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Cloudy☁️   (Confidence: 0.26 )🌤️ Welcome to the Bitcoin weather forecast! 🌤️ Unfortunately, I have some cloudy news for Bitcoin investors. ☁️ Looking at the chart index for the past hour, the confidence level that the weather in the Bitcoin world will be sunny is only 0.26, which is significantly less than the baseline of 0.864. Let's take a closer look at the data. The opening price was 29187, and the high was 29264, while the low was 29174. The closing price was 29214. This indicates that there has been some volatility in the market, but overall the price has remained relatively stable. In terms of technical indicators, the exponential moving averages (ema) have been trending upwards, with ema9 at 29078, ema21 at 28988, ema50 at 28855, ema100 at 28786, and ema200 at 28796. The relative strength index (rsi) is at 61, which suggests that Bitcoin is neither overbought nor oversold. However, the fast and slow stochastic oscillators (fast_k at 62, slow_k at 59, and slow_d at 51) indicate that there may be some bearish pressure on the price. Additionally, the Moving Average Convergence Divergence (macd) is negative at -83, which also indicates a bearish trend. Overall, the Bitcoin weather forecast is looking cloudy, and investors may want to exercise caution in the short term. Keep an eye on the technical indicators and be prepared for potential volatility in the market. ☁️💰💻
BINANCE:BTCUSD
by ChatGPT_Bitcoin_Forecaster
Pearson down 11% on AI worriesIf AI is eating your lunch, your company better have a good strategic plan to adjust and create new revenue streams. This is classic Marketing myopia, e.g. US railways, in mid 20th century, saw market share eroded by the airlines. That's because railroads never saw themselves in the transportation business, and limited themselves themselves by thinking they were in Railroads alone. Pearson has had to deal with all sorts of changes, especially technology, and I'm sure they will have a strategic plan. Technicals: Bearish reversal now under 50 weekly EMA, but volume isn't high so early days. Below 756 would confirm a new downtrend.
LSE:PSON
by andmk2
Sunny🌞   (Confidence: 1.0 )🌞 Good news for bitcoin investors! 🚀 Based on the chart, the bitcoin weather seems sunny ☀️ with a high confidence level of 1.0. The opening price of 28498 has been followed by an even higher closing price of 28505, with a high of 28528 and a low of 28433. 📈 The exponential moving averages (EMA) show an upward trend with the EMA9 at 28536 and the EMA21 at 28447, while the EMA50 and EMA100 are also on an upward trajectory at 28471 and 28617 respectively. The EMA200 is also showing bullish sentiment at 28668. 💹 The relative strength index (RSI) of 54 and fast_k at 61, indicate moderate bullish sentiment, with the slow_k and slow_d both in the bullish zone at 65 and 69 respectively. The moving average convergence divergence (MACD) also shows positive momentum, with a value of 256. 💰 With all these factors combined, the bitcoin market seems to be in a healthy position for investors, with the potential for further gains in the near future.
BINANCE:BTCUSD
by ChatGPT_Bitcoin_Forecaster
Sunny🌞   (Confidence: 1.0 )🌤️ Bitcoin Weather Forecast 🌤️ It's looking like sunny skies ahead for Bitcoin! ☀️ In the past hour, Bitcoin's price opened at 28158 and climbed as high as 28384, with a low of 28000. The closing price was 28350, above the ema9 of 28446, but below the ema21 of 28655. Despite this, the long-term trend is still looking good with the ema50 at 28908, ema100 at 28951, and ema200 at 28807. Although the RSI is only at 36, indicating oversold conditions, the fast_k is at 50 and the slow_k is at 31, suggesting a potential bullish momentum. The slow_d is at 26, which may signal a continuation of the bullish trend. Overall, with a high confidence level of 1.0, it's looking like a good time to invest in Bitcoin. However, it's always important to keep an eye on market trends and adjust your strategy accordingly. Happy trading! 💰💻📈
BINANCE:BTCUSDLong
by ChatGPT_Bitcoin_Forecaster
AMD to resume ZIG ZAG UPSIDEAs we analyze the 4 hour chart of AMD we have continued to seem the same pattern inside the "macro" blue channel and I believe we continue to grind higher inside this channel. As you can see there are 3 other channels inside the blue channel (the white channels) which show a "bullish" correction then a push higher every time. I am looking for AMD to do the same thing until we see bearish signs (price heading out of the blue channel)... Until then I will continue to play the saying "trend is your friend"... Let's continue to look for upside on AMD.
NASDAQ:AMDLong
by UnknownUnicorn910751
BCAN penny stock microcap combining AI and Cannabis LONGBCAN is a microcap in the AI serving the cannabis industry. It's software is patent protected. It is currently trading at less than 25% of its all time high and has a history of volatile spikes. This is a low float stock with insiders holding significant positions. On the one hour chart, in the past couple of days, price has descended from the POC line of the volume profile down to sit on top of the demand zone as shown by the LuxAlgo indicator. The Wycoff volume oscillator shows the corresponding selling volume dominating over buying volume. I see this as a long trade with targets of the POC line and the top of the high volume area of the volume profile. The overall profit of about 17-18% . The stop loss is set at 5% below the entry in the demand zone.
BLong
by AwesomeAvani
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
C3.ai 30% Gain in the next 11 daysOK Traders. Look at the Timed Cycles on the top of my chart and the trading pattern that AI has undergone since early February. I for one am not going to let this pass me by again. Likely by Wednesday I will take a large Position as the potential for a 30% gain within 11 days is very real. I firmly believe it's worth the risk for less than 2 weeks time. The Stochastic RSI appears to be confirming this as well with the same repeating pattern. This stock is Highly Manipulated so make sure you have set Trailing Stops on your positions. As well you can see where the potential top will be so be prepared to Short near the top if you are so inclined with your trading strategy.
NYSE:AI
by grahammk
4
$AI Holding Support?I’ve taken a small position in NYSE:AI here as it seems to have put in a support level. In addition, the selling volume has declined quite a bit suggesting to me that sellers are pretty much done. If we have a pocket pivot in the next few days / weeks, I will bring it up to a full position size. I am using the support area as my stop guideline. If it fails, then it proves that was not a supported area. For my trading style I like the risk reward ratio here. Thanks for looking. Ideas, not investment / trading advice.
NYSE:AILong
by jaxdog
3
HOW WILL AI AFFECT FINANCIAL MARKETS?Artificial Intelligence (AI) is revolutionizing the financial markets, with its algorithms and automated systems allowing for faster and more accurate trading decisions. AI technology has already seen success in stock market trading, but it is now being used to analyze data from all areas of finance, including banking and investments. In this article, we will explore the advantages and challenges posed by AI-based trading systems, as well as potential opportunities for AI in the future of financial markets. Finally, we will provide guidance on how to prepare for the impact of AI on financial markets. 1. Understanding AI and its Impact on the Financial Market Artificial Intelligence (AI) is an advanced technology that has been used in a variety of industries to automate tasks and make decisions. In the financial markets, AI can be used to analyze large amounts of data quickly and accurately. It can recognize patterns, identify trends, and even predict outcomes in order to generate trading signals for investors. The potential implications of AI in the financial markets are vast. AI-based systems can be used to streamline trading processes, reduce risk, and increase profitability. However, there are also drawbacks associated with using AI in finance that must be considered. For example, AI systems may lack the human intuition needed to make sound decisions during volatile market conditions or when dealing with complex security types. AI-based systems have already demonstrated their ability to recognize certain trends and patterns in financial data. For instance, AI has been used successfully by traders to detect price movements before they occur and capitalize on them accordingly. Similarly, these systems can also identify correlations between different asset classes or sectors over time, allowing investors to diversify their portfolios more efficiently. Finally, there are a number of examples of successful applications of AI in finance already taking place around the world. Hedge funds have adopted machine learning algorithms for portfolio optimization; banks have leveraged natural language processing (NLP) technologies for customer service; and stock exchanges have implemented automated surveillance solutions for fraud detection. All of these examples demonstrate how powerful AI can be when it comes to making decisions within the financial markets. 2. Advantages of AI in Trading AI has the potential to revolutionize how trading is conducted in financial markets. By leveraging the power of AI, traders can gain an edge in the markets and improve their chances of success. Here are some of the main advantages of using AI in trading: 1. Quick and Accurate Analysis: AI-based systems are capable of quickly analyzing large amounts of data and providing accurate market insights. This helps traders make faster, more informed decisions about when to buy or sell a particular asset. It also reduces the risk associated with manual analysis, as there is less chance for human error to enter into decision making processes. 2. Identifying Profitable Opportunities: AI-based systems are able to identify profitable opportunities that may otherwise be overlooked by manual analysis. This allows traders to capitalize on positive trends and maximize returns from their investments. 3. Identifying Risks: AI-based systems can also help identify risks associated with certain trades or investments, allowing traders to mitigate these risks before acting on them. This helps reduce losses and improves overall profitability for investors and traders alike. 4. Automated Decision Making: AI-based systems can automate certain aspects of trading decisions, eliminating the need for manual input or assistance from a human trader/investor. This reduces errors associated with manual decision making processes, while increasing efficiency and accuracy when it comes time to execute trades or invest in assets. 5. Lower Overall Costs: Finally, using an AI-based system helps reduce overall costs associated with trading due to its ability to automate certain processes and eliminate errors associated with manual decision making processes. This can help improve profitability for investors/traders over time by reducing expenses related to trading activities such as commissions, fees, etcetera 3. Future Opportunities for AI in Financial Markets The potential of Artificial Intelligence (AI) in the financial markets is immense. It has the power to revolutionize how traders and investors make decisions, identify new opportunities, and reduce risk. AI-based systems are able to automate processes and improve accuracy in decision making - providing a competitive advantage to those who utilize it. Additionally, algorithmic trading can give an extra edge by increasing efficiency when predicting market trends and stock prices. Synthetic assets are another way that AI is being employed in the financial sector. These products can provide investors with exposure to investments not typically offered on traditional markets or products. Furthermore, AI helps organizations create effective risk management strategies by recognizing potential risks quickly and offering guidance on how to prevent them from occurring. AI has already been utilized by some of the world's largest banks as a way to gain insight into the complexities of financial markets; giving businesses access to innovative investment strategies and new growth prospects within their organization. As this technology develops further, now is the perfect time for corporate entities to prepare for its impact on their operations so they can take full advantage of its many advantages when they arise. In summary, AI offers a great opportunity for traders and investors alike in terms of achieving higher returns while minimizing losses through improved decision making processes, enhanced analysis effectiveness, and more precise predictions about stock prices and market trends. With its rapid evolution continuing apace, it’s essential for companies operating in the financial industry to start preparing now for what lies ahead so they can capitalize on all that this powerful technology has to offer them in future years! 4. Challenges Faced by AI in Financial Markets AI is a powerful tool for understanding and predicting financial markets, but it does come with certain challenges that must be addressed in order for it to become a viable tool. Below, we will explore the five main challenges facing AI when applied to financial markets. Developing Reliable Algorithms: Developing reliable algorithms is essential for successful AI trading systems. It is important to ensure that investors are not exposed to unnecessary risks due to inaccurate predictions or unreliable models. In order to minimise such risks, developers need to carefully tweak existing AI algorithms and develop new ones that can accurately predict market outcomes. This requires complex mathematical models as well as an in-depth understanding of the data being analyzed. Ensuring System Security: Financial markets involve sensitive information which needs to be kept secure at all times. As such, security should be one of the top priorities for any organization utilizing AI in finance. Strong passwords and authentication protocols should be implemented and regularly tested, while any vulnerabilities should be actively monitored and patched immediately. Additionally, organizations should use encryption techniques such as Secure Socket Layer (SSL) or Transport Layer Security (TLS) whenever possible when transmitting or storing data on their servers or networks. Predicting Ethical Implications: The ethical implications of using AI in finance also need to be considered before integrating these technologies into existing systems and processes. This includes analyzing how decisions made by these systems could affect individuals or groups of people – both positively and negatively – as well as exploring potential legal ramifications of using AI-based trading systems. Organizations must consider these issues carefully before deploying any new technology in their operations and ensure they have the necessary safeguards in place if needed. Responding To Unstructured Data: Another challenge associated with using AI in finance is its ability to handle unstructured data accurately in real-time. Unstructured data can come from sources such as news stories, social media posts, customer feedback surveys etc., all of which can offer valuable insights into current market trends and conditions that may not otherwise be apparent from structured numerical data alone. As such, developing algorithms which can effectively interpret this type of data is an important area of research for financial institutions looking to utilize the power of AI in their operations. Exploring Long-Term Implications: Finally, organizations must consider the long-term implications of utilizing AI technologies when making decisions related to their financial operations. This includes considering whether there will be any unintended consequences associated with relying too heavily on automated decision making processes; whether there are sufficient safeguards against manipulation by malicious actors; and whether there are strategies in place which enable companies to remain competitive over time without sacrificing customer privacy or other ethical considerations.. Ultimately, organizations need to think carefully about how they integrate AI into their existing infrastructure before taking action so they can make informed decisions about how best utilize this technology going forward 5. How to Prepare for the Impact of AI on Financial Markets As AI continues to gain prominence in financial markets, companies must be proactive in understanding the risks and benefits of incorporating it into their trading strategies. To get ready for the impact of AI on financial markets, a strategic approach is necessary that includes comprehending how regulatory bodies interact with this technology, identifying potential partners who can help navigate its complexities, and remaining aware of advancements with AI. Here are several tips to prepare: 1. Assess Risks & Benefits: Investigate current trends in AI to detect both possibilities and drawbacks. Additionally, familiarize yourself with rules or laws related to using AI in finance industries so you can ensure following regulations while still gaining from its benefits. 2. Design Strategies: Develop tactics that maximize advantages while minimizing risks. This may include automating processes or creating algorithms that enable you to recognize opportunities quickly and make wise decisions faster than before. Consider partnering up with experts who understand integrating AI into existing infrastructure and procedures. 3. Stay Updated: Companies running finance businesses must be cognizant of new technologies like artificial intelligence so they remain competitive without compromising customer privacy or other ethical standards--this entails subscribing to industry news sources, attending conferences such as FinTech Connect Live!, reading industry blogs such as FintechToday or TechCrunch’s Fintech section among other options! 4. Analyze Regulatory Bodies: Organizations operating within the finance sector should have an idea on how regulatory bodies view machine learning applications when it comes to making decisions within the organization--this data will help them stay compliant without sacrificing customer confidentiality or other moral considerations by providing guidance on acceptable usage policies or suggesting alternate options if one is disapproved by a certain body plus researching various jurisdictions' regulations depending where services need be offered globally.. 5. Find Partnerships: Experienced partners may be essential when introducing artificial intelligence into your operations--not only they provide technical support but also share advice on merging machine learning applications into existing infrastructure and processes as well as helping produce suitable usage policies meeting all applicable regulation standards across global locations.. Cooperating allows leveraging resources more efficiently plus benefiting from shared experiences thus increasing success chances! By taking these steps, companies operating within financial sectors can benefit from any opportunities presented by artificial intelligence while avoiding associated risks—ensuring their compliance is met without endangering customer confidentiality or other ethical issues along the way! Traders, if you liked this idea or if you have your own opinion about it, write in the comments. I will be glad 👩‍💻
SP:SPXEducation
by Lingrid
14
CyberAggent At the Supply Line of an Ascending Broadening WedgeCyberAgent currently sits at the Supply Line of a long established Ascending Broadening Wedge and while my first instinct would usually be to short, I think this one is showing signs that it will give us a strong bullish reaction off of this supply line as we have a bunch of Bullish Divergence on the MACD and RSI and are at the 55 Moving Average on the 2 Month while trading in a very tight more local falling wedge pattern. Upon Breakout i think we could go up to make a 0.886 Retrace before ultimately confirming a Partial Decline and Breaking Down. But in the meantime im very bullish here.
TSE:4751Long
by RizeSenpai
AI, Reversal to the UPSIDE in order..AI is consolidating at 20-22 price levels -- a strong order block support which has been tested quite a few times already. Net buying / accumulation has been spotted at the current levels forming 4H higher lows. Expect a reversal to the upside from this price point. Spotted at 22.0 TAYOR. ----------------- LAST QTR results Jan 2023 for reference: Jan 2023EPS beat by 72.57% EPS (USD) Expected -0.22 Reported -0.06 Surprise 72.57% Revenue (USD) Expected 64.25M Reported 66.67M Surprise 3.77%
NYSE:AILong
by JSAL
Market Sentiment and Trend Prediction System. Predictive Model. The codes listed below (free&easy;), detailed steps to follow for developing the event prediction system: 1. **Collecting Data**: we will need to gather data from various sources. We can use Python-based web scraping libraries like Beautiful Soup and Scrapy to extract data from news websites and social media platforms (scraping exports data from websites, it is safe and legal, but better contact website admins and ask for authorization) 2. **Cleaning and Preprocessing Data**: After collecting the data, we need to clean and preprocess it. We can use Python libraries like Pandas and NumPy to remove duplicates, missing values, and irrelevant information. 3. **Natural Language Processing**: Once the data is cleaned, we can use natural language processing (NLP) techniques to extract insights from the text data. For example, we can use the NLTK library to perform tokenization, stemming, and lemmatization on the text data. 4. **Model Building**: We can use machine learning algorithms like Random Forest, Gradient Boosted Trees, or Support Vector Machines (SVMs) to build predictive models. These models can help us predict the occurrence of an event or the sentiment associated with a specific topic. 5. **Dashboard and Visualization**: Finally, we can create an intuitive dashboard using tools like Tableau or Power BI to display the analyzed data in real-time. We can use interactive visualizations like bar graphs, pie charts, and heat maps to provide users with a clear understanding of the events and their impacts. 6. **Testing and Deployment**: Once the system is developed, we need to test it thoroughly to ensure that it is delivering the expected results. We can use various testing frameworks like pytest, unittest, or nosetests to automate the testing process. Once testing is completed, we can deploy the system to the production environment. 7. **Regular Maintenance and Updates**: We also need to ensure that the system is continuously monitored. The codes : Termux (the app is in playstore, github etc, to excute python files, or commands,for every step, some general commands and libraries that you might find useful: 1. Collecting Data: - To install Scrapy, run `pip install scrapy`. - To install Beautiful Soup, run `pip install beautifulsoup4`. - To scrape data from a webpage using Scrapy, run `scrapy crawl `. - To scrape data from a webpage using Beautiful Soup, use Python's built-in `urllib` or `requests` module to fetch the webpage's HTML. Then, use Beautiful Soup to parse the HTML and extract the relevant data. 2. Cleaning and Preprocessing Data: - To install Pandas, run `pip install pandas`. - To install NumPy, run `pip install numpy`. - To remove duplicates, use Pandas' `drop_duplicates()` function. - To remove missing values, use Pandas' `dropna()` function. - To filter out irrelevant data, use Pandas' indexing functions like `loc` and `iloc`. 3. Natural Language Processing: - To install NLTK, run `pip install nltk`. - To perform tokenization, run `nltk.tokenize.word_tokenize(text)`. - To perform stemming, run `nltk.stem.PorterStemmer().stem(word)`. - To perform lemmatization, run `nltk.stem.WordNetLemmatizer().lemmatize(word)`. 4. Model Building: - To install scikit-learn, run `pip install scikit-learn`. - To instantiate a Random Forest classifier, run `from sklearn.ensemble import RandomForestClassifier; clf = RandomForestClassifier()`. - To fit the model to the data, run `clf.fit(X_train, y_train)`, where `X_train` is the input data and `y_train` is the output labels. - To use the model to make predictions, run `clf.predict(X_test)`. 5. Dashboard and Visualization: - To install Tableau, follow the instructions on their website. - To install Power BI, follow the instructions on their website. - To create a bar graph in Python, use the `matplotlib` library: `import matplotlib.pyplot as plt; plt.bar(x, y); plt.show()`. - To create a pie chart in Python, use `plt.pie(values, labels=labels); plt.show()`. - To create a heat map in Python, use `sns.heatmap(data, cmap='coolwarm'); plt.show()` (assuming you have installed the Seaborn library). These are general commands and libraries that you can use as a starting point. If you need me to explain how to use termux, let me know.
Education
by Moroccolover
3