Prediction of stock prices has been a crucial and challenging task, especially in the case of highly volatile digital currencies such as Bitcoin. This research examineS the potential of using neural network models, namely LSTMs and GRUs, to forecast Bitcoin's price movements. We employ five-fold cross-validation to enhance generalization and utilize L2 regularization to reduce overfitting and noise. Our study demonstrates that the GRUs models offer better accuracy than LSTMs model for predicting Bitcoin's price. Specifically, the GRU model has an MSE of 4.67, while the LSTM model has an MSE of 6.25 when compared to the actual prices in the test set data. This finding indicates that GRU models are better equipped to process sequential data with long-term dependencies, a characteristic of financial time series data such as Bitcoin prices. In summary, our results provide valuable insights into the potential of neural network models for accurate Bitcoin price prediction and emphasize the importance of employing appropriate regularization techniques to enhance model performance.

It is a comprehensive course that shows how you can build a stylish web app with machine learning at the backend to predict the future price of any cryptocurrency. The main course has a mini crash course on Python for newbies and culminates into the theory and practice of Machine Learning and its predictive modeling application on cryptocurrencies. At the end of this course, you will be able to develop a full-fledged web app that will take in data (available for free on the Internet). As you will provide the data to the web app, the web app having its predictive machine learning model at the backend will spit out the future prices of a cryptocurrency. The course includes all the code for the web app, and with a tiny tuning in the code, you can adjust the web app to predict the prices of any cryptocurrency.

Artificial Intelligence is the behaviour or rules followed by or created by machines to imitate human or animal intelligence. There are several scenarios where one might use artificial intelligence for problem solving or various tasks. Artificial intelligence can be achieved through machine learning. Machine learning is the process of achieving artificial intelligence in a computer system either by supervision or learning itself. The 2 types of artificial intelligence are a rule-based model that simply follows instructions given to it and a machine learning model that trains on useful data first before predicting future results or solving problems.

Predicting stock markets has been an endeavor a lot of people have chased. I spent about 6 months building an end-to-end ML system for algorithmic trading. I've been running the production system to place paper orders for the last 5 months and generated returns of 23% as compared to S&P-500's 10.7%. Returns and risk metrics for paper trading and backtesting shown below. There's a lot to talk about!

When it comes to using machine learning in the stock market, there are multiple approaches a trader can do to utilize ML models. From determining future risk to predicting stock prices, machine learning can be used for virtually any kind of financial modeling. In our previous articles, we delved into the usage of two Time Series models: SARIMAX and Facebook Prophet. We utilized both of these models to forecast the potential, future prices of Bitcoin. In another article, we used classification models to classify stocks based on their performance in quarterly reports.

Someone recently asked on the statistics Stack Exchange why the squared error is used in statistics. This is something I'd been wondering about myself recently, so I decided to take a crack at answering it. The post below is adapted from that answer. It's true that one could choose to use, say, the absolute error instead of the squared error. In fact, the absolute error is often closer to what you "care about" when making predictions from your model.

The Ministry of Science, ICT & Future Planning announced on June 19 that Ulsan National Institute of Science & Technology professor Choi Jae-shik recently developed an artificial intelligence system and is going to unveil it at an academic seminar this month. According to the professor, the system is capable of predicting the future prices of houses, future stock prices, foreign exchange rate movements and the like after reading newspaper articles, business reports and so on and then automatically drawing up reports in English. "The system will become capable of drawing up the same reports in Korean at some point in time next year and writing news articles in the near future," the professor remarked. Earlier, an AI system capable of stock price prediction has been developed by the MIT and the University of Cambridge. This system, however, is limited in accuracy because it predicts future prices by analyzing correlations between the prices of stocks owned by someone and the others based on numerical data such as past prices.

I am learning machine learning to use it for stock market price forecasting. While doing that I got this question. If we take any country with stock exchange they have more than one investment assests for trading and investing such as commodity, stock, futures,option,forex etc. Lets say a quant wants to make a machine learning model for stock price prediction in US market. There are thousands of companies (about 2800) stocks are listed in NYSE. How a quant will make a ML model for predicting stock price?