Blockchain Metrics and Indicators in Cryptocurrency Trading

• URL: http://arxiv.org/abs/2403.00770v1
• Date: Sun, 11 Feb 2024 12:34:58 GMT
• Title: Blockchain Metrics and Indicators in Cryptocurrency Trading
• Authors: Juan C. King, Roberto Dale, José M. Amigó,
• Abstract summary: The objective of this paper is the construction of new indicators that can be useful to operate in the cryptocurrency market. These indicators are based on public data obtained from the blockchain network, specifically from the nodes that make up Bitcoin mining.
• Score: 0.22940141855172028
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The objective of this paper is the construction of new indicators that can be useful to operate in the cryptocurrency market. These indicators are based on public data obtained from the blockchain network, specifically from the nodes that make up Bitcoin mining. Therefore, our analysis is unique to that network. The results obtained with numerical simulations of algorithmic trading and prediction via statistical models and Machine Learning demonstrate the importance of variables such as the hash rate, the difficulty of mining or the cost per transaction when it comes to trade Bitcoin assets or predict the direction of price. Variables obtained from the blockchain network will be called here blockchain metrics. The corresponding indicators (inspired by the "Hash Ribbon") perform well in locating buy signals. From our results, we conclude that such blockchain indicators allow obtaining information with a statistical advantage in the highly volatile cryptocurrency market.

Related papers

• Using Sentiment and Technical Analysis to Predict Bitcoin with Machine Learning [1.3053649021965603]
  This work represents a preliminary study on the importance of sentiment metrics in cryptocurrency forecasting. We present a novel approach for predicting Bitcoin price by combining the Fear & Greedy Index, a measure of market sentiment, Technical Analysis indicators, and the potential of Machine Learning algorithms.
  arXiv  Detail & Related papers  (2024-10-18T15:13:07Z)
• Predicting Bitcoin Market Trends with Enhanced Technical Indicator Integration and Classification Models [6.39158540499473]
  This study presents a machine learning model based on classification to forecast the direction of the cryptocurrency market. It is trained using historical data and important technical indicators such as the Moving Average Convergence Divergence, the Relative Strength Index, and Bollinger Bands. The results show a buy/sell signal accuracy of over 92%.
  arXiv  Detail & Related papers  (2024-10-09T14:29:50Z)
• The Latency Price of Threshold Cryptosystem in Blockchains [52.359230560289745]
  We study the interplay between threshold cryptography and a class of blockchains that use Byzantine-fault tolerant (BFT) consensus protocols. Existing approaches for threshold cryptosystems introduce a latency overhead of at least one message delay for running the threshold cryptographic protocol. We propose a mechanism to eliminate this overhead for blockchain-native threshold cryptosystems with tight thresholds.
  arXiv  Detail & Related papers  (2024-07-16T20:53:04Z)
• Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learning [51.13534069758711]
  Decentralized approaches like blockchain offer a compelling solution by implementing a consensus mechanism among multiple entities. Federated Learning (FL) enables participants to collaboratively train models while safeguarding data privacy. This paper investigates the synergy between blockchain's security features and FL's privacy-preserving model training capabilities.
  arXiv  Detail & Related papers  (2024-03-28T07:08:26Z)
• A Data-driven Deep Learning Approach for Bitcoin Price Forecasting [10.120972108960425]
  We propose a shallow Bidirectional-LSTM (Bi-LSTM) model to forecast bitcoin closing prices in a daily time frame. We compare the performance with that of other forecasting methods, and show that with the help of the proposed feature engineering method, a shallow deep neural network outperforms other popular price forecasting models.
  arXiv  Detail & Related papers  (2023-10-27T10:35:47Z)
• Cryptocurrency Portfolio Optimization by Neural Networks [81.20955733184398]
  This paper proposes an effective algorithm based on neural networks to take advantage of these investment products. A deep neural network, which outputs the allocation weight of each asset at a time interval, is trained to maximize the Sharpe ratio. A novel loss term is proposed to regulate the network's bias towards a specific asset, thus enforcing the network to learn an allocation strategy that is close to a minimum variance strategy.
  arXiv  Detail & Related papers  (2023-10-02T12:33:28Z)
• Graph Regularized Nonnegative Latent Factor Analysis Model for Temporal Link Prediction in Cryptocurrency Transaction Networks [1.6801544027052142]
  Link prediction learning structure of network is helpful to understand the mechanism of network. We propose a single latent factor-dependent, non-negative, multiplicative and graph regularized-incorporated update (SLF-NMGRU) algorithm. Experiments on a real cryptocurrency transaction network show that the proposed method improves both the accuracy and the computational efficiency.
  arXiv  Detail & Related papers  (2022-08-03T08:58:59Z)
• Token Spammers, Rug Pulls, and SniperBots: An Analysis of the Ecosystem of Tokens in Ethereum and in the Binance Smart Chain (BNB) [50.888293380932616]
  We study the ecosystem of the tokens and liquidity pools. We find that about 60% of tokens are active for less than one day. We estimate that 1-day rug pulls generated $240 million in profits.
  arXiv  Detail & Related papers  (2022-06-16T14:20:19Z)
• Quantum-resistance in blockchain networks [46.63333997460008]
  This paper describes the work carried out by the Inter-American Development Bank, the IDB Lab, LACChain, Quantum Computing (CQC), and Tecnologico de Monterrey to identify and eliminate quantum threats in blockchain networks. The advent of quantum computing threatens internet protocols and blockchain networks because they utilize non-quantum resistant cryptographic algorithms.
  arXiv  Detail & Related papers  (2021-06-11T23:39:25Z)
• The Doge of Wall Street: Analysis and Detection of Pump and Dump Cryptocurrency Manipulations [50.521292491613224]
  This paper performs an in-depth analysis of two market manipulations organized by communities over the Internet: The pump and dump and the crowd pump. The pump and dump scheme is a fraud as old as the stock market. Now, it got new vitality in the loosely regulated market of cryptocurrencies. We report on three case studies related to pump and dump groups.
  arXiv  Detail & Related papers  (2021-05-03T10:20:47Z)
• A Blockchain Transaction Graph based Machine Learning Method for Bitcoin Price Prediction [8.575998118995216]
  Existing bitcoin prediction works mostly on trivial feature engineering. We propose k-order transaction graph to reveal patterns under different scope. A novel prediction method is proposed to accept the features and make price prediction, which can take advantage from particular patterns from different history period.
  arXiv  Detail & Related papers  (2020-08-21T20:08:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.