Combining technical indicators and feature selection methods to predict Bitcoin price
| Autor: | Jian-Kai Huang, 黃建凱 |
|---|---|
| Rok vydání: | 2019 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 107 With the rapid development of blockchain, cryptocurrency has attracted more and more attention. Bitcoin has become the most popular virtual currency in recent years. Many investors regard bitcoin as a stock-like investment tool. However, the price of Bitcoin fluctuates drastically, the investors can not accurately predict the price of Bitcoin. In view of the fact that technical indicators can effectively predict stock prices and feature extraction methods are helpful in forecasting stock price and bankruptcy, we propose using 24 popular stock market technical indicators, e.g., Moving Average Convergence Divergence(MACD), Bollinger Bands, Stochastic Oscillator(KD), Williams %R(W%R), to predict the price of bitcoin. Next, six feature selection methods e.g., Information gain, Principal components analysis, are used to select more useful indicators. Finally, eight classification algorithms and five prediction algorithms are employed to predict the trend of bitcoin price and the bitcoin price. The experiments uses the sliding window scheme to maintain the temporal correlation of training data and test data. The experiment results show that the technical indicator can effectively predict Bitcoin price and also indicate the technical indicators and algorithms which are effective in prediction. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
| Externí odkaz: |
|