Blockchain Metrics and Indicators in Cryptocurrency Trading

ArXiv ID: 2403.00770 “View on arXiv”

Authors: Unknown

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.

Keywords: Blockchain Metrics, Hash Rate, Machine Learning, Algorithmic Trading, Bitcoin, Cryptocurrency

Complexity vs Empirical Score

  • Math Complexity: 4.0/10
  • Empirical Rigor: 6.5/10
  • Quadrant: Street Traders
  • Why: The paper employs relatively standard statistical and machine learning models (Random Forest, LSTM) without deep mathematical derivations, but heavily emphasizes empirical validation through numerical simulations, algorithmic trading backtests, and specific data handling (Quandl platform) with available Python code.
  flowchart TD
    A["Research Goal:<br>Construct new crypto trading indicators<br>using Blockchain Metrics"] --> B["Data Collection"]
    
    B --> C{"Bitcoin Blockchain Data"}
    C --> C1["Hash Rate"]
    C --> C2["Mining Difficulty"]
    C --> C3["Transaction Cost"]
    
    B --> D["Methodology"]
    D --> D1["Algorithmic Trading Simulations"]
    D --> D2["Statistical Models"]
    D --> D3["Machine Learning"]
    
    D1 & D2 & D3 --> E["Computational Process:<br>Develop 'Hash Ribbon' inspired<br>indicators & test efficacy"]
    
    E --> F["Key Findings/Outcomes"]
    F --> F1["Indicators provide<br>statistical advantage"]
    F --> F2["Identify buy signals<br>effectively"]
    F --> F3["Hash Rate, Difficulty, &<br>Cost are significant variables"]