🌞 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,...
🌤️ 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...
🌞 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...
🌥️ 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...
Price now: 28.2k USD Range: (25.6, 29.5) Expected correction -2.4%
Big Data and Ai is the future that will be eseential by 2030 connecting machines to \facilitate smart contracts etc. this can be a leader if it plays it very well similar to LUNA FTM POLYGON ... it just needs the right mix of team to make it in the big cap league for the few like Google Microsoft TESLA etc are already in place which i think are on the lookout...
This 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...
these strategies are signaling the consolidative move isnt over, and revisiting mean and regression is likely theres no way to prove at the moment we will go through a phase like this, but if the opportunity presents itself its a path that mathematically makes sense
This 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...
A 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...
people 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...
Averaging Statistical Arbitrage with Reduced Allocation
example of low frequency high probability patterns
testing the statistical arbitrage strategy for 6 months
Example of an arbitrage strategy between correlated assets
Fibo levels: AB=0.61 XA BC=0.61 AB=$6.65 0.78 XA=$63 1.6 BC=$70 0.88 XA=$94 2 BC=$120 2.24 BC=$169 1.13 XA=$250 2.6 BC=$288 1.41 XA=$763 3.6 BC=$1189 1.6 XA=$1752
This chart was created to accompany a blog post which explores leveraging machine learning (RNN: LSTM) using Tensorflow Keras and SHAP to determine which factors (indicators and correlations with Macro, such as oil futures prices, Fed Funds rate, consumer spending, etc) are found by the model to be the most predictive in nature. Findings will be posted in the comments.
This chart was created to accompany a blog post which explores leveraging machine learning (RNN: LSTM) using Tensorflow Keras and SHAP to determine which factors (indicators and correlations with Macro, such as oil futures prices, Fed Funds rate, consumer spending, etc) are found by the model to be the most predictive in nature. Findings will be posted in the comments.