the index and rose 0.7% on the month and was up 7.9% from a year ago. Energy also was a significant contributor, up 2% month over month (M/M) and 8.7% annually, while food costs rose 0.5% M/M and 10.1% annually. Food: Up 0.5% M/M in January from 0.1% in December. Annualized inflation is 10.1%. Energy: Up 2.0% M/M in January from -3.1% in December. Annualized gain is 8.7%, of which, gasoline (+1.9%), diesel (+27.7%), electricity (+11.9%), and natural gas (+26.7%). Shelter: Up 0.7% M/M in January from 0.8% in December. Up 7.9% Y/Y. Transportation: Up 0.9% M/M in January from 0.6% in December. Up 14.6% Y/Y. While the headline CPI ticks down from 6.5% to 6.4% on an annualized basis, January price increase of 0.5% is significantly higher than the December reading of +0.1%. Overall inflation level is undoubtedly on the way down, but price increases from food, shelter and transportation are very sticky and don’t normally go down once moving up. Is disinflation a fact or fiction? I think we are somewhere in between, in the Twilight Zone. The US Stock Market Narratives In the past three years, the stock market narratives have changed several times: • After the initial pandemic hit in March 2020, US stocks staged a very impressive bull run. Growth drivers were US companies innovating with new products and services and catering for “work-from-home” employees and “play-at-home” consumers. • 2022 started with a major geopolitical crisis, pushing stocks sharply down. Fed rate hikes from March 2022 dragged major stock indexes into bear market territory. • Since inflation peaked in July and the Core CPI reading confirmed it in October 2022, US stock market began to rebound, centering on the notion of “Fed Pivot”. More recently, investors are caught by conflicting economic data. • Unemployment at 50-year low vs. Big Techs pushing rounds of massive lay-offs; • Lower inflation rate vs. “Eggflation” and “Shrinkflation” that consumers experience; • Whether the Fed is hawkish or dovish depends on the next dataset. While investors try to make sense of all these, stock market moves sideways. The 30-day returns for Dow and the S&P are -0.83% and +3.20%, respectively. Are we at the beginning of a new bull market? Or is it a bear relief, a temporary rebound from a bear market? To make an assessment, you need to know how many more rate hikes could be (pick a number between 1 and 4), and what the terminal rate would be (5.0%, 5.25%, 5.5%, 5.75%, or 6.0%)? I have no idea. When uncertainty becomes the dominant narrative, it’s time to explore opportunities that promise more certainties. AI - New Engine for Economic Growth One visible exception is Nasdaq 100, which gained 8.9% in the past month. S&P Technology Select Sector Index (XAK) had an even higher return at 9.5%. Most Big Tech companies were vastly overstaffed during the pandemic years. With stock prices cutting by two thirds or more, they are forced to live within their cash flow. Cutting off unprofitable divisions and laying off redundant staff help them survive in leaner years. This is certainly very painful for the tech workers and their families. To investors, Big Tech may have transformed itself from lofty Growth Stock to reasonably priced Value Stock. For Nasdaq 100, its trailing 12-month price/earnings ratio was 34 one year ago. It is 23.63 as of February 10th, according to Birinyi Associates. Technological breakthroughs have always powered the US economy to growth for the long run. Personal computers in the 1980s, Internet for the 1990s, smart phones and mobile internet in the 2000s, and electric vehicles in the 2010s, just to name a few. In my opinion, Artificial Intelligence will be our economic growth engine in 2023. While AI is nothing new, the viral success of ChatGPT has sensationally changed the game. Its popularity among mainstream consumers helps form strong support for all publicly traded companies in the AI sector, not just for OpenAI and its parent Microsoft. While writing this report, I asked ChatGPT one question: “Please show me the top-10 publicly traded stocks in the AI field.” Below is his/her/its answer: 1. Alphabet Inc. (GOOGL) - Google's parent company, which has been investing heavily in AI technology and applications. 2. NVIDIA Corporation (NVDA) - A leading company in graphics processing units (GPUs), which are commonly used in AI training and inferencing. 3. International Business Machines Corporation (IBM) - A company that has been developing AI technologies for several years and has a range of products and services, including the Watson AI platform. 4. Microsoft Corporation (MSFT) - Microsoft has invested heavily in AI technologies, and its Azure platform provides a range of AI tools and services. 5. Amazon.com, Inc. (AMZN) - Amazon's AI capabilities are used in a variety of products and services, including the Alexa voice assistant and its recommendation algorithms. 6. Baidu, Inc. (BIDU) - A Chinese company that has been investing heavily in AI technologies, including natural language processing and autonomous driving. 7. Alibaba Group Holding Limited (BABA) - A Chinese company that has also invested heavily in AI technologies and has been developing AI applications for e-commerce, logistics, and finance. 8. Intel Corporation (INTC) - A company that has been developing hardware specifically for AI, including custom processors and FPGA chips. 9. Twilio Inc. (TWLO) - A company that provides cloud-based communication services and has been investing in AI technologies to improve its services. 10. Advanced Micro Devices, Inc. (AMD) - A company that develops CPUs and GPUs for AI applications and has been investing heavily in machine learning and other AI technologies. This is very impressive. ChatGPT not only gives me a list of the companies, but also highlights each company’s involvement in the AI field. Since ChatGPT doesn’t have data beyond 2021, we come back to our trusty TradingView to pull out 1-year return charts. What a brutal year! Only Microsoft manages to gain 5.3%. The rest in the list had negative returns from -10% to 40%. Twilio is the loss leader, yielding -66.8% in the last 12 months. This drives home the two major risks in new technology investing: Firstly, at an early stage, you have no idea which technology will win out at the end. Is it direct current (DC) or alternative current (AC)? Airship or Aircraft? VHS or Betamax? Cable TV or satellite TV? And TDMA or GSM for cellular signal? Secondly, you do not know which company will become a leader in a winner-take-all market. If you go back in time and invest in the new automobile industry in 1908, you have a 99% chance of losing money, unless you luckily picked Ford, General Motors, or Chrysler out of the 253 publicly traded automakers. Likewise, if you invested in mobile phone companies in early 2000, you likely picked Motorola, Blackberry, Ericsson, or Nokia. However, when an outsider Apple launch a breakthrough product, iPhone 1 in 2007, it knocked out all leading cellphone markers and became the ultimate winner. Right now, I predict that most electric vehicle makers will go out of business in five years, except for Tesla, and maybe BYD. The Case for S&P Technology Select Sector Index Consistently picking winners in emerging technologies is extremely difficult. Even the smartest stock picker could not beat the market. Take Cathy Wood’s Ark Innovation ETF (ARKK) as an example, its cumulative returns comparing to the Nasdaq 100 were: • 1-year: -42.8% vs. -12.4%; • 5-year: -2.1% vs. +85.1%; • Since Inception (8-year): +100.3% vs. +203.5%. Diversification is a very powerful concept in investing, notably in times of uncertainty. Concentrating on stock picking, many active managers tend to cloud objective assessment with their own conviction and lose sight of potential market leaders amid emerging mega trends. Passive investment via index futures focusing on the high-tech sector allows us to express our conviction and capture emerging trends. XAK is one of the 11 sector indexes in the S&P 500. Its top holdings are Apple (AAPL), Microsoft (MSFT), Nvidia (NVDA), Visa (V), Mastercard (MA), Broadcom (AVGO), Cisco (CSCO), and Adobe (ADBE). My research shows that S&P Technology Select Sector (XAK) outperformed many Big Tech stocks and ETFs in both short-term and long-term. According to Fact Sheet published by S&P, as of January 31st, the annualized historical returns are -15.22% (1Y), 13.82% (3Y), 16.22% (5Y) and 18.48% (10Y). Total returns since inception are 6,425.9%. You may invest in one of the technology sector ETFs, such as SPDR XLK, iShare IYW, and Vanguard VGT. But CME E-Mini S&P Technology Select Sector Index Futures (XAK) has distinguished features over ETFs. Firstly, XAK has five quarterly contracts to choose from: March, June, September, December and March 2024. This allows us to evaluate strategies focusing on expected future value of the index, up to 1 year ahead. Secondly, you could place either Long or Short position, allowing both bullish and bearish strategies to implement. Thirdly, initial margin of placing 1 contract is approximately 35% of the notional value. This built-in leverage could enhance the returns if market moves in the right direction. Finally, by holding a long position on the quarterly futures contract and rolling it each quarter, investors could replicate the strategy of holding the stocks or the ETFs. Happy Trading. Disclaimers *Trade ideas cited above are for illustration only, as an integral part of a case study to demonstrate the fundamental concepts in risk management under the market scenarios being discussed. They shall not be construed as investment recommendations or advice. Nor are they used to promote any specific products, or services. CME Real-time Market Data help identify trade set-ups and express my market views. If you have futures in your trading portfolio, check out on CME Group data plans in TradingView that suit your trading needs www.tradingview.com
CME_MINI:XAK1!Long
by JimHuangChicago
3
ChatGPT will be the game changer.Reason to invest into ChatGPT 1. Microsoft invested over $10billion into ChatGPT 2. ChatGPT sets record for fastest-growing user base . (Feb 1 (Reuters) - ChatGPT, the popular chatbot from OpenAI, is estimated to have reached 100 million monthly active users in January, just two months after launch, making it the fastest-growing consumer application in history, according to a UBS study on Wednesday.) It took TikTok about nine months after its global launch to reach 100 million users and Instagram 2-1/2 years, according to data from Sensor Tower. 3. ChatGPT has finally make their product workable and catch Microsoft and the world attention. Company such as Google, Baidu, Tencent (wechat) all started to sing into the same AI trend. FOMO from investors may start to pour in soon. 4. Even though price have increased from $10 low to recent $30 high, but it never too late to join the party. If you are still holding onto Meta waiting for Metaverse (which not yet ready), why not try this ChatGPT. 5. Base on chart, few price level to do DCA if you have invested in. ($22, $20, $18, $15) 6. Lastly, I have entered into it and prepare to do DCA along the way.
NYSE:AILong
by probabilityta
1