人工知能学会第二種研究会資料
Online ISSN : 2436-5556
機械学習アルゴリズムによるビットコイン価格変動の予測
新立 拓也ピフル ルカーシュ海蔵寺 大成
著者情報
研究報告書・技術報告書 フリー

2017 年 2017 巻 FIN-019 号 p. 45-

詳細
抄録

We study the limits of prediction accuracy of Bitcoin price data in CNY currency using tick data from the OKCoin Bitcoin exchange (source: Kaiko data). The tick data contain the price, volume, and trade direction, and are transformed to the OHLCV format using standard methods. In this report, we deploy the Support Vector Machine algorithm by Vapnik to estimate the sign of the hour-to-hour transaction return using a sampling moving window of varying size on the past data. Several kernel functions are validated. Our first results for all months of the year 2015 show that the hit ratio accuracy level (the fraction of correctly predicted upward or downward events) does not exceed 60%. It remains to be established whether this low result corresponds to the causal extraction limit inherent in the data, or whether it can be improved by deploying other methods, such as LSTM networks in deep learning.

著者関連情報
© 2017 著作者
前の記事 次の記事
feedback
Top