Bitcoin is the king of all cryptocurrencies. The plummeting and soaring price of Bitcoin is of immense concern for crypto analysts, experts, and traders all over the world. Researchers and enthusiasts devise different methods to predict the prices of Bitcoin. Most of the methods require thorough technical expertise in machine learning, deep learning, Python, mathematics, and other such subjects of advanced learning. Read on to find out more about the most accurate methods of Bitcoin price prediction.
Prediction of Bitcoin Price With Python
The right source code in Python programming language can help in the prediction of the price movement and direction of the Bitcoin market. The model developed by Python can not only forecast the direction of the market but can also make an accurate prediction of several Bitcoin prices at particular time-stamps. The Python model is driven by a Bitcoin price prediction algorithm which transmits a buying or selling signal at a specific date and time, to extract profits. The Python model of Bitcoin price prediction now has a huge supporter base of Bitcoin investors, data scientists, machine learning experts, and beginners who seek to be a part of the crypto world.
The first step in building the Python model of Bitcoin price prediction was to assemble the tools. The tools used were Django (a Python framework that facilitates rapid development and pragmatic design of the model), PyTrends (provides a user-friendly interface for the automation of the download of reports from the website of Google Trends), Tableau Public (for the visualization of data in the form of charts), Jupyter Notebook (an application for creating and sharing documents of live code, narrative text, visualization, and equations.)
The developers obtained a detailed history of the Bitcoin transaction and then transferred the data to the SQL database. The calculations were processed in Python for the transmission of the Buy/Sell signal based on the price movement of BTC:USD and the keyword ratio from Google Trends data. The front-end developers used Django to create the user-interface of the Python model of Bitcoin price prediction.
Prediction of the Rise and Fall of Bitcoin Price With Mathematics
The Bitcoin market and the price movement of cryptocurrencies cannot be analyzed by traditional methods. For the technical analysis of the crypto market and for accurate Bitcoin price prediction, an individual needs to have excellent expertise in mathematics and statistics. The mathematical model of Bitcoin price prediction involves the use of several statistical indicators whose values are noted and analyzed for forecasting the price of Bitcoin, and for assessing where and when to buy and sell the crypto assets like Bitcoin for maximum profits. The mathematical model of Bitcoin price prediction involves the following indicators.
l Relative Strength Index: RSI indicates the momentum for the identification of the various market trends. The mathematical model uses RSI to indicate if the market is oversold or overbought and consequently gives the signal for buying or selling Bitcoin. The simple mathematical formula is RSI equals average profits over average losses.
l Fibonacci Retracement: FR is derived from the famous Fibonacci series that begins with 0 and 1, and then continues like every successive number in the series is an addition of the preceding two numbers. FR tries to quantify the expected pullback after a huge and rapid rise or a dip in the Bitcoin price.
l On Balance Volume: OBV is a volume-based indicator of the mathematical model of Bitcoin price prediction. OBV uses cumulative trading volume for measuring the strength of market trends in the downward or upward direction.
l Stochastic Oscillator: SO evaluates the momentum, and then predicts the market movement and Bitcoin price based on the momentum. There is always a shift in momentum before a price shift.
l Moving Averages: MA can predict long-, medium-, and short-term market trends. This indicator considers the average Bitcoin price over a specific period. It can accurately predict Bitcoin price volatility and price shifts.
Prediction of Bitcoin Price With Machine Learning
Techniques of machine learning enable the prediction of Bitcoin price at different frequencies by the classification of the price of Bitcoin into the high-frequency price and daily price. The machine learning model of Bitcoin price prediction considers a set of high-frequency features that include network and property, gold spot price, market, and trading. It acquires the basic daily frequency features from regular crypto exchanges for the price prediction of Bitcoin in every five minutes, as per Bitcoin News. The model employs the methods of Linear Discriminant Analysis and Logistic Regression for almost accurate prediction of Bitcoin price movement and fluctuation by machine learning. Some of the best machine learning models for Bitcoin price prediction are Support Vector Machine, XGBoost, Random Forest, and Quadratic Discriminant Analysis.
Other Methods of Prediction of the Price of Bitcoin
The deep learning model of Bitcoin price prediction
The model of deep learning uses the LSTM (Long Short Term Memory) neural network for the real-time prediction of the Bitcoin price. The model obtains cryptocurrency data in real-time, processes the data for the neural network, tests out the prediction, and then visualizes the results.
Artificial Intelligence model of Bitcoin price prediction:
The AI model obtains the data of daily price change and formulates a price fluctuation pattern. It tests out the different patterns to reach an optimal pattern. The model makes Bitcoin price predictions based on the optimal price pattern.
The heart and soul of Bitcoin is its price, which experiences erratic volatility in the market. The prediction of the price of Bitcoin can not only make millionaires out of common men but can redefine the global economy as well. Bitcoin prices can be predicted by the application of a variety of methods like machine learning, mathematics, Python programming, and more. Every method is complex, but the result is the generation of accurate buying and selling signals for Bitcoin. Delve deeper into the different methods and become a part of the community of researchers and scientists to develop newer models of Bitcoin price prediction.