Get started

Machinelearning

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
Sunny🌞 (Confidence: 1.0 )🌞 Good news for investors! Based on the chart index, it seems like the weather in the bitcoin world will be sunny ☀️. With a confidence level of 1.0, which is higher than the baseline of 0.864, it's a positive sign for the future of Bitcoin. The current price of Bitcoin has been consistently high, with a low of 28890 and a high of 29146. The volume of Bitcoin traded in the past hour is also significant, which shows that there is a lot of activity happening in the market. 📈 Additionally, the Exponential Moving Averages (EMA) for the past hour, including EMA9, EMA21, EMA50, EMA100, and EMA200, all indicate that the trend is bullish. This suggests that the price of Bitcoin will continue to rise in the future. 🚀 The Relative Strength Index (RSI) is also very high, standing at 82, indicating that Bitcoin is currently overbought. However, this doesn't necessarily mean that the price will drop in the short term. The Fast Stochastic Oscillator is showing a reading of 79, and the Slow Stochastic Oscillator has a reading of 86, both of which are in bullish territory. The Moving Average Convergence Divergence (MACD) is also showing a positive reading of 451, which is another positive sign for the price of Bitcoin. 💰 Overall, the Bitcoin market seems to be in a good position, and it's a great time to invest in Bitcoin. However, as with any investment, there is always a certain amount of risk involved. It's important to do your own research and make informed decisions before investing in any asset. 🧐
BINANCE:BTCUSDLong
by ChatGPT_Bitcoin_Forecaster
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
How can AI help to improve algorithmic trading strategies?AI is transforming the field of algorithmic trading, which involves using computer programs to execute trades based on predefined rules and strategies. AI can help to improve algorithmic trading performance and efficiency by providing advanced data analysis, predictive modeling, and optimization techniques. In this article, we will explore some of the ways that AI can enhance algorithmic trading and some of the challenges and opportunities that lie ahead. One of the main advantages of AI in algorithmic trading is its ability to process and interpret large and complex data sets in real-time. AI algorithms can leverage various sources of data, such as market prices, volumes, news, social media, sentiment, and historical trends, to identify patterns, correlations, and anomalies that may indicate trading opportunities. AI can also use natural language processing (NLP) and computer vision to extract relevant information from unstructured data, such as text, images, and videos. Another benefit of AI in algorithmic trading is its ability to learn from data and adapt to changing market conditions. AI algorithms can use machine learning (ML) and deep learning (DL) techniques to train on historical and live data and generate predictive models that can forecast future market movements and outcomes. AI can also use reinforcement learning (RL) techniques to learn from its own actions and feedback and optimize its trading strategies over time. A further aspect of AI in algorithmic trading is its ability to optimize trading performance and reduce costs. AI algorithms can use mathematical optimization methods to find the optimal combination of parameters, such as entry and exit points, order size, timing, and risk management, that can maximize profits and minimize losses. AI can also use high-frequency trading (HFT) techniques to execute trades at high speeds and volumes, taking advantage of small price fluctuations and arbitrage opportunities. AI can also help to reduce transaction costs, such as commissions, fees, slippage, and market impact, by using smart order routing and execution algorithms that can find the best available prices and liquidity across multiple venues. However, AI in algorithmic trading also faces some challenges and limitations that need to be addressed. One of the main challenges is the quality and reliability of data. AI algorithms depend on accurate and timely data to perform well, but data sources may be incomplete, inconsistent, noisy, or outdated. Data may also be subject to manipulation or hacking by malicious actors who may try to influence or deceive the algorithms. Therefore, AI algorithms need to have robust data validation, verification, and security mechanisms to ensure data integrity and trustworthiness. Another challenge is the complexity and interpretability of AI algorithms. AI algorithms may use sophisticated and nonlinear models that are difficult to understand and explain. This may pose a problem for traders who need to monitor and control their algorithms and regulators who need to oversee and audit their activities. Moreover, AI algorithms may exhibit unexpected or undesirable behaviors or outcomes that may harm the traders or the market stability. Therefore, AI algorithms need to have transparent and explainable methods that can provide clear and meaningful insights into their logic and decisions. However, there are also ethical and social implications of AI in algorithmic trading. AI algorithms may have an impact on the market efficiency, fairness, and inclusiveness. For example, AI algorithms may create or amplify market inefficiencies or distortions by exploiting information asymmetries or creating feedback loops or cascades. AI algorithms may also create or exacerbate market inequalities or exclusions by favoring certain groups or individuals over others or by creating barriers to entry or access for new or small players. Therefore, AI algorithms need to have ethical and social principles that can ensure their alignment with human values and interests. In conclusion, AI is a powerful tool that can help to improve algorithmic trading strategies and performance by providing advanced data analysis, predictive modeling, and optimization techniques. However, AI also poses some challenges and risks that need to be addressed by ensuring data quality and reliability, algorithm complexity and interpretability, and ethical and social implications. By doing so, AI can create a more efficient, effective, and equitable algorithmic trading environment for all stakeholders.
BITSTAMP:BTCUSDEducation
by JS_TechTrading
SOL Bearish Continuation According to Deep LearningThis post is a continuation of my ongoing efforts to fine-tune a predictive algorithm based on deep learning methods, and I am recording results in the form of ideas as future reference. Brief Background: This algorithm is based on a custom CNN-LSTM implementation I have developed for multivariate financial time series forecasting using the Pytorch framework in python. If you are familiar with some of my indicators, the features I'm using are similar to the ones I use in the Lorentzian Distance Classifier script that I published recently, except they are normalized and filtered in a slightly different way. The most critical I’ve found are WT3D, CCI, ADX, and RSI. The previous post in this series: As always, it is important to keep in perspective that while these predictions have the potential to be helpful, they are not guaranteed, and the cryptocurrency market, in particular, can be highly volatile. This post is not financial advice, and as with any investment decision, conducting thorough research and analysis is essential before entering a position. As in the case of any ML-based technique, it is most useful when used as a source of confluence for traditional TA. Notes: - Remember that the CCI Release is tomorrow and that this model does not consider additional volatility from this particular event. - The new DTW (Dynamic Time Warping) Metric is an experimental feature geared towards assessing how reliable the model's prediction is. The closer to 0 this number is, the more accurate the prediction.
Short
by jdehorty
6
SOL Next Leg according to Deep LearningThis post is a continuation of my ongoing efforts to fine-tune a predictive algorithm based on deep learning methods. Last post in this series: Previously, the algorithm correctly projected SOL's breakout to the upside following SOL's consolidation at around the $16 mark. As a next leg, the algorithm predicts that a noticeable continuation to the upside is likely in the coming days, and I am posting this prediction here for future reference. As always, it is important to keep in perspective that while these predictions have the potential to be helpful, they are not guaranteed, and the cryptocurrency market, in particular, can be highly volatile. This post is not financial advice and as with any investment decision, conducting thorough research and analysis is essential before entering a position.
Long
by jdehorty
11
SOL Breakout according to Deep LearningA deep learning algorithm that I am currently working on predicts that the price of SOL (Solana) will experience a breakout to the upside in the coming days. I am posting this prediction to have it recorded for future reference. Deep learning algorithms are a type of Machine Learning algorithm designed to learn and improve their performance over time through training on large datasets. In the case of predicting the price of SOL, the algorithm has analyzed historical feature data, which I have spent a considerable amount of time selecting/wrangling. Using this data, the algorithm has identified patterns/trends that suggest an upward breakout is likely to occur, as shown in the included screenshot. It is worth noting that while these predictions can be helpful, they are not guaranteed, and the cryptocurrency market, in particular, is highly volatile. As with any investment, conducting thorough research and traditional technical analysis is critical before opening a position.
Long
by jdehorty
9
open ai is a nice fun toolpeople have used it to cheat on university exams. people with no coding experience have used it to develop software. people use it to penetration test vulnerabilities in networks. its all cloud based supercomputing. does this mean openai is going to change the world? no. does it mean microsofts cloud computing business is saved? no. does that mean its a good investment? yes. obviously bulls got power bomb suplexed back into the dirt at the end there, but its as if they dont care. as long as were buying the rumor, selling the news, im going to assume theres more rumors, and more news. aquisitions and debt to assets peaked after trumps election, and were rubbing up against corona bottom anchored vwap, and top of regression. if these metrics continue bull, im long, and if we resist and move lower im bear.
NASDAQ:MSFT
by cerealpatterns
Automatic Gamma Levels Spot alternative on TradingView? Sure!By combining Options expiration data, DarkPool & process it by Machine Learning algorithm, we can get another perspective of market picture. One of the services is already trying to provide this data, however so far it requires manual work in order to apply those levels on the chart. I provide a way for automatic level recognition valid for intraday session, based on data calculated by ML/AI algorithm, exported to Quandl database and imported to TradingView indicator. When you add to that Call & Put Wall levels I publish on website, you will have all information in hand that you can use for intraday trading. I provide current for the moment of writing analysis snapshot of 4 majors with applied Gamma Levels as well as key levels from Options&Darkpool data market. If you want to learn more and start having edge on the market (information is the key!), you will for sure find more details at my profile ;) I wish you all Big Profits on the market!
NYMEX:CL1!
by skyline-trader
2
Oil shift to downtrend? Options and AI whispersWe can observe at the beginning of this week major shift in sentiment of the traders with Oil in their portfolio. As previous week still we saw money put on 130$, this week the last major level as Call Wall is at 100$. I would even include scenario that around that level we can go slightly higher due to Virgin VPOCs at 102 and 104.5 - still though it should be short-term run and only possible if wet closer to 100$ first. So far we can observe downtrend on Oil. If 92.2$ will be broken, downtrend can continue. The next major Options and Darkpool markets Support lays at 80$. Before that there is no other level (so far) that can stop this move.
NYMEX:CL1!
by skyline-trader
Deep Learning AI's Ascending Channel - A Neuralnet AnalysisDeep Learning AI's Ascending Channel - A Neuralnet Analysis Target: $37k. Chartpattern: Parallel Channels (Ascending - high probability bearish breakdown) Technical indicators support: Relative Strength Index (RSI - bearish divergences) AI painted the chart using TradingView's native charting tools. Analysis: we used Google ML "Firebase" Toolkit, OXYBITS Space Invariant Artificial Neural Networks. 100% bots, zero humans, DYO before investment.
BITSTAMP:BTCUSDShort
by Elysian_Mind
3
Fall potential on Gold #options #ai #mlOption data and AI algorithm from analysis data from this market show the possibility of retreat from growth and switch to Bears taking control over. We have a powerful resistance (supply zone) around 2060, and Virgin VPOCs can be found only below the current price level (for a moment of writing analysis). Current option support is 1881 and this is a key level when it comes to potential closure of the downward movement. To Gamma Flip (whose exceeding will increase the volatility on the market) still far (1786), but it is worth observing what will happen on subsequent sessions. Falls will be negated after exceeding 1975. I take into account all Expiration from the gold option, which are then treated by the Machine Learning script. AI in this case shows the main key levels on the market and conclusions from data analysis. They are exported to the Quandl base and then imported to TradingView. Data is also published every day a week, on my website. Remember - knowledge and data are power, in this case, increasing significantly a chance for profitable trading :)
COMEX:GC1!
by skyline-trader