A Deep Learning-Based Cryptocurrency Price Prediction Model That Uses On-Chain Data

Abstract

Cryptocurrency has recently attracted substantial interest from investors due to its underlying philosophy of decentralization and transparency. Considering cryptocurrency's volatility and unique characteristics, accurate price prediction is essential for developing successful investment strategies. To this end, the authors of this work propose a novel framework that predicts the price of Bitcoin (BTC), a dominant cryptocurrency. For stable prediction performance in unseen price range, the change point detection technique is employed. In particular, it is used to segment time-series data so that normalization can be separately conducted based on segmentation. In addition, on-chain data, the unique records listed on the blockchain that are inherent in cryptocurrencies, are collected and utilized as input variables to predict prices. Furthermore, this work proposes self-attention-based multiple long short-term memory (SAM-LSTM), which consists of multiple LSTM modules for on-chain variable groups and the attention mechanism, for the prediction model. Experiments with real-world BTC price data and various method setups have proven the proposed framework's effectiveness in BTC price prediction. The results are promising, with the highest MAE, RMSE, MSE, and MAPE values of 0.3462, 0.5035, 0.2536, and 1.3251, respectively.

A survey of deep learning applications in cryptocurrency

“… A SAM-LSTM-based prediction model is proposed Kim et al. 144 LSTM BTC, ETH, ADA, DASH, LTC, and XMR The proposed model’s accurate response percentage has increased by roughly 13%–21%, according to experimental findings, which has significantly enhanced performance. A deep learning model containing sellProfit, buyProfit, and maxProfit input features is presented with a criterion for which action is most beneficial at any given time.…”

Forecasting Bitcoin Volatility Spikes from Whale Transactions and Cryptoquant Data Using Synthesizer Transformer Models

“…On-chain data includes information from the blockchain ledger, such as the details of each transaction (e.g. from which wallet, to which wallet, amount, fees paid to miners), and the difficulty of mining blocks as well as the block sizes (Jagannath et al, 2021;Kim et al, 2022). The availability of such data can gives us incredible insight in upcoming price movements (Zheng et al, 2021).…”

“…Looking at existing literature, we see that utilizing this transparency allows one to establish a trader's edge. For instance, Kim et al (2022) show that onchain data can be useful when predicting Bitcoin's price with a self-attentionbased multiple long short-term memory model (SA-LSTM). While they provide a list of 42 variables used, there is no ablation study or XAI method used to identify which variables are most important.…”

DLCP2F: a DL-based cryptocurrency price prediction framework

“…According to reported results, the proposed algorithm predicted the prices with great accuracy, and it may be used in various applications based on the bitcoin price prediction. Finally, Kim et al [41] proposed a multiple LSTM based on self-attention. Their proposed model contains numerous LSTM modules for on-chain variable groups as well as the attention mechanism of the prediction model.…”